librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-14 06:27:53	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 519 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-14 06:27:45	card stringlengths 11 1.01M
Pongsasit/mod-th-cross-encoder	Pongsasit	2025-04-02T16:12:14Z	6	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "sentence-transformers", "text-ranking", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-ranking	2024-04-26T10:51:32Z	--- library_name: transformers tags: - sentence-transformers pipeline_tag: text-ranking --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: Pongsasit Thongpramoon - Model type: Cross Encoder - Language(s) (NLP): Thai - ## How to Get Started with the Model Use the code below to get started with the model. ```python from sentence_transformers.cross_encoder import CrossEncoder model = CrossEncoder("Pongsasit/mod-th-cross-encoder") scores = model.predict([["อาหารตามสั่ง", "หมู เห็ด เป็ด ไก่"], ["อาหารตามสั่ง", "รถ เรือ เครื่องบิน จักรยาน"]]) ```
johnsett/audio-diffusion-electronic	johnsett	2025-04-02T16:12:07Z	0	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-audio-generation", "diffusion-models-class", "license:mit", "diffusers:AudioDiffusionPipeline", "region:us" ]	null	2025-04-02T16:11:40Z	--- license: mit tags: - pytorch - diffusers - unconditional-audio-generation - diffusion-models-class --- # Model Card for Unit 4 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional audio generation of music in the genre Electronic ## Usage <pre> from IPython.display import Audio from diffusers import DiffusionPipeline pipe = DiffusionPipeline.from_pretrained("johnsett/audio-diffusion-electronic") output = pipe() display(output.images[0]) display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate())) </pre>
samoline/test_dpo_83894893789453	samoline	2025-04-02T16:11:04Z	2	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:Maykeye/TinyLLama-v0", "base_model:finetune:Maykeye/TinyLLama-v0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-01T19:49:22Z	--- base_model: Maykeye/TinyLLama-v0 library_name: transformers model_name: test_dpo_83894893789453 tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for test_dpo_83894893789453 This model is a fine-tuned version of [Maykeye/TinyLLama-v0](https://huggingface.co/Maykeye/TinyLLama-v0). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="samoline/test_dpo_83894893789453", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/samoline-nan/Gradients-On-Demand/runs/xu4nbsb0) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Skyfallirk/claude_monet_LoRa	Skyfallirk	2025-04-02T16:10:36Z	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-04-02T16:09:55Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: photo collage in Claude Monet style widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - Skyfallirk/claude_monet_LoRa <Gallery /> ## Model description These are Skyfallirk/claude_monet_LoRa LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use photo collage in Claude Monet style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](Skyfallirk/claude_monet_LoRa/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
Haricot24601/ppo-Lunarlander-v2-3	Haricot24601	2025-04-02T16:09:59Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2025-04-02T16:09:20Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 15.20 +/- 75.87 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo_reference' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 500000 'learning_rate': 0.0001 'num_envs': 8 'num_steps': 256 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 8 'update_epochs': 10 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'Haricot24601/ppo-Lunarlander-v2-3' 'batch_size': 2048 'minibatch_size': 256} ```
mlx-community/tasksource-ModernBERT-base-embed-bf16	mlx-community	2025-04-02T16:09:11Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "modernbert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:6661966", "loss:MultipleNegativesRankingLoss", "loss:CachedMultipleNegativesRankingLoss", "loss:SoftmaxLoss", "loss:AnglELoss", "loss:CoSENTLoss", "loss:CosineSimilarityLoss", "mlx", "en", "dataset:tomaarsen/natural-questions-hard-negatives", "dataset:tomaarsen/gooaq-hard-negatives", "dataset:bclavie/msmarco-500k-triplets", "dataset:sentence-transformers/all-nli", "dataset:sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1", "dataset:sentence-transformers/gooaq", "dataset:sentence-transformers/natural-questions", "dataset:tasksource/merged-2l-nli", "dataset:tasksource/merged-3l-nli", "dataset:tasksource/zero-shot-label-nli", "dataset:MoritzLaurer/dataset_train_nli", "dataset:google-research-datasets/paws", "dataset:nyu-mll/glue", "dataset:mwong/fever-evidence-related", "dataset:tasksource/sts-companion", "base_model:answerdotai/ModernBERT-base", "base_model:finetune:answerdotai/ModernBERT-base", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-04-02T15:42:12Z	--- language: - en tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:6661966 - loss:MultipleNegativesRankingLoss - loss:CachedMultipleNegativesRankingLoss - loss:SoftmaxLoss - loss:AnglELoss - loss:CoSENTLoss - loss:CosineSimilarityLoss - mlx base_model: answerdotai/ModernBERT-base widget: - source_sentence: Daniel went to the kitchen. Sandra went back to the kitchen. Daniel moved to the garden. Sandra grabbed the apple. Sandra went back to the office. Sandra dropped the apple. Sandra went to the garden. Sandra went back to the bedroom. Sandra went back to the office. Mary went back to the office. Daniel moved to the bathroom. Sandra grabbed the apple. Sandra travelled to the garden. Sandra put down the apple there. Mary went back to the bathroom. Daniel travelled to the garden. Mary took the milk. Sandra grabbed the apple. Mary left the milk there. Sandra journeyed to the bedroom. John travelled to the office. John went back to the garden. Sandra journeyed to the garden. Mary grabbed the milk. Mary left the milk. Mary grabbed the milk. Mary went to the hallway. John moved to the hallway. Mary picked up the football. Sandra journeyed to the kitchen. Sandra left the apple. Mary discarded the milk. John journeyed to the garden. Mary dropped the football. Daniel moved to the bathroom. Daniel journeyed to the kitchen. Mary travelled to the bathroom. Daniel went to the bedroom. Mary went to the hallway. Sandra got the apple. Sandra went back to the hallway. Mary moved to the kitchen. Sandra dropped the apple there. Sandra grabbed the milk. Sandra journeyed to the bathroom. John went back to the kitchen. Sandra went to the kitchen. Sandra travelled to the bathroom. Daniel went to the garden. Daniel moved to the kitchen. Sandra dropped the milk. Sandra got the milk. Sandra put down the milk. John journeyed to the garden. Sandra went back to the hallway. Sandra picked up the apple. Sandra got the football. Sandra moved to the garden. Daniel moved to the bathroom. Daniel travelled to the garden. Sandra went back to the bathroom. Sandra discarded the football. sentences: - In the adulthood stage, it can jump, walk, run - The chocolate is bigger than the container. - The football before the bathroom was in the garden. - source_sentence: Almost everywhere the series converges then . sentences: - The series then converges almost everywhere . - Scrivener dated the manuscript to the 12th century , C. R. Gregory to the 13th century . Currently the manuscript is dated by the INTF to the 12th century . - Both daughters died before he did , Tosca in 1976 and Janear in 1981 . - source_sentence: how are you i'm doing good thank you you im not good having cough and colg sentences: - 'This example tweet expresses the emotion: happiness' - This example utterance is about cooking recipies. - This example text from a US presidential speech is about macroeconomics - source_sentence: A man is doing pull-ups sentences: - The man is doing exercises in a gym - A black and white dog with a large branch is running in the field - There is no man drawing - source_sentence: A chef is preparing some food sentences: - The man is lifting weights - A chef is preparing a meal - A dog is in a sandy area with the sand that is being stirred up into the air and several plants are in the background datasets: - tomaarsen/natural-questions-hard-negatives - tomaarsen/gooaq-hard-negatives - bclavie/msmarco-500k-triplets - sentence-transformers/all-nli - sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1 - sentence-transformers/gooaq - sentence-transformers/natural-questions - tasksource/merged-2l-nli - tasksource/merged-3l-nli - tasksource/zero-shot-label-nli - MoritzLaurer/dataset_train_nli - google-research-datasets/paws - nyu-mll/glue - mwong/fever-evidence-related - tasksource/sts-companion pipeline_tag: sentence-similarity library_name: sentence-transformers --- # mlx-community/tasksource-ModernBERT-base-embed-bf16 The Model [mlx-community/tasksource-ModernBERT-base-embed-bf16](https://huggingface.co/mlx-community/tasksource-ModernBERT-base-embed-bf16) was converted to MLX format from [tasksource/ModernBERT-base-embed](https://huggingface.co/tasksource/ModernBERT-base-embed) using mlx-lm version 0.0.3. ## Use with mlx ```bash pip install mlx-embeddings ``` ```python from mlx_embeddings import load, generate import mlx.core as mx model, tokenizer = load("mlx-community/tasksource-ModernBERT-base-embed-bf16") # For text embeddings output = generate(model, processor, texts=["I like grapes", "I like fruits"]) embeddings = output.text_embeds # Normalized embeddings # Compute dot product between normalized embeddings similarity_matrix = mx.matmul(embeddings, embeddings.T) print("Similarity matrix between texts:") print(similarity_matrix) ```
deeplogixio/deeplogix	deeplogixio	2025-04-02T16:08:48Z	0	0	null	[ "en", "license:mit", "region:us" ]	null	2025-04-02T15:05:13Z	--- license: mit language: - en --- # Deeplogix AI RPC Deeplogix AI RPC is a Python module for seamlessly running remote private AI models from your Python code as if these models were installed locally. ## Installation Use the package manager [pip](https://pip.pypa.io/en/stable/) for install. Development version from TestPyPi: ```bash pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple deeplogix ``` Production version from PyPi: ```bash pip install deeplogix ``` ## Usage For module built-in demos: ```bash python -m deeplogix --demo ``` Or for your A.I. script: ```bash python -m deeplogix ./path/to/your/script.py ``` Or just: ```bash python -m deeplogix ``` And it will print usage options. When you run Deeplogix module first time - it will ask you hostId and token, which could be obtained at [Deeplogix](https://www.deeplogix.io/) site after sign up. ## License [MIT](https://choosealicense.com/licenses/mit/)
wilzeelbub/testo	wilzeelbub	2025-04-02T16:07:06Z	0	1	null	[ "region:us" ]	null	2025-04-02T16:04:37Z	from datasets import load_dataset dataset = load_dataset("imdb") # Example with IMDB dataset from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments # Load pre-trained model and tokenizer model_name = "bert-base-uncased" tokenizer = BertTokenizer.from_pretrained(model_name) model = BertForSequenceClassification.from_pretrained(model_name) # Tokenize the dataset def tokenize_function(examples): return tokenizer(examples['text'], padding="max_length", truncation=True) tokenized_datasets = dataset.map(tokenize_function, batched=True) # Define training arguments training_args = TrainingArguments( output_dir="./results", evaluation_strategy="epoch", learning_rate=2e-5, per_device_train_batch_size=16, num_train_epochs=3, ) # Create Trainer instance trainer = Trainer( model=model, args=training_args, train_dataset=tokenized_datasets["train"], eval_dataset=tokenized_datasets["test"], ) # Train the model trainer.train() model.save_pretrained("./my_model") tokenizer.save_pretrained("./my_model") # Upload to Hugging Face from huggingface_hub import HfApi, HfFolder # Log in to your Hugging Face account HfFolder.save_token("YOUR_HUGGINGFACE_TOKEN") api = HfApi() api.upload_folder( folder_path="./my_model", path_in_repo="my_model", repo_id="your_username/my_model", repo_type="model", ) from transformers import pipeline classifier = pipeline("text-classification", model="your_username/my_model") result = classifier("This is an example sentence.") print(result)
mlx-community/tasksource-ModernBERT-base-embed-4bit	mlx-community	2025-04-02T16:06:41Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "modernbert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:6661966", "loss:MultipleNegativesRankingLoss", "loss:CachedMultipleNegativesRankingLoss", "loss:SoftmaxLoss", "loss:AnglELoss", "loss:CoSENTLoss", "loss:CosineSimilarityLoss", "mlx", "en", "dataset:tomaarsen/natural-questions-hard-negatives", "dataset:tomaarsen/gooaq-hard-negatives", "dataset:bclavie/msmarco-500k-triplets", "dataset:sentence-transformers/all-nli", "dataset:sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1", "dataset:sentence-transformers/gooaq", "dataset:sentence-transformers/natural-questions", "dataset:tasksource/merged-2l-nli", "dataset:tasksource/merged-3l-nli", "dataset:tasksource/zero-shot-label-nli", "dataset:MoritzLaurer/dataset_train_nli", "dataset:google-research-datasets/paws", "dataset:nyu-mll/glue", "dataset:mwong/fever-evidence-related", "dataset:tasksource/sts-companion", "base_model:answerdotai/ModernBERT-base", "base_model:finetune:answerdotai/ModernBERT-base", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-04-02T15:41:22Z	--- language: - en tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:6661966 - loss:MultipleNegativesRankingLoss - loss:CachedMultipleNegativesRankingLoss - loss:SoftmaxLoss - loss:AnglELoss - loss:CoSENTLoss - loss:CosineSimilarityLoss - mlx base_model: answerdotai/ModernBERT-base widget: - source_sentence: Daniel went to the kitchen. Sandra went back to the kitchen. Daniel moved to the garden. Sandra grabbed the apple. Sandra went back to the office. Sandra dropped the apple. Sandra went to the garden. Sandra went back to the bedroom. Sandra went back to the office. Mary went back to the office. Daniel moved to the bathroom. Sandra grabbed the apple. Sandra travelled to the garden. Sandra put down the apple there. Mary went back to the bathroom. Daniel travelled to the garden. Mary took the milk. Sandra grabbed the apple. Mary left the milk there. Sandra journeyed to the bedroom. John travelled to the office. John went back to the garden. Sandra journeyed to the garden. Mary grabbed the milk. Mary left the milk. Mary grabbed the milk. Mary went to the hallway. John moved to the hallway. Mary picked up the football. Sandra journeyed to the kitchen. Sandra left the apple. Mary discarded the milk. John journeyed to the garden. Mary dropped the football. Daniel moved to the bathroom. Daniel journeyed to the kitchen. Mary travelled to the bathroom. Daniel went to the bedroom. Mary went to the hallway. Sandra got the apple. Sandra went back to the hallway. Mary moved to the kitchen. Sandra dropped the apple there. Sandra grabbed the milk. Sandra journeyed to the bathroom. John went back to the kitchen. Sandra went to the kitchen. Sandra travelled to the bathroom. Daniel went to the garden. Daniel moved to the kitchen. Sandra dropped the milk. Sandra got the milk. Sandra put down the milk. John journeyed to the garden. Sandra went back to the hallway. Sandra picked up the apple. Sandra got the football. Sandra moved to the garden. Daniel moved to the bathroom. Daniel travelled to the garden. Sandra went back to the bathroom. Sandra discarded the football. sentences: - In the adulthood stage, it can jump, walk, run - The chocolate is bigger than the container. - The football before the bathroom was in the garden. - source_sentence: Almost everywhere the series converges then . sentences: - The series then converges almost everywhere . - Scrivener dated the manuscript to the 12th century , C. R. Gregory to the 13th century . Currently the manuscript is dated by the INTF to the 12th century . - Both daughters died before he did , Tosca in 1976 and Janear in 1981 . - source_sentence: how are you i'm doing good thank you you im not good having cough and colg sentences: - 'This example tweet expresses the emotion: happiness' - This example utterance is about cooking recipies. - This example text from a US presidential speech is about macroeconomics - source_sentence: A man is doing pull-ups sentences: - The man is doing exercises in a gym - A black and white dog with a large branch is running in the field - There is no man drawing - source_sentence: A chef is preparing some food sentences: - The man is lifting weights - A chef is preparing a meal - A dog is in a sandy area with the sand that is being stirred up into the air and several plants are in the background datasets: - tomaarsen/natural-questions-hard-negatives - tomaarsen/gooaq-hard-negatives - bclavie/msmarco-500k-triplets - sentence-transformers/all-nli - sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1 - sentence-transformers/gooaq - sentence-transformers/natural-questions - tasksource/merged-2l-nli - tasksource/merged-3l-nli - tasksource/zero-shot-label-nli - MoritzLaurer/dataset_train_nli - google-research-datasets/paws - nyu-mll/glue - mwong/fever-evidence-related - tasksource/sts-companion pipeline_tag: sentence-similarity library_name: sentence-transformers --- # mlx-community/ModernBERT-base-embed-4bit The Model [mlx-community/tasksource-ModernBERT-base-embed-4bit](https://huggingface.co/mlx-community/tasksource-ModernBERT-base-embed-4bit) was converted to MLX format from [tasksource/ModernBERT-base-embed](https://huggingface.co/tasksource/ModernBERT-base-embed) using mlx-lm version 0.0.3. ## Use with mlx ```bash pip install mlx-embeddings ``` ```python from mlx_embeddings import load, generate import mlx.core as mx model, tokenizer = load("mlx-community/tasksource-ModernBERT-base-embed-4bit") # For text embeddings output = generate(model, processor, texts=["I like grapes", "I like fruits"]) embeddings = output.text_embeds # Normalized embeddings # Compute dot product between normalized embeddings similarity_matrix = mx.matmul(embeddings, embeddings.T) print("Similarity matrix between texts:") print(similarity_matrix) ```
meister1378/gemma2-9b-3753_step	meister1378	2025-04-02T16:05:27Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma2", "trl", "en", "base_model:HumanF-MarkrAI/Gukbap-Gemma2-9B", "base_model:finetune:HumanF-MarkrAI/Gukbap-Gemma2-9B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-02T16:04:07Z	--- base_model: HumanF-MarkrAI/Gukbap-Gemma2-9B tags: - text-generation-inference - transformers - unsloth - gemma2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: meister1378 - License: apache-2.0 - Finetuned from model : HumanF-MarkrAI/Gukbap-Gemma2-9B This gemma2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mlx-community/nomicai-modernbert-embed-base-bf16	mlx-community	2025-04-02T16:05:15Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "modernbert", "feature-extraction", "sentence-similarity", "mteb", "transformers.js", "mlx", "en", "base_model:answerdotai/ModernBERT-base", "base_model:finetune:answerdotai/ModernBERT-base", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-04-02T15:07:20Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb - transformers.js - mlx license: apache-2.0 language: - en base_model: - answerdotai/ModernBERT-base - nomic-ai/modernbert-embed-unsupervised base_model_relation: finetune model-index: - name: binarize_False results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 78.13432835820896 - type: ap value: 42.190424731303246 - type: f1 value: 72.34446401534811 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.093825 - type: ap value: 90.03727505544286 - type: f1 value: 93.0874055138833 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 48.428000000000004 - type: f1 value: 47.74311520203536 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 23.898 - type: map_at_10 value: 39.775 - type: map_at_100 value: 40.827000000000005 - type: map_at_1000 value: 40.837 - type: map_at_20 value: 40.604 - type: map_at_3 value: 34.519 - type: map_at_5 value: 37.307 - type: mrr_at_1 value: 24.395 - type: mrr_at_10 value: 39.963 - type: mrr_at_100 value: 41.014 - type: mrr_at_1000 value: 41.024 - type: mrr_at_20 value: 40.791 - type: mrr_at_3 value: 34.732 - type: mrr_at_5 value: 37.480999999999995 - type: ndcg_at_1 value: 23.898 - type: ndcg_at_10 value: 48.962 - type: ndcg_at_100 value: 53.386 - type: ndcg_at_1000 value: 53.634 - type: ndcg_at_20 value: 51.898999999999994 - type: ndcg_at_3 value: 38.034 - type: ndcg_at_5 value: 43.036 - type: precision_at_1 value: 23.898 - type: precision_at_10 value: 7.852 - type: precision_at_100 value: 0.9769999999999999 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 4.4990000000000006 - type: precision_at_3 value: 16.073999999999998 - type: precision_at_5 value: 12.063 - type: recall_at_1 value: 23.898 - type: recall_at_10 value: 78.521 - type: recall_at_100 value: 97.724 - type: recall_at_1000 value: 99.644 - type: recall_at_20 value: 89.972 - type: recall_at_3 value: 48.222 - type: recall_at_5 value: 60.313 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 47.69067314293749 - type: v_measures value: - 0.4953006738713271 - 0.500982950617211 - 0.490168788349858 - 0.4924060458428337 - 0.475176328561399 - 0.47446297663785564 - 0.46948807073019405 - 0.4772028638329531 - 0.48735189935310713 - 0.48641173887761663 - 0.5575029526712674 - 0.5574020390232136 - 0.5536066904942645 - 0.5536169413675474 - 0.5566938602585987 - 0.5561143054736898 - 0.561846457174852 - 0.5511643632282144 - 0.5514762015499715 - 0.551824471283655 - 0.5148077891863135 - 0.29015461701593837 - 0.4430422977323321 - 0.40857527197890686 - 0.3479983114229163 - 0.27582001934225003 - 0.29595564003512503 - 0.22528676611734755 - 0.3073271865740206 - 1.0 - 0.2749401557058413 - 0.4953006738713271 - 0.500982950617211 - 0.490168788349858 - 0.4924060458428337 - 0.475176328561399 - 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type: recall_at_5 value: 36.858999999999995 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: BeIR/cqadupstack config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 29.098000000000003 - type: map_at_10 value: 38.97 - type: map_at_100 value: 40.293 - type: map_at_1000 value: 40.397 - type: map_at_20 value: 39.778999999999996 - type: map_at_3 value: 35.723 - type: map_at_5 value: 37.519999999999996 - type: mrr_at_1 value: 35.515 - type: mrr_at_10 value: 44.55 - type: mrr_at_100 value: 45.37 - type: mrr_at_1000 value: 45.412 - type: mrr_at_20 value: 45.054 - type: mrr_at_3 value: 41.835 - type: mrr_at_5 value: 43.356 - type: ndcg_at_1 value: 35.515 - type: ndcg_at_10 value: 44.91 - type: ndcg_at_100 value: 50.27700000000001 - type: ndcg_at_1000 value: 52.215 - type: ndcg_at_20 value: 47.235 - type: ndcg_at_3 value: 39.505 - type: ndcg_at_5 value: 42.016 - type: precision_at_1 value: 35.515 - type: precision_at_10 value: 8.152 - type: precision_at_100 value: 1.262 - type: precision_at_1000 value: 0.16 - type: precision_at_20 value: 4.851 - type: precision_at_3 value: 18.447 - type: precision_at_5 value: 13.321 - type: recall_at_1 value: 29.098000000000003 - type: recall_at_10 value: 57.115 - type: recall_at_100 value: 79.467 - type: recall_at_1000 value: 92.162 - type: recall_at_20 value: 65.161 - type: recall_at_3 value: 42.254000000000005 - type: recall_at_5 value: 48.415 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: BeIR/cqadupstack config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 27.372000000000003 - type: map_at_10 value: 37.781 - type: map_at_100 value: 39.128 - type: map_at_1000 value: 39.238 - type: map_at_20 value: 38.592 - type: map_at_3 value: 34.782999999999994 - type: map_at_5 value: 36.466 - type: mrr_at_1 value: 33.904 - type: mrr_at_10 value: 43.15 - type: mrr_at_100 value: 44.049 - type: mrr_at_1000 value: 44.107 - type: mrr_at_20 value: 43.721 - type: mrr_at_3 value: 40.677 - type: mrr_at_5 value: 42.19 - type: ndcg_at_1 value: 33.904 - type: ndcg_at_10 value: 43.527 - type: ndcg_at_100 value: 49.004999999999995 - type: ndcg_at_1000 value: 51.276999999999994 - type: ndcg_at_20 value: 45.988 - type: ndcg_at_3 value: 38.824999999999996 - type: ndcg_at_5 value: 41.04 - type: precision_at_1 value: 33.904 - type: precision_at_10 value: 7.854 - type: precision_at_100 value: 1.2309999999999999 - type: precision_at_1000 value: 0.16 - type: precision_at_20 value: 4.692 - type: precision_at_3 value: 18.531 - type: precision_at_5 value: 13.150999999999998 - type: recall_at_1 value: 27.372000000000003 - type: recall_at_10 value: 55.245999999999995 - type: recall_at_100 value: 78.278 - type: recall_at_1000 value: 93.718 - type: recall_at_20 value: 64.095 - type: recall_at_3 value: 41.665 - type: recall_at_5 value: 47.632000000000005 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: BeIR/cqadupstack config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 25.653 - type: map_at_10 value: 32.151 - type: map_at_100 value: 33.152 - type: map_at_1000 value: 33.243 - type: map_at_20 value: 32.717 - type: map_at_3 value: 30.287 - type: map_at_5 value: 31.25 - type: mrr_at_1 value: 28.988000000000003 - type: mrr_at_10 value: 35.131 - type: mrr_at_100 value: 36.002 - type: mrr_at_1000 value: 36.069 - type: mrr_at_20 value: 35.61 - type: mrr_at_3 value: 33.308 - type: mrr_at_5 value: 34.259 - type: ndcg_at_1 value: 28.988000000000003 - type: ndcg_at_10 value: 35.988 - type: ndcg_at_100 value: 40.764 - type: ndcg_at_1000 value: 43.112 - type: ndcg_at_20 value: 37.852999999999994 - type: ndcg_at_3 value: 32.562000000000005 - type: ndcg_at_5 value: 33.983000000000004 - type: precision_at_1 value: 28.988000000000003 - type: precision_at_10 value: 5.475 - type: precision_at_100 value: 0.8500000000000001 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_20 value: 3.229 - type: precision_at_3 value: 13.905999999999999 - type: precision_at_5 value: 9.386999999999999 - type: recall_at_1 value: 25.653 - type: recall_at_10 value: 44.962 - type: recall_at_100 value: 66.405 - type: recall_at_1000 value: 83.88799999999999 - type: recall_at_20 value: 51.79899999999999 - type: recall_at_3 value: 35.144999999999996 - type: recall_at_5 value: 38.814 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: BeIR/cqadupstack config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 17.825 - type: map_at_10 value: 25.592 - type: map_at_100 value: 26.613999999999997 - type: map_at_1000 value: 26.734 - type: map_at_20 value: 26.115 - type: map_at_3 value: 23.119 - type: map_at_5 value: 24.54 - type: mrr_at_1 value: 21.335 - type: mrr_at_10 value: 29.165000000000003 - type: mrr_at_100 value: 30.049 - type: mrr_at_1000 value: 30.121 - type: mrr_at_20 value: 29.639 - type: mrr_at_3 value: 26.863999999999997 - type: mrr_at_5 value: 28.185 - type: ndcg_at_1 value: 21.335 - type: ndcg_at_10 value: 30.357 - type: ndcg_at_100 value: 35.410000000000004 - type: ndcg_at_1000 value: 38.24 - type: ndcg_at_20 value: 32.08 - type: ndcg_at_3 value: 25.95 - type: ndcg_at_5 value: 28.081 - type: precision_at_1 value: 21.335 - type: precision_at_10 value: 5.506 - type: precision_at_100 value: 0.928 - type: precision_at_1000 value: 0.135 - type: precision_at_20 value: 3.2550000000000003 - type: precision_at_3 value: 12.239 - type: precision_at_5 value: 8.885 - type: recall_at_1 value: 17.825 - type: recall_at_10 value: 41.105999999999995 - type: recall_at_100 value: 64.17 - type: recall_at_1000 value: 84.19200000000001 - type: recall_at_20 value: 47.497 - type: recall_at_3 value: 28.862 - type: recall_at_5 value: 34.348 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: BeIR/cqadupstack config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 29.435 - type: map_at_10 value: 38.261 - type: map_at_100 value: 39.242 - type: map_at_1000 value: 39.347 - type: map_at_20 value: 38.742 - type: map_at_3 value: 35.457 - type: map_at_5 value: 37.043 - type: mrr_at_1 value: 34.235 - type: mrr_at_10 value: 42.24 - type: mrr_at_100 value: 42.988 - type: mrr_at_1000 value: 43.043 - type: mrr_at_20 value: 42.613 - type: mrr_at_3 value: 39.832 - type: mrr_at_5 value: 41.227000000000004 - type: ndcg_at_1 value: 34.235 - type: ndcg_at_10 value: 43.384 - type: ndcg_at_100 value: 48.14 - type: ndcg_at_1000 value: 50.414 - type: ndcg_at_20 value: 44.913 - type: ndcg_at_3 value: 38.454 - type: ndcg_at_5 value: 40.776 - type: precision_at_1 value: 34.235 - type: precision_at_10 value: 7.164 - type: precision_at_100 value: 1.065 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_20 value: 4.021 - type: precision_at_3 value: 17.226 - type: precision_at_5 value: 12.071 - type: recall_at_1 value: 29.435 - type: recall_at_10 value: 54.93900000000001 - type: recall_at_100 value: 76.176 - type: recall_at_1000 value: 91.989 - type: recall_at_20 value: 60.451 - type: recall_at_3 value: 41.332 - type: recall_at_5 value: 47.316 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: BeIR/cqadupstack config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 25.605 - type: map_at_10 value: 34.162 - type: map_at_100 value: 35.827999999999996 - type: map_at_1000 value: 36.04 - type: map_at_20 value: 35.016000000000005 - type: map_at_3 value: 30.984 - type: map_at_5 value: 32.717 - type: mrr_at_1 value: 30.435000000000002 - type: mrr_at_10 value: 38.681 - type: mrr_at_100 value: 39.656000000000006 - type: mrr_at_1000 value: 39.71 - type: mrr_at_20 value: 39.208999999999996 - type: mrr_at_3 value: 35.903 - type: mrr_at_5 value: 37.454 - type: ndcg_at_1 value: 30.435000000000002 - type: ndcg_at_10 value: 39.916000000000004 - type: ndcg_at_100 value: 45.958 - type: ndcg_at_1000 value: 48.449999999999996 - type: ndcg_at_20 value: 42.085 - type: ndcg_at_3 value: 34.696 - type: ndcg_at_5 value: 37.147000000000006 - type: precision_at_1 value: 30.435000000000002 - type: precision_at_10 value: 7.767 - type: precision_at_100 value: 1.547 - type: precision_at_1000 value: 0.23800000000000002 - type: precision_at_20 value: 4.941 - type: precision_at_3 value: 16.073999999999998 - type: precision_at_5 value: 11.937000000000001 - type: recall_at_1 value: 25.605 - type: recall_at_10 value: 50.654999999999994 - type: recall_at_100 value: 77.609 - type: recall_at_1000 value: 93.518 - type: recall_at_20 value: 58.845000000000006 - type: recall_at_3 value: 36.272 - type: recall_at_5 value: 42.596000000000004 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: BeIR/cqadupstack config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 23.666 - type: map_at_10 value: 30.980999999999998 - type: map_at_100 value: 32.0 - type: map_at_1000 value: 32.098 - type: map_at_20 value: 31.621 - type: map_at_3 value: 28.449999999999996 - type: map_at_5 value: 29.731999999999996 - type: mrr_at_1 value: 25.692999999999998 - type: mrr_at_10 value: 32.788000000000004 - type: mrr_at_100 value: 33.783 - type: mrr_at_1000 value: 33.849000000000004 - type: mrr_at_20 value: 33.408 - type: mrr_at_3 value: 30.561 - type: mrr_at_5 value: 31.716 - type: ndcg_at_1 value: 25.692999999999998 - type: ndcg_at_10 value: 35.428 - type: ndcg_at_100 value: 40.375 - type: ndcg_at_1000 value: 42.802 - type: ndcg_at_20 value: 37.621 - type: ndcg_at_3 value: 30.476999999999997 - type: ndcg_at_5 value: 32.621 - type: precision_at_1 value: 25.692999999999998 - type: precision_at_10 value: 5.508 - type: precision_at_100 value: 0.848 - type: precision_at_1000 value: 0.116 - type: precision_at_20 value: 3.272 - type: precision_at_3 value: 12.631 - type: precision_at_5 value: 8.872 - type: recall_at_1 value: 23.666 - type: recall_at_10 value: 47.532000000000004 - type: recall_at_100 value: 69.73700000000001 - type: recall_at_1000 value: 87.83800000000001 - type: recall_at_20 value: 55.61000000000001 - type: recall_at_3 value: 34.06 - type: recall_at_5 value: 39.254 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 16.337 - type: map_at_10 value: 26.488 - type: map_at_100 value: 28.415000000000003 - type: map_at_1000 value: 28.584 - type: map_at_20 value: 27.557 - type: map_at_3 value: 22.665 - type: map_at_5 value: 24.542 - type: mrr_at_1 value: 36.417 - type: mrr_at_10 value: 48.001 - type: mrr_at_100 value: 48.784 - type: mrr_at_1000 value: 48.809000000000005 - type: mrr_at_20 value: 48.507 - type: mrr_at_3 value: 45.103 - type: mrr_at_5 value: 46.843 - type: ndcg_at_1 value: 36.417 - type: ndcg_at_10 value: 35.67 - type: ndcg_at_100 value: 42.716 - type: ndcg_at_1000 value: 45.639 - type: ndcg_at_20 value: 38.471 - type: ndcg_at_3 value: 30.444 - type: ndcg_at_5 value: 32.004 - type: precision_at_1 value: 36.417 - type: precision_at_10 value: 10.73 - type: precision_at_100 value: 1.833 - type: precision_at_1000 value: 0.23800000000000002 - type: precision_at_20 value: 6.596 - type: precision_at_3 value: 22.302 - type: precision_at_5 value: 16.521 - type: recall_at_1 value: 16.337 - type: recall_at_10 value: 40.671 - type: recall_at_100 value: 64.55300000000001 - type: recall_at_1000 value: 80.934 - type: recall_at_20 value: 48.381 - type: recall_at_3 value: 27.279999999999998 - type: recall_at_5 value: 32.621 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 9.056000000000001 - type: map_at_10 value: 19.419 - type: map_at_100 value: 27.069 - type: map_at_1000 value: 28.666000000000004 - type: map_at_20 value: 22.434 - type: map_at_3 value: 13.895 - type: map_at_5 value: 16.121 - type: mrr_at_1 value: 69.0 - type: mrr_at_10 value: 75.804 - type: mrr_at_100 value: 76.117 - type: mrr_at_1000 value: 76.125 - type: mrr_at_20 value: 76.009 - type: mrr_at_3 value: 74.375 - type: mrr_at_5 value: 75.4 - type: ndcg_at_1 value: 57.49999999999999 - type: ndcg_at_10 value: 41.495 - type: ndcg_at_100 value: 45.208 - type: ndcg_at_1000 value: 52.221 - type: ndcg_at_20 value: 40.617999999999995 - type: ndcg_at_3 value: 46.592 - type: ndcg_at_5 value: 43.559 - type: precision_at_1 value: 69.0 - type: precision_at_10 value: 32.574999999999996 - type: precision_at_100 value: 10.205 - type: precision_at_1000 value: 2.036 - type: precision_at_20 value: 24.687 - type: precision_at_3 value: 49.75 - type: precision_at_5 value: 42.0 - type: recall_at_1 value: 9.056000000000001 - type: recall_at_10 value: 24.866 - type: recall_at_100 value: 50.097 - type: recall_at_1000 value: 72.038 - type: recall_at_20 value: 31.858999999999998 - type: recall_at_3 value: 15.096000000000002 - type: recall_at_5 value: 18.548000000000002 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 48.259999999999984 - type: f1 value: 43.1498589523159 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 74.798 - type: map_at_10 value: 83.454 - type: map_at_100 value: 83.623 - type: map_at_1000 value: 83.635 - type: map_at_20 value: 83.55 - type: map_at_3 value: 82.392 - type: map_at_5 value: 83.167 - type: mrr_at_1 value: 80.708 - type: mrr_at_10 value: 88.377 - type: mrr_at_100 value: 88.411 - type: mrr_at_1000 value: 88.411 - type: mrr_at_20 value: 88.402 - type: mrr_at_3 value: 87.646 - type: mrr_at_5 value: 88.232 - type: ndcg_at_1 value: 80.708 - type: ndcg_at_10 value: 87.35199999999999 - type: ndcg_at_100 value: 87.91600000000001 - type: ndcg_at_1000 value: 88.12299999999999 - type: ndcg_at_20 value: 87.593 - type: ndcg_at_3 value: 85.738 - type: ndcg_at_5 value: 86.845 - type: precision_at_1 value: 80.708 - type: precision_at_10 value: 10.432 - type: precision_at_100 value: 1.091 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_20 value: 5.296 - type: precision_at_3 value: 32.778 - type: precision_at_5 value: 20.399 - type: recall_at_1 value: 74.798 - type: recall_at_10 value: 94.459 - type: recall_at_100 value: 96.614 - type: recall_at_1000 value: 97.868 - type: recall_at_20 value: 95.254 - type: recall_at_3 value: 90.144 - type: recall_at_5 value: 92.965 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 20.008 - type: map_at_10 value: 32.731 - type: map_at_100 value: 34.467999999999996 - type: map_at_1000 value: 34.643 - type: map_at_20 value: 33.717000000000006 - type: map_at_3 value: 28.427999999999997 - type: map_at_5 value: 30.788 - type: mrr_at_1 value: 40.586 - type: mrr_at_10 value: 49.056 - type: mrr_at_100 value: 49.887 - type: mrr_at_1000 value: 49.929 - type: mrr_at_20 value: 49.552 - type: mrr_at_3 value: 46.785 - type: mrr_at_5 value: 48.004000000000005 - type: ndcg_at_1 value: 40.586 - type: ndcg_at_10 value: 40.589999999999996 - type: ndcg_at_100 value: 47.03 - type: ndcg_at_1000 value: 49.994 - type: ndcg_at_20 value: 43.229 - type: ndcg_at_3 value: 37.061 - type: ndcg_at_5 value: 37.992 - type: precision_at_1 value: 40.586 - type: precision_at_10 value: 11.219 - type: precision_at_100 value: 1.781 - type: precision_at_1000 value: 0.232 - type: precision_at_20 value: 6.705 - type: precision_at_3 value: 24.743000000000002 - type: precision_at_5 value: 18.086 - type: recall_at_1 value: 20.008 - type: recall_at_10 value: 47.412 - type: recall_at_100 value: 71.274 - type: recall_at_1000 value: 88.898 - type: recall_at_20 value: 55.706999999999994 - type: recall_at_3 value: 33.346 - type: recall_at_5 value: 39.112 - task: type: Retrieval dataset: name: MTEB HotpotQA type: None config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - 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0.514824404624281 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: None config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 85.75430649102938 - type: cos_sim_ap value: 73.62842656477649 - type: cos_sim_f1 value: 67.76023680315738 - type: cos_sim_precision value: 63.61741547012506 - type: cos_sim_recall value: 72.4802110817942 - type: dot_accuracy value: 85.7423854085951 - type: dot_ap value: 73.59147637253723 - type: dot_f1 value: 67.69498693867396 - type: dot_precision value: 64.03859731701577 - type: dot_recall value: 71.79419525065963 - type: euclidean_accuracy value: 85.7423854085951 - type: euclidean_ap value: 73.6288990409654 - type: euclidean_f1 value: 67.80415430267064 - type: euclidean_precision value: 63.79711493718009 - type: euclidean_recall value: 72.34828496042216 - type: manhattan_accuracy value: 85.69470107885796 - type: manhattan_ap value: 73.49219614602531 - type: manhattan_f1 value: 67.60809797550613 - type: manhattan_precision value: 64.22127255460589 - type: manhattan_recall value: 71.37203166226914 - type: max_accuracy value: 85.75430649102938 - type: max_ap value: 73.6288990409654 - type: max_f1 value: 67.80415430267064 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: None config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.08293553770326 - type: cos_sim_ap value: 86.21246419992926 - type: cos_sim_f1 value: 78.49922526377924 - type: cos_sim_precision value: 75.35769939084857 - type: cos_sim_recall value: 81.9140745303357 - type: dot_accuracy value: 89.08681647067955 - type: dot_ap value: 86.19733517196862 - type: dot_f1 value: 78.51132446157838 - type: dot_precision value: 75.70233755093287 - type: dot_recall value: 81.53680320295658 - type: euclidean_accuracy value: 89.07517367175069 - type: euclidean_ap value: 86.21198725320203 - type: euclidean_f1 value: 78.49867139061116 - type: euclidean_precision value: 75.38276155372839 - type: euclidean_recall value: 81.88327687095781 - type: manhattan_accuracy value: 89.0538285403811 - type: manhattan_ap value: 86.17785515765131 - type: manhattan_f1 value: 78.48184098593084 - type: manhattan_precision value: 74.34396308285694 - type: manhattan_recall value: 83.10748383122882 - type: max_accuracy value: 89.08681647067955 - type: max_ap value: 86.21246419992926 - type: max_f1 value: 78.51132446157838 --- # mlx-community/modernbert-embed-base-bf16 The Model [mlx-community/nomicai-modernbert-embed-base-bf16](https://huggingface.co/mlx-community/nomicai-modernbert-embed-base-bf16) was converted to MLX format from [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) using mlx-lm version 0.0.3. ## Use with mlx ```bash pip install mlx-embeddings ``` ```python from mlx_embeddings import load, generate import mlx.core as mx model, tokenizer = load("mlx-community/nomicai-modernbert-embed-base-bf16") # For text embeddings output = generate(model, processor, texts=["I like grapes", "I like fruits"]) embeddings = output.text_embeds # Normalized embeddings # Compute dot product between normalized embeddings similarity_matrix = mx.matmul(embeddings, embeddings.T) print("Similarity matrix between texts:") print(similarity_matrix) ```
veeru7474/llam321	veeru7474	2025-04-02T16:03:12Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-02T16:02:39Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
optimum/bert-base-cased-squad2-neuronx	optimum	2025-04-02T16:00:50Z	0	0	null	[ "bert", "license:apache-2.0", "region:us" ]	null	2025-04-02T15:58:56Z	--- license: apache-2.0 --- INFERENTIA2 ONLY ```py from transformers import AutoTokenizer from optimum.neuron import NeuronBertForQuestionAnswering input_shapes = {"batch_size": 1, "sequence_length": 128} compiler_args = {"auto_cast": "matmul", "auto_cast_type": "bf16"} neuron_model = NeuronBertForQuestionAnswering.from_pretrained( "deepset/bert-base-cased-squad2", export=True, input_shapes, compiler_args, ) # Save locally neuron_model.save_pretrained("bert_base_cased_squad2_neuronx") neuron_model.push_to_hub( "bert_base_cased_squad2_neuronx", repository_id="optimum/bert-base-cased-squad2-neuronx", # Replace with your HF Hub repo id ) ```
mlx-community/nomicai-modernbert-embed-base-4bit	mlx-community	2025-04-02T15:59:08Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "modernbert", "feature-extraction", "sentence-similarity", "mteb", "transformers.js", "mlx", "en", "base_model:answerdotai/ModernBERT-base", "base_model:finetune:answerdotai/ModernBERT-base", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-04-02T13:59:42Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb - transformers.js - mlx license: apache-2.0 language: - en base_model: - answerdotai/ModernBERT-base - nomic-ai/modernbert-embed-unsupervised base_model_relation: finetune model-index: - name: binarize_False results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 78.13432835820896 - type: ap value: 42.190424731303246 - type: f1 value: 72.34446401534811 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.093825 - type: ap value: 90.03727505544286 - type: f1 value: 93.0874055138833 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 48.428000000000004 - type: f1 value: 47.74311520203536 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 23.898 - type: map_at_10 value: 39.775 - type: map_at_100 value: 40.827000000000005 - type: map_at_1000 value: 40.837 - type: map_at_20 value: 40.604 - type: map_at_3 value: 34.519 - type: map_at_5 value: 37.307 - type: mrr_at_1 value: 24.395 - type: mrr_at_10 value: 39.963 - type: mrr_at_100 value: 41.014 - type: mrr_at_1000 value: 41.024 - type: mrr_at_20 value: 40.791 - type: mrr_at_3 value: 34.732 - type: mrr_at_5 value: 37.480999999999995 - type: ndcg_at_1 value: 23.898 - type: ndcg_at_10 value: 48.962 - type: ndcg_at_100 value: 53.386 - type: ndcg_at_1000 value: 53.634 - type: ndcg_at_20 value: 51.898999999999994 - type: ndcg_at_3 value: 38.034 - type: ndcg_at_5 value: 43.036 - type: precision_at_1 value: 23.898 - type: precision_at_10 value: 7.852 - type: precision_at_100 value: 0.9769999999999999 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 4.4990000000000006 - type: precision_at_3 value: 16.073999999999998 - type: precision_at_5 value: 12.063 - type: recall_at_1 value: 23.898 - type: recall_at_10 value: 78.521 - type: recall_at_100 value: 97.724 - type: recall_at_1000 value: 99.644 - type: recall_at_20 value: 89.972 - type: recall_at_3 value: 48.222 - type: recall_at_5 value: 60.313 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 47.69067314293749 - type: v_measures value: - 0.4953006738713271 - 0.500982950617211 - 0.490168788349858 - 0.4924060458428337 - 0.475176328561399 - 0.47446297663785564 - 0.46948807073019405 - 0.4772028638329531 - 0.48735189935310713 - 0.48641173887761663 - 0.5575029526712674 - 0.5574020390232136 - 0.5536066904942645 - 0.5536169413675474 - 0.5566938602585987 - 0.5561143054736898 - 0.561846457174852 - 0.5511643632282144 - 0.5514762015499715 - 0.551824471283655 - 0.5148077891863135 - 0.29015461701593837 - 0.4430422977323321 - 0.40857527197890686 - 0.3479983114229163 - 0.27582001934225003 - 0.29595564003512503 - 0.22528676611734755 - 0.3073271865740206 - 1.0 - 0.2749401557058413 - 0.4953006738713271 - 0.500982950617211 - 0.490168788349858 - 0.4924060458428337 - 0.475176328561399 - 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type: map_at_1 value: 25.653 - type: map_at_10 value: 32.151 - type: map_at_100 value: 33.152 - type: map_at_1000 value: 33.243 - type: map_at_20 value: 32.717 - type: map_at_3 value: 30.287 - type: map_at_5 value: 31.25 - type: mrr_at_1 value: 28.988000000000003 - type: mrr_at_10 value: 35.131 - type: mrr_at_100 value: 36.002 - type: mrr_at_1000 value: 36.069 - type: mrr_at_20 value: 35.61 - type: mrr_at_3 value: 33.308 - type: mrr_at_5 value: 34.259 - type: ndcg_at_1 value: 28.988000000000003 - type: ndcg_at_10 value: 35.988 - type: ndcg_at_100 value: 40.764 - type: ndcg_at_1000 value: 43.112 - type: ndcg_at_20 value: 37.852999999999994 - type: ndcg_at_3 value: 32.562000000000005 - type: ndcg_at_5 value: 33.983000000000004 - type: precision_at_1 value: 28.988000000000003 - type: precision_at_10 value: 5.475 - type: precision_at_100 value: 0.8500000000000001 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_20 value: 3.229 - type: precision_at_3 value: 13.905999999999999 - type: precision_at_5 value: 9.386999999999999 - type: recall_at_1 value: 25.653 - type: recall_at_10 value: 44.962 - type: recall_at_100 value: 66.405 - type: recall_at_1000 value: 83.88799999999999 - type: recall_at_20 value: 51.79899999999999 - type: recall_at_3 value: 35.144999999999996 - type: recall_at_5 value: 38.814 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: BeIR/cqadupstack config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 17.825 - type: map_at_10 value: 25.592 - type: map_at_100 value: 26.613999999999997 - type: map_at_1000 value: 26.734 - type: map_at_20 value: 26.115 - type: map_at_3 value: 23.119 - type: map_at_5 value: 24.54 - type: mrr_at_1 value: 21.335 - type: mrr_at_10 value: 29.165000000000003 - type: mrr_at_100 value: 30.049 - type: mrr_at_1000 value: 30.121 - type: mrr_at_20 value: 29.639 - type: mrr_at_3 value: 26.863999999999997 - type: mrr_at_5 value: 28.185 - type: ndcg_at_1 value: 21.335 - 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type: map_at_1000 value: 39.347 - type: map_at_20 value: 38.742 - type: map_at_3 value: 35.457 - type: map_at_5 value: 37.043 - type: mrr_at_1 value: 34.235 - type: mrr_at_10 value: 42.24 - type: mrr_at_100 value: 42.988 - type: mrr_at_1000 value: 43.043 - type: mrr_at_20 value: 42.613 - type: mrr_at_3 value: 39.832 - type: mrr_at_5 value: 41.227000000000004 - type: ndcg_at_1 value: 34.235 - type: ndcg_at_10 value: 43.384 - type: ndcg_at_100 value: 48.14 - type: ndcg_at_1000 value: 50.414 - type: ndcg_at_20 value: 44.913 - type: ndcg_at_3 value: 38.454 - type: ndcg_at_5 value: 40.776 - type: precision_at_1 value: 34.235 - type: precision_at_10 value: 7.164 - type: precision_at_100 value: 1.065 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_20 value: 4.021 - type: precision_at_3 value: 17.226 - type: precision_at_5 value: 12.071 - type: recall_at_1 value: 29.435 - type: recall_at_10 value: 54.93900000000001 - type: recall_at_100 value: 76.176 - type: recall_at_1000 value: 91.989 - type: recall_at_20 value: 60.451 - type: recall_at_3 value: 41.332 - type: recall_at_5 value: 47.316 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: BeIR/cqadupstack config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 25.605 - type: map_at_10 value: 34.162 - type: map_at_100 value: 35.827999999999996 - type: map_at_1000 value: 36.04 - type: map_at_20 value: 35.016000000000005 - type: map_at_3 value: 30.984 - type: map_at_5 value: 32.717 - type: mrr_at_1 value: 30.435000000000002 - type: mrr_at_10 value: 38.681 - type: mrr_at_100 value: 39.656000000000006 - type: mrr_at_1000 value: 39.71 - type: mrr_at_20 value: 39.208999999999996 - type: mrr_at_3 value: 35.903 - type: mrr_at_5 value: 37.454 - type: ndcg_at_1 value: 30.435000000000002 - type: ndcg_at_10 value: 39.916000000000004 - type: ndcg_at_100 value: 45.958 - type: ndcg_at_1000 value: 48.449999999999996 - type: ndcg_at_20 value: 42.085 - type: ndcg_at_3 value: 34.696 - type: ndcg_at_5 value: 37.147000000000006 - type: precision_at_1 value: 30.435000000000002 - type: precision_at_10 value: 7.767 - type: precision_at_100 value: 1.547 - type: precision_at_1000 value: 0.23800000000000002 - type: precision_at_20 value: 4.941 - type: precision_at_3 value: 16.073999999999998 - type: precision_at_5 value: 11.937000000000001 - type: recall_at_1 value: 25.605 - type: recall_at_10 value: 50.654999999999994 - type: recall_at_100 value: 77.609 - type: recall_at_1000 value: 93.518 - type: recall_at_20 value: 58.845000000000006 - type: recall_at_3 value: 36.272 - type: recall_at_5 value: 42.596000000000004 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: BeIR/cqadupstack config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 23.666 - type: map_at_10 value: 30.980999999999998 - type: map_at_100 value: 32.0 - type: map_at_1000 value: 32.098 - type: map_at_20 value: 31.621 - type: map_at_3 value: 28.449999999999996 - type: map_at_5 value: 29.731999999999996 - type: mrr_at_1 value: 25.692999999999998 - type: mrr_at_10 value: 32.788000000000004 - type: mrr_at_100 value: 33.783 - type: mrr_at_1000 value: 33.849000000000004 - type: mrr_at_20 value: 33.408 - type: mrr_at_3 value: 30.561 - type: mrr_at_5 value: 31.716 - type: ndcg_at_1 value: 25.692999999999998 - type: ndcg_at_10 value: 35.428 - type: ndcg_at_100 value: 40.375 - type: ndcg_at_1000 value: 42.802 - type: ndcg_at_20 value: 37.621 - type: ndcg_at_3 value: 30.476999999999997 - type: ndcg_at_5 value: 32.621 - type: precision_at_1 value: 25.692999999999998 - type: precision_at_10 value: 5.508 - type: precision_at_100 value: 0.848 - type: precision_at_1000 value: 0.116 - type: precision_at_20 value: 3.272 - type: precision_at_3 value: 12.631 - type: precision_at_5 value: 8.872 - type: recall_at_1 value: 23.666 - type: recall_at_10 value: 47.532000000000004 - type: recall_at_100 value: 69.73700000000001 - type: recall_at_1000 value: 87.83800000000001 - type: recall_at_20 value: 55.61000000000001 - type: recall_at_3 value: 34.06 - type: recall_at_5 value: 39.254 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 16.337 - type: map_at_10 value: 26.488 - type: map_at_100 value: 28.415000000000003 - type: map_at_1000 value: 28.584 - type: map_at_20 value: 27.557 - type: map_at_3 value: 22.665 - type: map_at_5 value: 24.542 - type: mrr_at_1 value: 36.417 - type: mrr_at_10 value: 48.001 - type: mrr_at_100 value: 48.784 - type: mrr_at_1000 value: 48.809000000000005 - type: mrr_at_20 value: 48.507 - type: mrr_at_3 value: 45.103 - type: mrr_at_5 value: 46.843 - type: ndcg_at_1 value: 36.417 - type: ndcg_at_10 value: 35.67 - type: ndcg_at_100 value: 42.716 - type: ndcg_at_1000 value: 45.639 - type: ndcg_at_20 value: 38.471 - type: ndcg_at_3 value: 30.444 - type: ndcg_at_5 value: 32.004 - type: precision_at_1 value: 36.417 - type: precision_at_10 value: 10.73 - type: precision_at_100 value: 1.833 - type: precision_at_1000 value: 0.23800000000000002 - type: precision_at_20 value: 6.596 - type: precision_at_3 value: 22.302 - type: precision_at_5 value: 16.521 - type: recall_at_1 value: 16.337 - type: recall_at_10 value: 40.671 - type: recall_at_100 value: 64.55300000000001 - type: recall_at_1000 value: 80.934 - type: recall_at_20 value: 48.381 - type: recall_at_3 value: 27.279999999999998 - type: recall_at_5 value: 32.621 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 9.056000000000001 - type: map_at_10 value: 19.419 - type: map_at_100 value: 27.069 - type: map_at_1000 value: 28.666000000000004 - type: map_at_20 value: 22.434 - type: map_at_3 value: 13.895 - type: map_at_5 value: 16.121 - type: mrr_at_1 value: 69.0 - type: mrr_at_10 value: 75.804 - type: mrr_at_100 value: 76.117 - type: mrr_at_1000 value: 76.125 - type: mrr_at_20 value: 76.009 - type: mrr_at_3 value: 74.375 - type: mrr_at_5 value: 75.4 - type: ndcg_at_1 value: 57.49999999999999 - type: ndcg_at_10 value: 41.495 - type: ndcg_at_100 value: 45.208 - type: ndcg_at_1000 value: 52.221 - type: ndcg_at_20 value: 40.617999999999995 - type: ndcg_at_3 value: 46.592 - type: ndcg_at_5 value: 43.559 - type: precision_at_1 value: 69.0 - type: precision_at_10 value: 32.574999999999996 - type: precision_at_100 value: 10.205 - type: precision_at_1000 value: 2.036 - type: precision_at_20 value: 24.687 - type: precision_at_3 value: 49.75 - type: precision_at_5 value: 42.0 - type: recall_at_1 value: 9.056000000000001 - type: recall_at_10 value: 24.866 - type: recall_at_100 value: 50.097 - type: recall_at_1000 value: 72.038 - type: recall_at_20 value: 31.858999999999998 - type: recall_at_3 value: 15.096000000000002 - type: recall_at_5 value: 18.548000000000002 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 48.259999999999984 - type: f1 value: 43.1498589523159 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 74.798 - type: map_at_10 value: 83.454 - type: map_at_100 value: 83.623 - type: map_at_1000 value: 83.635 - type: map_at_20 value: 83.55 - type: map_at_3 value: 82.392 - type: map_at_5 value: 83.167 - type: mrr_at_1 value: 80.708 - type: mrr_at_10 value: 88.377 - type: mrr_at_100 value: 88.411 - type: mrr_at_1000 value: 88.411 - type: mrr_at_20 value: 88.402 - type: mrr_at_3 value: 87.646 - type: mrr_at_5 value: 88.232 - type: ndcg_at_1 value: 80.708 - type: ndcg_at_10 value: 87.35199999999999 - type: ndcg_at_100 value: 87.91600000000001 - type: ndcg_at_1000 value: 88.12299999999999 - type: ndcg_at_20 value: 87.593 - type: ndcg_at_3 value: 85.738 - type: ndcg_at_5 value: 86.845 - type: precision_at_1 value: 80.708 - type: precision_at_10 value: 10.432 - type: precision_at_100 value: 1.091 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_20 value: 5.296 - type: precision_at_3 value: 32.778 - type: precision_at_5 value: 20.399 - type: recall_at_1 value: 74.798 - type: recall_at_10 value: 94.459 - type: recall_at_100 value: 96.614 - type: recall_at_1000 value: 97.868 - type: recall_at_20 value: 95.254 - type: recall_at_3 value: 90.144 - type: recall_at_5 value: 92.965 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 20.008 - type: map_at_10 value: 32.731 - type: map_at_100 value: 34.467999999999996 - type: map_at_1000 value: 34.643 - type: map_at_20 value: 33.717000000000006 - type: map_at_3 value: 28.427999999999997 - type: map_at_5 value: 30.788 - type: mrr_at_1 value: 40.586 - type: mrr_at_10 value: 49.056 - type: mrr_at_100 value: 49.887 - type: mrr_at_1000 value: 49.929 - type: mrr_at_20 value: 49.552 - 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type: map_at_1 value: 41.789 - type: map_at_10 value: 57.898 - type: map_at_100 value: 58.632 - type: map_at_1000 value: 58.693 - type: map_at_20 value: 58.314 - type: map_at_3 value: 55.236 - type: map_at_5 value: 56.852999999999994 - type: mrr_at_1 value: 83.57900000000001 - type: mrr_at_10 value: 87.631 - type: mrr_at_100 value: 87.764 - type: mrr_at_1000 value: 87.77000000000001 - type: mrr_at_20 value: 87.70700000000001 - type: mrr_at_3 value: 87.02499999999999 - type: mrr_at_5 value: 87.34100000000001 - type: ndcg_at_1 value: 83.57900000000001 - type: ndcg_at_10 value: 67.11399999999999 - type: ndcg_at_100 value: 69.686 - type: ndcg_at_1000 value: 70.926 - type: ndcg_at_20 value: 68.119 - type: ndcg_at_3 value: 63.402 - type: ndcg_at_5 value: 65.354 - type: precision_at_1 value: 83.57900000000001 - type: precision_at_10 value: 13.333 - type: precision_at_100 value: 1.537 - type: precision_at_1000 value: 0.16999999999999998 - type: precision_at_20 value: 6.988999999999999 - type: precision_at_3 value: 38.929 - type: precision_at_5 value: 24.897 - type: recall_at_1 value: 41.789 - type: recall_at_10 value: 66.664 - type: recall_at_100 value: 76.833 - type: recall_at_1000 value: 85.14500000000001 - type: recall_at_20 value: 69.892 - type: recall_at_3 value: 58.392999999999994 - type: recall_at_5 value: 62.242 - task: type: Classification dataset: name: MTEB ImdbClassification type: None config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 86.6108 - type: ap value: 81.63890253106925 - type: f1 value: 86.54585789538082 - task: type: Retrieval dataset: name: MTEB MSMARCO type: None config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 22.407 - type: map_at_10 value: 34.603 - type: map_at_100 value: 35.808 - type: map_at_1000 value: 35.855 - type: map_at_20 value: 35.368 - type: map_at_3 value: 30.764000000000003 - type: map_at_5 value: 32.964 - type: mrr_at_1 value: 23.009 - type: mrr_at_10 value: 35.136 - 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0.514824404624281 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: None config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 85.75430649102938 - type: cos_sim_ap value: 73.62842656477649 - type: cos_sim_f1 value: 67.76023680315738 - type: cos_sim_precision value: 63.61741547012506 - type: cos_sim_recall value: 72.4802110817942 - type: dot_accuracy value: 85.7423854085951 - type: dot_ap value: 73.59147637253723 - type: dot_f1 value: 67.69498693867396 - type: dot_precision value: 64.03859731701577 - type: dot_recall value: 71.79419525065963 - type: euclidean_accuracy value: 85.7423854085951 - type: euclidean_ap value: 73.6288990409654 - type: euclidean_f1 value: 67.80415430267064 - type: euclidean_precision value: 63.79711493718009 - type: euclidean_recall value: 72.34828496042216 - type: manhattan_accuracy value: 85.69470107885796 - type: manhattan_ap value: 73.49219614602531 - type: manhattan_f1 value: 67.60809797550613 - type: manhattan_precision value: 64.22127255460589 - type: manhattan_recall value: 71.37203166226914 - type: max_accuracy value: 85.75430649102938 - type: max_ap value: 73.6288990409654 - type: max_f1 value: 67.80415430267064 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: None config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.08293553770326 - type: cos_sim_ap value: 86.21246419992926 - type: cos_sim_f1 value: 78.49922526377924 - type: cos_sim_precision value: 75.35769939084857 - type: cos_sim_recall value: 81.9140745303357 - type: dot_accuracy value: 89.08681647067955 - type: dot_ap value: 86.19733517196862 - type: dot_f1 value: 78.51132446157838 - type: dot_precision value: 75.70233755093287 - type: dot_recall value: 81.53680320295658 - type: euclidean_accuracy value: 89.07517367175069 - type: euclidean_ap value: 86.21198725320203 - type: euclidean_f1 value: 78.49867139061116 - type: euclidean_precision value: 75.38276155372839 - type: euclidean_recall value: 81.88327687095781 - type: manhattan_accuracy value: 89.0538285403811 - type: manhattan_ap value: 86.17785515765131 - type: manhattan_f1 value: 78.48184098593084 - type: manhattan_precision value: 74.34396308285694 - type: manhattan_recall value: 83.10748383122882 - type: max_accuracy value: 89.08681647067955 - type: max_ap value: 86.21246419992926 - type: max_f1 value: 78.51132446157838 --- # mlx-community/nomicai-modernbert-embed-base-4bit The Model [mlx-community/nomicai-modernbert-embed-base-4bit](https://huggingface.co/mlx-community/nomicai-modernbert-embed-base-4bit) was converted to MLX format from [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) using mlx-lm version 0.0.3. ## Use with mlx ```bash pip install mlx-embeddings ``` ```python from mlx_embeddings import load, generate import mlx.core as mx model, tokenizer = load("mlx-community/nomicai-modernbert-embed-base-4bit") # For text embeddings output = generate(model, processor, texts=["I like grapes", "I like fruits"]) embeddings = output.text_embeds # Normalized embeddings # Compute dot product between normalized embeddings similarity_matrix = mx.matmul(embeddings, embeddings.T) print("Similarity matrix between texts:") print(similarity_matrix) ```
withpi/pi_scorer_bert_v3_init_t_24000	withpi	2025-04-02T15:57:57Z	0	0	transformers	[ "transformers", "safetensors", "modernbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-02T15:57:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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grnr9730/wan2.1_i2v_480p_14B_fp8_scaled	grnr9730	2025-04-02T15:55:46Z	0	0	null	[ "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-02T14:03:56Z	--- license: apache-2.0 ---
efraimdahl/notagen_lieder_labled_fullft	efraimdahl	2025-04-02T15:55:33Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T15:20:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Leyey/alexandre_delmar_LoRA	Leyey	2025-04-02T15:54:33Z	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-04-02T15:54:24Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: a photo in alexandre delmar style widget: [] tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - Leyey/alexandre_delmar_LoRA <Gallery /> ## Model description These are Leyey/alexandre_delmar_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo in alexandre delmar style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](Leyey/alexandre_delmar_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
MindX-Deepu/ChatBot	MindX-Deepu	2025-04-02T15:54:28Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-02T15:54:27Z	--- license: apache-2.0 ---
coffiee/rs23	coffiee	2025-04-02T15:54:04Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T15:49:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
diamandislabii/4-tumor-compact-h5	diamandislabii	2025-04-02T15:53:21Z	0	0	keras	[ "keras", "region:us" ]	null	2025-04-02T15:22:54Z	--- library_name: keras --- This model has been uploaded using the Keras library and can be used with JAX, TensorFlow, and PyTorch backends. This model card has been generated automatically and should be completed by the model author. See [Model Cards documentation](https://huggingface.co/docs/hub/model-cards) for more information. For more details about the model architecture, check out [config.json](./config.json).
missbrittanyb/sonia-lora	missbrittanyb	2025-04-02T15:51:38Z	0	0	null	[ "license:other", "region:us" ]	null	2025-04-02T15:12:23Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---
mlx-community/answerdotai-ModernBERT-base-6bit	mlx-community	2025-04-02T15:50:49Z	0	0	transformers	[ "transformers", "safetensors", "modernbert", "fill-mask", "masked-lm", "long-context", "mlx", "en", "license:apache-2.0", "autotrain_compatible", "region:us" ]	fill-mask	2025-04-02T15:47:52Z	--- library_name: transformers license: apache-2.0 language: - en tags: - fill-mask - masked-lm - long-context - modernbert - mlx pipeline_tag: fill-mask inference: false --- # mlx-community/answerdotai-ModernBERT-base-6bit The Model [mlx-community/answerdotai-ModernBERT-base-6bit](https://huggingface.co/mlx-community/answerdotai-ModernBERT-base-6bit) was converted to MLX format from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) using mlx-lm version 0.0.3. ## Use with mlx ```bash pip install mlx-embeddings ``` ```python from mlx_embeddings import load, generate import mlx.core as mx model, tokenizer = load("mlx-community/answerdotai-ModernBERT-base-6bit") # For text embeddings output = generate(model, processor, texts=["I like grapes", "I like fruits"]) embeddings = output.text_embeds # Normalized embeddings # Compute dot product between normalized embeddings similarity_matrix = mx.matmul(embeddings, embeddings.T) print("Similarity matrix between texts:") print(similarity_matrix) ```
kontol666/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hoarse_alert_woodpecker	kontol666	2025-04-02T15:50:43Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am hoarse alert woodpecker", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T15:50:18Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hoarse_alert_woodpecker tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am hoarse alert woodpecker - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hoarse_alert_woodpecker This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="kontol666/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hoarse_alert_woodpecker", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.50.3 - Pytorch: 2.5.1 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
nik311/therapy-lora-hermes	nik311	2025-04-02T15:49:36Z	0	1	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-02T15:49:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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PotatoOff/mxbai-rerank-xsmall-v1-safetensors	PotatoOff	2025-04-02T15:48:13Z	5	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "reranker", "transformers.js", "sentence-transformers", "text-ranking", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-ranking	2024-09-03T06:00:20Z	--- library_name: transformers tags: - reranker - transformers.js - sentence-transformers license: apache-2.0 language: - en pipeline_tag: text-ranking --- <br><br> <p align="center"> <svg xmlns="http://www.w3.org/2000/svg" xml:space="preserve" viewBox="0 0 2020 1130" width="150" height="150" aria-hidden="true"><path fill="#e95a0f" d="M398.167 621.992c-1.387-20.362-4.092-40.739-3.851-61.081.355-30.085 6.873-59.139 21.253-85.976 10.487-19.573 24.09-36.822 40.662-51.515 16.394-14.535 34.338-27.046 54.336-36.182 15.224-6.955 31.006-12.609 47.829-14.168 11.809-1.094 23.753-2.514 35.524-1.836 23.033 1.327 45.131 7.255 66.255 16.75 16.24 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fill="#eb5a57" d="M1216.359 827.958c-4.331-3.733-8.603-7.379-12.326-11.518l-26.664-30.44c-.866-.989-1.89-1.839-3.152-2.902 6.483-6.054 13.276-11.959 20.371-18.005l39.315 44.704c-5.648 6.216-11.441 12.12-17.544 18.161z"></path><path fill="#ec6168" d="M1231.598 334.101l38.999 36.066c.931.876 1.456 2.183 2.303 3.608-4.283 4.279-8.7 8.24-13.769 12.091-4.2-3.051-7.512-6.349-11.338-8.867-12.36-8.136-22.893-18.27-32.841-29.093l16.646-13.805z"></path><path fill="#ed656e" d="M1214.597 347.955c10.303 10.775 20.836 20.908 33.196 29.044 3.825 2.518 7.137 5.816 10.992 8.903-3.171 4.397-6.65 8.648-10.432 13.046-6.785-5.184-13.998-9.858-19.529-16.038-4.946-5.527-9.687-8.644-17.309-8.215-2.616.147-5.734-2.788-8.067-4.923-3.026-2.769-5.497-6.144-8.35-9.568 6.286-4.273 12.715-8.237 19.499-12.25z"></path></svg> </p> <p align="center"> <b>The crispy rerank family from <a href="https://mixedbread.ai"><b>Mixedbread</b></a>.</b> </p> # mxbai-rerank-xsmall-v1 This is the smallest model in our family of powerful reranker models. You can learn more about the models in our [blog post](https://www.mixedbread.ai/blog/mxbai-rerank-v1). We have three models: - [mxbai-rerank-xsmall-v1](https://huggingface.co/mixedbread-ai/mxbai-rerank-xsmall-v1) (🍞) - [mxbai-rerank-base-v1](https://huggingface.co/mixedbread-ai/mxbai-rerank-base-v1) - [mxbai-rerank-large-v1](https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v1) ## Quickstart Currently, the best way to use our models is with the most recent version of sentence-transformers. `pip install -U sentence-transformers` Let's say you have a query, and you want to rerank a set of documents. You can do that with only one line of code: ```python from sentence_transformers import CrossEncoder # Load the model, here we use our base sized model model = CrossEncoder("mixedbread-ai/mxbai-rerank-xsmall-v1") # Example query and documents query = "Who wrote 'To Kill a Mockingbird'?" documents = [ "'To Kill a Mockingbird' is a novel by Harper Lee published in 1960. It was immediately successful, winning the Pulitzer Prize, and has become a classic of modern American literature.", "The novel 'Moby-Dick' was written by Herman Melville and first published in 1851. It is considered a masterpiece of American literature and deals with complex themes of obsession, revenge, and the conflict between good and evil.", "Harper Lee, an American novelist widely known for her novel 'To Kill a Mockingbird', was born in 1926 in Monroeville, Alabama. She received the Pulitzer Prize for Fiction in 1961.", "Jane Austen was an English novelist known primarily for her six major novels, which interpret, critique and comment upon the British landed gentry at the end of the 18th century.", "The 'Harry Potter' series, which consists of seven fantasy novels written by British author J.K. Rowling, is among the most popular and critically acclaimed books of the modern era.", "'The Great Gatsby', a novel written by American author F. Scott Fitzgerald, was published in 1925. The story is set in the Jazz Age and follows the life of millionaire Jay Gatsby and his pursuit of Daisy Buchanan." ] # Lets get the scores results = model.rank(query, documents, return_documents=True, top_k=3) ``` <details> <summary>JavaScript Example</summary> Install [transformers.js](https://github.com/xenova/transformers.js) `npm i @xenova/transformers` Let's say you have a query, and you want to rerank a set of documents. In JavaScript, you need to add a function: ```javascript import { AutoTokenizer, AutoModelForSequenceClassification } from '@xenova/transformers'; const model_id = 'mixedbread-ai/mxbai-rerank-xsmall-v1'; const model = await AutoModelForSequenceClassification.from_pretrained(model_id); const tokenizer = await AutoTokenizer.from_pretrained(model_id); /** * Performs ranking with the CrossEncoder on the given query and documents. Returns a sorted list with the document indices and scores. * @param {string} query A single query * @param {string[]} documents A list of documents * @param {Object} options Options for ranking * @param {number} [options.top_k=undefined] Return the top-k documents. If undefined, all documents are returned. * @param {number} [options.return_documents=false] If true, also returns the documents. If false, only returns the indices and scores. / async function rank(query, documents, { top_k = undefined, return_documents = false, } = {}) { const inputs = tokenizer( new Array(documents.length).fill(query), { text_pair: documents, padding: true, truncation: true, } ) const { logits } = await model(inputs); return logits .sigmoid() .tolist() .map(([score], i) => ({ corpus_id: i, score, ...(return_documents ? { text: documents[i] } : {}) })) .sort((a, b) => b.score - a.score) .slice(0, top_k); } // Example usage: const query = "Who wrote 'To Kill a Mockingbird'?" const documents = [ "'To Kill a Mockingbird' is a novel by Harper Lee published in 1960. It was immediately successful, winning the Pulitzer Prize, and has become a classic of modern American literature.", "The novel 'Moby-Dick' was written by Herman Melville and first published in 1851. It is considered a masterpiece of American literature and deals with complex themes of obsession, revenge, and the conflict between good and evil.", "Harper Lee, an American novelist widely known for her novel 'To Kill a Mockingbird', was born in 1926 in Monroeville, Alabama. She received the Pulitzer Prize for Fiction in 1961.", "Jane Austen was an English novelist known primarily for her six major novels, which interpret, critique and comment upon the British landed gentry at the end of the 18th century.", "The 'Harry Potter' series, which consists of seven fantasy novels written by British author J.K. Rowling, is among the most popular and critically acclaimed books of the modern era.", "'The Great Gatsby', a novel written by American author F. Scott Fitzgerald, was published in 1925. The story is set in the Jazz Age and follows the life of millionaire Jay Gatsby and his pursuit of Daisy Buchanan." ] const results = await rank(query, documents, { return_documents: true, top_k: 3 }); console.log(results); ``` </details> ## Using API You can use the large model via our API as follows: ```python from mixedbread_ai.client import MixedbreadAI mxbai = MixedbreadAI(api_key="{MIXEDBREAD_API_KEY}") res = mxbai.reranking( model="mixedbread-ai/mxbai-rerank-large-v1", query="Who is the author of To Kill a Mockingbird?", input=[ "To Kill a Mockingbird is a novel by Harper Lee published in 1960. It was immediately successful, winning the Pulitzer Prize, and has become a classic of modern American literature.", "The novel Moby-Dick was written by Herman Melville and first published in 1851. It is considered a masterpiece of American literature and deals with complex themes of obsession, revenge, and the conflict between good and evil.", "Harper Lee, an American novelist widely known for her novel To Kill a Mockingbird, was born in 1926 in Monroeville, Alabama. She received the Pulitzer Prize for Fiction in 1961.", "Jane Austen was an English novelist known primarily for her six major novels, which interpret, critique and comment upon the British landed gentry at the end of the 18th century.", "The Harry Potter series, which consists of seven fantasy novels written by British author J.K. Rowling, is among the most popular and critically acclaimed books of the modern era.", "The Great Gatsby, a novel written by American author F. Scott Fitzgerald, was published in 1925. The story is set in the Jazz Age and follows the life of millionaire Jay Gatsby and his pursuit of Daisy Buchanan." ], top_k=3, return_input=false ) print(res.data) ``` The API comes with additional features, such as a continous trained reranker! Check out the [docs](https://www.mixedbread.ai/docs) for more information. ## Evaluation Our reranker models are designed to elevate your search. They work extremely well in combination with keyword search and can even outperform semantic search systems in many cases. \| Model \| NDCG@10 \| Accuracy@3 \| \| ------------------------------------------------------------------------------------- \| -------- \| ---------- \| \| Lexical Search (Lucene) \| 38.0 \| 66.4 \| \| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) \| 41.6 \| 66.9 \| \| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) \| 45.2 \| 70.6 \| \| cohere-embed-v3 (semantic search) \| 47.5 \| 70.9 \| \| [mxbai-rerank-xsmall-v1](https://huggingface.co/mixedbread-ai/mxbai-rerank-xsmall-v1) \| 43.9* \| 70.0 \| \| [mxbai-rerank-base-v1](https://huggingface.co/mixedbread-ai/mxbai-rerank-base-v1) \| 46.9 \| 72.3 \| \| [mxbai-rerank-large-v1](https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v1) \| 48.8 \| 74.9 \| The reported results are aggregated from 11 datasets of BEIR. We used [Pyserini](https://github.com/castorini/pyserini/) to evaluate the models. Find more in our [blog-post](https://www.mixedbread.ai/blog/mxbai-rerank-v1) and on this [spreadsheet](https://docs.google.com/spreadsheets/d/15ELkSMFv-oHa5TRiIjDvhIstH9dlc3pnZeO-iGz4Ld4/edit?usp=sharing). ## Community Please join our [Discord Community](https://discord.gg/jDfMHzAVfU) and share your feedback and thoughts! We are here to help and also always happy to chat. ## License Apache 2.0
jon-fernandes/whisper-small-100	jon-fernandes	2025-04-02T15:47:15Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-02T15:47:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Savoxism/ppo-LunarLander-v2	Savoxism	2025-04-02T15:46:18Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-04-02T15:45:39Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -23.84 +/- 16.96 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
KingEmpire/sn9_pre_c04_18	KingEmpire	2025-04-02T15:45:31Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T14:53:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Membersuger/miners_cp2_w32	Membersuger	2025-04-02T15:43:02Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T15:15:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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blackjack007/Qwen2-0.5B-GRPO-test	blackjack007	2025-04-02T15:43:00Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:AI-MO/NuminaMath-TIR", "arxiv:2402.03300", "base_model:Qwen/Qwen2-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2-0.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-03-19T20:34:29Z	--- base_model: Qwen/Qwen2-0.5B-Instruct datasets: AI-MO/NuminaMath-TIR library_name: transformers model_name: Qwen2-0.5B-GRPO-test tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen2-0.5B-GRPO-test This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on the [AI-MO/NuminaMath-TIR](https://huggingface.co/datasets/AI-MO/NuminaMath-TIR) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="blackjack007/Qwen2-0.5B-GRPO-test", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.16.0 - Transformers: 4.50.2 - Pytorch: 2.6.0+cu124 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Publishing/gemma-3	Publishing	2025-04-02T15:41:52Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3", "trl", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-02T15:41:38Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Publishing - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Membersuger/miners_cp2_w30	Membersuger	2025-04-02T15:41:34Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T15:15:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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pictgensupport/filmnoir	pictgensupport	2025-04-02T15:37:27Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-02T15:37:24Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: filmnoir --- # Filmnoir <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `filmnoir` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('pictgensupport/filmnoir', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
Azimjoon/llama3-finetuned	Azimjoon	2025-04-02T15:34:17Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:openlm-research/open_llama_3b_v2", "base_model:adapter:openlm-research/open_llama_3b_v2", "license:apache-2.0", "region:us" ]	null	2025-04-02T15:34:14Z	--- library_name: peft license: apache-2.0 base_model: openlm-research/open_llama_3b_v2 tags: - generated_from_trainer model-index: - name: llama3-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # llama3-finetuned This model is a fine-tuned version of [openlm-research/open_llama_3b_v2](https://huggingface.co/openlm-research/open_llama_3b_v2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.14.0 - Transformers 4.50.2 - Pytorch 2.6.0+cu124 - Tokenizers 0.21.1
newtts2017/f56p2amz	newtts2017	2025-04-02T15:33:02Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-02T15:20:38Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: f56p2amz --- # F56P2Amz <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `f56p2amz` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "f56p2amz", "lora_weights": "https://huggingface.co/newtts2017/f56p2amz/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('newtts2017/f56p2amz', weight_name='lora.safetensors') image = pipeline('f56p2amz').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/newtts2017/f56p2amz/discussions) to add images that show off what you’ve made with this LoRA.
kostiantynk-outlook/c2eef299-8164-46d8-8b99-443a9c277f10	kostiantynk-outlook	2025-04-02T15:32:48Z	0	0	peft	[ "peft", "generated_from_trainer", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:adapter:Qwen/Qwen2.5-7B-Instruct", "region:us" ]	null	2025-04-02T15:30:49Z	--- library_name: peft tags: - generated_from_trainer base_model: Qwen/Qwen2.5-7B-Instruct model-index: - name: kostiantynk-outlook/c2eef299-8164-46d8-8b99-443a9c277f10 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # kostiantynk-outlook/c2eef299-8164-46d8-8b99-443a9c277f10 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5683 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
fpadovani/fr_clm_childes_30	fpadovani	2025-04-02T15:32:25Z	0	1	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-25T21:50:38Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: child_30 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # child_30 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.3875 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 30 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 40000 - training_steps: 100000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:--------:\|:------:\|:---------------:\| \| No log \| 2.1459 \| 2000 \| 6.2006 \| \| 6.0988 \| 4.2918 \| 4000 \| 4.4864 \| \| 6.0988 \| 6.4378 \| 6000 \| 4.0450 \| \| 3.809 \| 8.5837 \| 8000 \| 3.7977 \| \| 3.809 \| 10.7296 \| 10000 \| 3.6229 \| \| 3.368 \| 12.8755 \| 12000 \| 3.4840 \| \| 3.368 \| 15.0215 \| 14000 \| 3.3730 \| \| 3.1113 \| 17.1674 \| 16000 \| 3.2718 \| \| 3.1113 \| 19.3133 \| 18000 \| 3.1845 \| \| 2.916 \| 21.4592 \| 20000 \| 3.1091 \| \| 2.916 \| 23.6052 \| 22000 \| 3.0571 \| \| 2.7615 \| 25.7511 \| 24000 \| 3.0031 \| \| 2.7615 \| 27.8970 \| 26000 \| 2.9622 \| \| 2.6375 \| 30.0429 \| 28000 \| 2.9277 \| \| 2.6375 \| 32.1888 \| 30000 \| 2.9047 \| \| 2.5336 \| 34.3348 \| 32000 \| 2.8888 \| \| 2.5336 \| 36.4807 \| 34000 \| 2.8873 \| \| 2.4456 \| 38.6266 \| 36000 \| 2.8729 \| \| 2.4456 \| 40.7725 \| 38000 \| 2.8654 \| \| 2.3643 \| 42.9185 \| 40000 \| 2.8761 \| \| 2.3643 \| 45.0644 \| 42000 \| 2.8874 \| \| 2.2761 \| 47.2103 \| 44000 \| 2.9046 \| \| 2.2761 \| 49.3562 \| 46000 \| 2.9111 \| \| 2.1878 \| 51.5021 \| 48000 \| 2.9330 \| \| 2.1878 \| 53.6481 \| 50000 \| 2.9497 \| \| 2.1083 \| 55.7940 \| 52000 \| 2.9701 \| \| 2.1083 \| 57.9399 \| 54000 \| 2.9880 \| \| 2.0358 \| 60.0858 \| 56000 \| 3.0221 \| \| 2.0358 \| 62.2318 \| 58000 \| 3.0519 \| \| 1.9684 \| 64.3777 \| 60000 \| 3.0712 \| \| 1.9684 \| 66.5236 \| 62000 \| 3.0901 \| \| 1.9112 \| 68.6695 \| 64000 \| 3.1114 \| \| 1.9112 \| 70.8155 \| 66000 \| 3.1317 \| \| 1.8591 \| 72.9614 \| 68000 \| 3.1540 \| \| 1.8591 \| 75.1073 \| 70000 \| 3.1873 \| \| 1.8075 \| 77.2532 \| 72000 \| 3.2064 \| \| 1.8075 \| 79.3991 \| 74000 \| 3.2267 \| \| 1.7658 \| 81.5451 \| 76000 \| 3.2442 \| \| 1.7658 \| 83.6910 \| 78000 \| 3.2605 \| \| 1.7261 \| 85.8369 \| 80000 \| 3.2768 \| \| 1.7261 \| 87.9828 \| 82000 \| 3.2917 \| \| 1.6897 \| 90.1288 \| 84000 \| 3.3144 \| \| 1.6897 \| 92.2747 \| 86000 \| 3.3288 \| \| 1.6562 \| 94.4206 \| 88000 \| 3.3447 \| \| 1.6562 \| 96.5665 \| 90000 \| 3.3496 \| \| 1.6284 \| 98.7124 \| 92000 \| 3.3609 \| \| 1.6284 \| 100.8584 \| 94000 \| 3.3714 \| \| 1.6032 \| 103.0043 \| 96000 \| 3.3779 \| \| 1.6032 \| 105.1502 \| 98000 \| 3.3861 \| \| 1.5821 \| 107.2961 \| 100000 \| 3.3875 \| ### Framework versions - Transformers 4.45.2 - Pytorch 2.5.1+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
MarfinF/marfin_emotion	MarfinF	2025-04-02T15:31:47Z	351	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "mood", "emotion", "indonesian", "nli", "zero-shot-classification", "id", "en", "base_model:MoritzLaurer/mDeBERTa-v3-base-mnli-xnli", "base_model:finetune:MoritzLaurer/mDeBERTa-v3-base-mnli-xnli", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	zero-shot-classification	2025-03-25T01:21:22Z	--- license: apache-2.0 language: - id - en base_model: MoritzLaurer/mDeBERTa-v3-base-mnli-xnli pipeline_tag: zero-shot-classification tags: - mood - emotion - indonesian - nli metrics: - accuracy library_name: transformers --- # Marfin Emotion Detection Model 🎵 This model is fine-tuned from `MoritzLaurer/mDeBERTa-v3-base-mnli-xnli` for emotion detection tasks based on chat context, specifically optimized for Indonesian and English. ## 📝 Use Case The model predicts the relationship between user chat input and emotional hypotheses. It helps detect emotions like: - `senang` (happy) - `sedih` (sad) - `marah` (angry) - `takut` (fear) - `cinta` (love) This is useful for: - Emotion-based music recommendation - Sentiment analysis in real-time chat apps - AI-driven mood detection systems ## 📊 Training Details - Base model: mDeBERTa-v3-base-mnli-xnli - Fine-tuned with custom NLI-style dataset - Metrics: Accuracy ## 🏷 Tags `Zero-Shot Classification`, `Emotion`, `Mood`, `Indonesian`, `English` ## 📥 Example Usage ```python from transformers import pipeline classifier = pipeline("zero-shot-classification", model="MarfinF/marfin_emotion") text = "Aku lagi sedih banget hari ini" labels = ["senang", "sedih", "marah", "takut", "cinta"] result = classifier(text, candidate_labels=labels) print(result)
cafeburger/lora_model	cafeburger	2025-04-02T15:31:23Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-02T15:28:46Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Membersuger/miners_cp2_w29	Membersuger	2025-04-02T15:31:14Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T15:14:51Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Membersuger/miners_cp2_w27	Membersuger	2025-04-02T15:30:33Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T15:14:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Felladrin/gguf-Q8_0-bge-reranker-v2-m3	Felladrin	2025-04-02T15:29:57Z	83	2	sentence-transformers	[ "sentence-transformers", "gguf", "transformers", "text-embeddings-inference", "llama-cpp", "gguf-my-repo", "text-ranking", "multilingual", "base_model:BAAI/bge-reranker-v2-m3", "base_model:quantized:BAAI/bge-reranker-v2-m3", "license:apache-2.0", "endpoints_compatible", "region:us", "feature-extraction" ]	text-ranking	2024-10-08T07:20:56Z	--- base_model: BAAI/bge-reranker-v2-m3 language: - multilingual license: apache-2.0 pipeline_tag: text-ranking tags: - transformers - sentence-transformers - text-embeddings-inference - llama-cpp - gguf-my-repo library_name: sentence-transformers --- # Felladrin/bge-reranker-v2-m3-Q8_0-GGUF This model was converted to GGUF format from [`BAAI/bge-reranker-v2-m3`](https://huggingface.co/BAAI/bge-reranker-v2-m3) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/BAAI/bge-reranker-v2-m3) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Felladrin/bge-reranker-v2-m3-Q8_0-GGUF --hf-file bge-reranker-v2-m3-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Felladrin/bge-reranker-v2-m3-Q8_0-GGUF --hf-file bge-reranker-v2-m3-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Felladrin/bge-reranker-v2-m3-Q8_0-GGUF --hf-file bge-reranker-v2-m3-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Felladrin/bge-reranker-v2-m3-Q8_0-GGUF --hf-file bge-reranker-v2-m3-q8_0.gguf -c 2048 ```
bowilleatyou/be487d5c-c04f-4129-800b-55f45acdab86	bowilleatyou	2025-04-02T15:29:37Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-02T15:06:50Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
pictgensupport/folkart	pictgensupport	2025-04-02T15:29:07Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-02T15:29:05Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: folkart --- # Folkart <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `folkart` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('pictgensupport/folkart', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
JennyGan/70b-MP20-7000	JennyGan	2025-04-02T15:27:57Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.1-70B-Instruct", "base_model:adapter:meta-llama/Llama-3.1-70B-Instruct", "region:us" ]	null	2025-04-02T15:26:29Z	--- base_model: meta-llama/Llama-3.1-70B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: QuantizationMethod.BITS_AND_BYTES - _load_in_8bit: False - _load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 - bnb_4bit_quant_storage: uint8 - load_in_4bit: True - load_in_8bit: False ### Framework versions - PEFT 0.6.0
DreadPoor/Irix_1.1-12B-Model_Stock-Q6_K-GGUF	DreadPoor	2025-04-02T15:27:56Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:DreadPoor/Irix_1.1-12B-Model_Stock", "base_model:quantized:DreadPoor/Irix_1.1-12B-Model_Stock", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-02T15:27:13Z	--- base_model: DreadPoor/Irix_1.1-12B-Model_Stock library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # DreadPoor/Irix_1.1-12B-Model_Stock-Q6_K-GGUF This model was converted to GGUF format from [`DreadPoor/Irix_1.1-12B-Model_Stock`](https://huggingface.co/DreadPoor/Irix_1.1-12B-Model_Stock) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/DreadPoor/Irix_1.1-12B-Model_Stock) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo DreadPoor/Irix_1.1-12B-Model_Stock-Q6_K-GGUF --hf-file irix_1.1-12b-model_stock-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo DreadPoor/Irix_1.1-12B-Model_Stock-Q6_K-GGUF --hf-file irix_1.1-12b-model_stock-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo DreadPoor/Irix_1.1-12B-Model_Stock-Q6_K-GGUF --hf-file irix_1.1-12b-model_stock-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo DreadPoor/Irix_1.1-12B-Model_Stock-Q6_K-GGUF --hf-file irix_1.1-12b-model_stock-q6_k.gguf -c 2048 ```
albertus-sussex/veriscrape-sbert-camera-wo-ref-deepseek-chat	albertus-sussex	2025-04-02T15:26:53Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "new", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:6964", "loss:TripletLoss", "custom_code", "arxiv:1908.10084", "arxiv:1703.07737", "base_model:Alibaba-NLP/gte-base-en-v1.5", "base_model:finetune:Alibaba-NLP/gte-base-en-v1.5", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-04-02T15:26:35Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:6964 - loss:TripletLoss base_model: Alibaba-NLP/gte-base-en-v1.5 widget: - source_sentence: $109.99 sentences: - model - $117.99 - Kodak EasyShare C195 14 Megapixel Compact Camera - Silver - price - source_sentence: Canon sentences: - price - $60.99 - manufacturer - Panasonic - source_sentence: $127.00 sentences: - Canon PowerShot D10 Point & Shoot Digital Camera - Blue - 16:9 - 3x Optical Zoom - 4x Digital Zoom - 2.5" Active Matrix TFT Color LCD - $311.00 - model - price - source_sentence: Canon Powershot A495 Point & Shoot Digital Camera - 10 Megapixel - 2.50"... sentences: - Canon, Inc - manufacturer - model - Panasonic Lumix Dmc-zs7 Point & Shoot Digital Camera - 12.1 Megapixel - 3"... - source_sentence: ': Bell + Howell' sentences: - ': Olympus' - Bell & Howell Z10T ZoomTouch 10MP Touchscreen Digital Camera with Movie Mode, 3x Optical Zoom Lens, 3.0" LCD Screen, USB 2.0 - Silver - manufacturer - model pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy - silhouette_cosine - silhouette_euclidean model-index: - name: SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5 results: - task: type: triplet name: Triplet dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy value: 0.9741601943969727 name: Cosine Accuracy - type: cosine_accuracy value: 0.9732558131217957 name: Cosine Accuracy - task: type: silhouette name: Silhouette dataset: name: Unknown type: unknown metrics: - type: silhouette_cosine value: 0.8823210597038269 name: Silhouette Cosine - type: silhouette_euclidean value: 0.7564524412155151 name: Silhouette Euclidean - type: silhouette_cosine value: 0.8747285008430481 name: Silhouette Cosine - type: silhouette_euclidean value: 0.7522649168968201 name: Silhouette Euclidean --- # SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) <!-- at revision a829fd0e060bb84554da0dfd354d0de0f7712b7f --> - Maximum Sequence Length: 32 tokens - Output Dimensionality: 768 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 32, 'do_lower_case': False}) with Transformer model: NewModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("albertus-sussex/veriscrape-sbert-camera-wo-ref-deepseek-chat") # Run inference sentences = [ ': Bell + Howell', ': Olympus', 'Bell & Howell Z10T ZoomTouch 10MP Touchscreen Digital Camera with Movie Mode, 3x Optical Zoom Lens, 3.0" LCD Screen, USB 2.0 - Silver', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Triplet * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy \| 0.9742 \| #### Silhouette * Evaluated with <code>veriscrape.training.SilhouetteEvaluator</code> \| Metric \| Value \| \|:----------------------\|:-----------\| \| silhouette_cosine \| 0.8823 \| \| silhouette_euclidean \| 0.7565 \| #### Triplet * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy \| 0.9733 \| #### Silhouette * Evaluated with <code>veriscrape.training.SilhouetteEvaluator</code> \| Metric \| Value \| \|:----------------------\|:-----------\| \| silhouette_cosine \| 0.8747 \| \| silhouette_euclidean \| 0.7523 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 6,964 training samples * Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, <code>pos_attr_name</code>, <code>neg_attr_name</code>, and <code>website_id</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| negative \| pos_attr_name \| neg_attr_name \| website_id \| \|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| string \| string \| int \| \| details \| <ul><li>min: 3 tokens</li><li>mean: 12.74 tokens</li><li>max: 32 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 12.66 tokens</li><li>max: 32 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 12.28 tokens</li><li>max: 32 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 3.0 tokens</li><li>max: 3 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 3.0 tokens</li><li>max: 3 tokens</li></ul> \| <ul><li>0: ~9.80%</li><li>1: ~9.00%</li><li>2: ~6.80%</li><li>3: ~11.00%</li><li>4: ~10.20%</li><li>5: ~9.60%</li><li>6: ~10.80%</li><li>7: ~10.70%</li><li>8: ~10.30%</li><li>9: ~11.80%</li></ul> \| * Samples: \| anchor \| positive \| negative \| pos_attr_name \| neg_attr_name \| website_id \| \|:-----------------------------------------------------------\|:---------------------------------------------------------------------------------------------------\|:-----------------------\|:--------------------------\|:--------------------------\|:---------------\| \| <code>Casio Computer Co., Ltd</code> \| <code>Eastman Kodak Company</code> \| <code>$324.99</code> \| <code>manufacturer</code> \| <code>price</code> \| <code>9</code> \| \| <code>$188.99</code> \| <code>$96.99</code> \| <code>Panasonic</code> \| <code>price</code> \| <code>manufacturer</code> \| <code>9</code> \| \| <code>GE J1250 Point & Shoot Digital Camera - Black</code> \| <code>Pentax K-r 12.4 Megapixel Digital SLR Camera (Body with Lens Kit) - 18 mm-55 mm - Red</code> \| <code>$299.00</code> \| <code>model</code> \| <code>price</code> \| <code>2</code> \| * Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 774 evaluation samples * Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, <code>pos_attr_name</code>, <code>neg_attr_name</code>, and <code>website_id</code> * Approximate statistics based on the first 774 samples: \| \| anchor \| positive \| negative \| pos_attr_name \| neg_attr_name \| website_id \| \|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| string \| string \| int \| \| details \| <ul><li>min: 3 tokens</li><li>mean: 13.09 tokens</li><li>max: 32 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 13.14 tokens</li><li>max: 32 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 12.73 tokens</li><li>max: 32 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 3.0 tokens</li><li>max: 3 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 3.0 tokens</li><li>max: 3 tokens</li></ul> \| <ul><li>0: ~10.98%</li><li>1: ~11.11%</li><li>2: ~5.30%</li><li>3: ~10.59%</li><li>4: ~10.34%</li><li>5: ~9.04%</li><li>6: ~10.59%</li><li>7: ~10.85%</li><li>8: ~12.02%</li><li>9: ~9.17%</li></ul> \| * Samples: \| anchor \| positive \| negative \| pos_attr_name \| neg_attr_name \| website_id \| \|:------------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------\|:--------------------------\|:-------------------\|:---------------\| \| <code>Eastman Kodak Company</code> \| <code>Panasonic</code> \| <code>Kodak EasyShare C143 12 Megapixel Compact Camera - Silver</code> \| <code>manufacturer</code> \| <code>model</code> \| <code>9</code> \| \| <code>SL605 - Digital camera - compact - 12.2 Mpix - optical zoom: 5 x - supported memory: SD, SDHC - black (EC-SL605ZBPBUS)</code> \| <code>EASYSHARE C195 - Digital camera - compact - 14.0 Mpix - optical zoom: 5 x - supported memory: SD, SDHC - silver (8770414)</code> \| <code>$184.99</code> \| <code>model</code> \| <code>price</code> \| <code>7</code> \| \| <code>$186.99</code> \| <code>$96.99</code> \| <code>10.2MP Cyber-shot TX7 Digital Camera - Red (DSCTX7/R)</code> \| <code>price</code> \| <code>model</code> \| <code>7</code> \| * Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `num_train_epochs`: 5 - `warmup_ratio`: 0.1 #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 5 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| Validation Loss \| cosine_accuracy \| silhouette_cosine \| \|:-----:\|:----:\|:-------------:\|:---------------:\|:---------------:\|:-----------------:\| \| -1 \| -1 \| - \| - \| 0.9057 \| 0.3675 \| \| 1.0 \| 55 \| 0.3791 \| 0.2219 \| 0.9703 \| 0.8847 \| \| 2.0 \| 110 \| 0.1751 \| 0.2184 \| 0.9703 \| 0.8844 \| \| 3.0 \| 165 \| 0.1592 \| 0.2365 \| 0.9703 \| 0.8832 \| \| 4.0 \| 220 \| 0.1416 \| 0.2636 \| 0.9742 \| 0.8812 \| \| 5.0 \| 275 \| 0.1219 \| 0.2560 \| 0.9742 \| 0.8823 \| \| -1 \| -1 \| - \| - \| 0.9733 \| 0.8747 \| ### Framework Versions - Python: 3.10.16 - Sentence Transformers: 4.0.1 - Transformers: 4.45.2 - PyTorch: 2.5.1+cu124 - Accelerate: 1.6.0 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### TripletLoss ```bibtex @misc{hermans2017defense, title={In Defense of the Triplet Loss for Person Re-Identification}, author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, year={2017}, eprint={1703.07737}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
oznuriscoding/rte-best-model	oznuriscoding	2025-04-02T15:25:19Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-02T15:25:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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albertus-sussex/veriscrape-sbert-camera-wo-ref-gpt-4o-mini	albertus-sussex	2025-04-02T15:24:41Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "new", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:7250", "loss:TripletLoss", "custom_code", "arxiv:1908.10084", "arxiv:1703.07737", "base_model:Alibaba-NLP/gte-base-en-v1.5", "base_model:finetune:Alibaba-NLP/gte-base-en-v1.5", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-04-02T15:24:19Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:7250 - loss:TripletLoss base_model: Alibaba-NLP/gte-base-en-v1.5 widget: - source_sentence: $319.88 sentences: - Panasonic - price - manufacturer - $155.56 - source_sentence: Panasonic sentences: - model - International Innovations, Inc - manufacturer - Panasonic Lumix DMC-ZR1 Point & Shoot Digital Camera - Red - source_sentence: $89.00 sentences: - PW550 Compact Camera - model - $163.00 - price - source_sentence: Coolpix S4000 Compact Camera sentences: - model - Canon, Inc - manufacturer - Exilim EX-FS10 Compact Camera - source_sentence: Olympus sentences: - model - Olympus - manufacturer - Polycom pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy - silhouette_cosine - silhouette_euclidean model-index: - name: SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5 results: - task: type: triplet name: Triplet dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy value: 0.9714640378952026 name: Cosine Accuracy - type: cosine_accuracy value: 0.9642857313156128 name: Cosine Accuracy - task: type: silhouette name: Silhouette dataset: name: Unknown type: unknown metrics: - type: silhouette_cosine value: 0.8579733371734619 name: Silhouette Cosine - type: silhouette_euclidean value: 0.7253183722496033 name: Silhouette Euclidean - type: silhouette_cosine value: 0.8245989680290222 name: Silhouette Cosine - type: silhouette_euclidean value: 0.6966991424560547 name: Silhouette Euclidean --- # SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) <!-- at revision a829fd0e060bb84554da0dfd354d0de0f7712b7f --> - Maximum Sequence Length: 32 tokens - Output Dimensionality: 768 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 32, 'do_lower_case': False}) with Transformer model: NewModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("albertus-sussex/veriscrape-sbert-camera-wo-ref-gpt-4o-mini") # Run inference sentences = [ 'Olympus', 'Polycom', 'Olympus', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Triplet * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy \| 0.9715 \| #### Silhouette * Evaluated with <code>veriscrape.training.SilhouetteEvaluator</code> \| Metric \| Value \| \|:----------------------\|:----------\| \| silhouette_cosine \| 0.858 \| \| silhouette_euclidean \| 0.7253 \| #### Triplet * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy \| 0.9643 \| #### Silhouette * Evaluated with <code>veriscrape.training.SilhouetteEvaluator</code> \| Metric \| Value \| \|:----------------------\|:-----------\| \| silhouette_cosine \| 0.8246 \| \| silhouette_euclidean \| 0.6967 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 7,250 training samples * Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, <code>pos_attr_name</code>, <code>neg_attr_name</code>, and <code>website_id</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| negative \| pos_attr_name \| neg_attr_name \| website_id \| \|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| string \| string \| int \| \| details \| <ul><li>min: 3 tokens</li><li>mean: 10.67 tokens</li><li>max: 32 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 10.73 tokens</li><li>max: 32 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 9.78 tokens</li><li>max: 32 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 3.0 tokens</li><li>max: 3 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 3.0 tokens</li><li>max: 3 tokens</li></ul> \| <ul><li>0: ~8.70%</li><li>1: ~9.80%</li><li>2: ~9.30%</li><li>3: ~8.60%</li><li>4: ~11.60%</li><li>5: ~9.00%</li><li>6: ~10.70%</li><li>7: ~11.60%</li><li>8: ~10.50%</li><li>9: ~10.20%</li></ul> \| * Samples: \| anchor \| positive \| negative \| pos_attr_name \| neg_attr_name \| website_id \| \|:---------------------\|:---------------------\|:------------------------------------------------\|:--------------------------\|:--------------------------\|:---------------\| \| <code>$266.76</code> \| <code>$166.96</code> \| <code>Sony Corporation</code> \| <code>price</code> \| <code>manufacturer</code> \| <code>1</code> \| \| <code>SAMSUNG</code> \| <code>Kodak</code> \| <code>SAMSUNG SL605 Black Digital Camera</code> \| <code>manufacturer</code> \| <code>model</code> \| <code>6</code> \| \| <code>Kodak</code> \| <code>Nikon</code> \| <code>$67.99</code> \| <code>manufacturer</code> \| <code>price</code> \| <code>4</code> \| * Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 806 evaluation samples * Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, <code>pos_attr_name</code>, <code>neg_attr_name</code>, and <code>website_id</code> * Approximate statistics based on the first 806 samples: \| \| anchor \| positive \| negative \| pos_attr_name \| neg_attr_name \| website_id \| \|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| string \| string \| int \| \| details \| <ul><li>min: 3 tokens</li><li>mean: 10.67 tokens</li><li>max: 32 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 10.61 tokens</li><li>max: 32 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 10.16 tokens</li><li>max: 32 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 3.0 tokens</li><li>max: 3 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 3.0 tokens</li><li>max: 3 tokens</li></ul> \| <ul><li>0: ~9.80%</li><li>1: ~9.68%</li><li>2: ~10.30%</li><li>3: ~9.80%</li><li>4: ~10.67%</li><li>5: ~9.93%</li><li>6: ~11.41%</li><li>7: ~7.94%</li><li>8: ~10.05%</li><li>9: ~10.42%</li></ul> \| * Samples: \| anchor \| positive \| negative \| pos_attr_name \| neg_attr_name \| website_id \| \|:-------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:-----------------------------------------------------------------------\|:--------------------------\|:--------------------------\|:---------------\| \| <code>Canon PowerShot SD1400 IS 14.1 Megapixel Compact Camera - 5 mm-20 mm - Silver</code> \| <code>International Innovations SS1000 5 Megapixel Compact Camera-6.50 mm</code> \| <code>Canon, Inc</code> \| <code>model</code> \| <code>manufacturer</code> \| <code>8</code> \| \| <code>FinePix A150 Compact Camera</code> \| <code>Cyber-shot DSC-W350 Compact Camera</code> \| <code>Nikon Corporation</code> \| <code>model</code> \| <code>manufacturer</code> \| <code>9</code> \| \| <code>Sony</code> \| <code>Nikon</code> \| <code>New! SONY Cyber-shot DSC-H55 14.1MP Digital Camera Silver</code> \| <code>manufacturer</code> \| <code>model</code> \| <code>0</code> \| * Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `num_train_epochs`: 5 - `warmup_ratio`: 0.1 #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 5 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| Validation Loss \| cosine_accuracy \| silhouette_cosine \| \|:-----:\|:----:\|:-------------:\|:---------------:\|:---------------:\|:-----------------:\| \| -1 \| -1 \| - \| - \| 0.9057 \| 0.3489 \| \| 1.0 \| 57 \| 0.3625 \| 0.2596 \| 0.9727 \| 0.8412 \| \| 2.0 \| 114 \| 0.172 \| 0.2100 \| 0.9727 \| 0.8578 \| \| 3.0 \| 171 \| 0.1677 \| 0.2118 \| 0.9715 \| 0.8345 \| \| 4.0 \| 228 \| 0.1716 \| 0.2267 \| 0.9715 \| 0.8541 \| \| 5.0 \| 285 \| 0.1578 \| 0.2303 \| 0.9715 \| 0.8580 \| \| -1 \| -1 \| - \| - \| 0.9643 \| 0.8246 \| ### Framework Versions - Python: 3.10.16 - Sentence Transformers: 4.0.1 - Transformers: 4.45.2 - PyTorch: 2.5.1+cu124 - Accelerate: 1.6.0 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### TripletLoss ```bibtex @misc{hermans2017defense, title={In Defense of the Triplet Loss for Person Re-Identification}, author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, year={2017}, eprint={1703.07737}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
Jadhesh/jadhesh-long-hair	Jadhesh	2025-04-02T15:24:35Z	0	0	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "fluxgym", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-02T15:24:26Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym base_model: black-forest-labs/FLUX.1-dev instance_prompt: JadLH2025 license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # Jadhesh Long Hair A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `JadLH2025` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc. Weights for this model are available in Safetensors format.
DreadPoor/Irixium-12B-Model_Stock-Q4_K_M-GGUF	DreadPoor	2025-04-02T15:24:00Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:DreadPoor/Irixium-12B-Model_Stock", "base_model:quantized:DreadPoor/Irixium-12B-Model_Stock", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-02T15:23:26Z	--- base_model: DreadPoor/Irixium-12B-Model_Stock library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # DreadPoor/Irixium-12B-Model_Stock-Q4_K_M-GGUF This model was converted to GGUF format from [`DreadPoor/Irixium-12B-Model_Stock`](https://huggingface.co/DreadPoor/Irixium-12B-Model_Stock) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/DreadPoor/Irixium-12B-Model_Stock) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo DreadPoor/Irixium-12B-Model_Stock-Q4_K_M-GGUF --hf-file irixium-12b-model_stock-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo DreadPoor/Irixium-12B-Model_Stock-Q4_K_M-GGUF --hf-file irixium-12b-model_stock-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo DreadPoor/Irixium-12B-Model_Stock-Q4_K_M-GGUF --hf-file irixium-12b-model_stock-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo DreadPoor/Irixium-12B-Model_Stock-Q4_K_M-GGUF --hf-file irixium-12b-model_stock-q4_k_m.gguf -c 2048 ```
tomvoelker/gpt22gpt2-gpt2-medium-cnn-dailymail-seed42	tomvoelker	2025-04-02T15:22:39Z	141	0	transformers	[ "transformers", "tensorboard", "safetensors", "encoder-decoder", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-03-13T12:55:03Z	--- library_name: transformers tags: - generated_from_trainer metrics: - rouge model-index: - name: gpt22gpt2-gpt2-medium-cnn-dailymail-seed42 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt22gpt2-gpt2-medium-cnn-dailymail-seed42 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7212 - Rouge1: 0.2011 - Rouge2: 0.0413 - Rougel: 0.1250 - Rougelsum: 0.1887 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\| \| 2.8287 \| 0.2229 \| 2000 \| 2.6199 \| 0.1926 \| 0.0340 \| 0.1248 \| 0.1800 \| \| 2.4721 \| 0.4458 \| 4000 \| 2.2632 \| 0.1971 \| 0.0382 \| 0.1284 \| 0.1816 \| \| 2.2324 \| 0.6687 \| 6000 \| 2.0400 \| 0.2164 \| 0.0487 \| 0.1345 \| 0.2025 \| \| 2.1025 \| 0.8916 \| 8000 \| 1.9285 \| 0.1827 \| 0.0330 \| 0.1175 \| 0.1691 \| \| 1.8926 \| 1.1145 \| 10000 \| 1.8595 \| 0.1712 \| 0.0288 \| 0.1124 \| 0.1598 \| \| 1.8605 \| 1.3374 \| 12000 \| 1.8191 \| 0.1919 \| 0.0371 \| 0.1230 \| 0.1790 \| \| 1.8327 \| 1.5603 \| 14000 \| 1.7931 \| 0.1838 \| 0.0346 \| 0.1189 \| 0.1724 \| \| 1.8209 \| 1.7832 \| 16000 \| 1.7606 \| 0.1847 \| 0.0342 \| 0.1169 \| 0.1733 \| \| 1.7879 \| 2.0061 \| 18000 \| 1.7484 \| 0.1949 \| 0.0390 \| 0.1227 \| 0.1830 \| \| 1.6658 \| 2.2290 \| 20000 \| 1.7460 \| 0.1910 \| 0.0358 \| 0.1201 \| 0.1798 \| \| 1.6547 \| 2.4519 \| 22000 \| 1.7386 \| 0.1906 \| 0.0360 \| 0.1196 \| 0.1791 \| \| 1.6426 \| 2.6748 \| 24000 \| 1.7256 \| 0.1953 \| 0.0387 \| 0.1226 \| 0.1833 \| \| 1.6495 \| 2.8977 \| 26000 \| 1.7212 \| 0.2011 \| 0.0413 \| 0.1250 \| 0.1887 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0 - Datasets 2.21.0 - Tokenizers 0.19.1
Membersuger/miners_cp2_w24	Membersuger	2025-04-02T15:21:03Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T15:14:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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sigridjineth/ko-reranker-v1.1	sigridjineth	2025-04-02T15:20:51Z	918	6	null	[ "safetensors", "new", "reranker", "korean", "text-ranking", "custom_code", "ko", "dataset:sigridjineth/korean_nli_dataset_reranker_v0", "arxiv:2407.19669", "arxiv:2312.15503", "arxiv:2402.03216", "base_model:Alibaba-NLP/gte-multilingual-reranker-base", "base_model:finetune:Alibaba-NLP/gte-multilingual-reranker-base", "license:apache-2.0", "region:us" ]	text-ranking	2024-12-11T06:26:50Z	--- license: apache-2.0 language: - ko base_model: - Alibaba-NLP/gte-multilingual-reranker-base datasets: - sigridjineth/korean_nli_dataset_reranker_v0 tags: - reranker - korean pipeline_tag: text-ranking --- # Model Card: sigridjineth/ko-reranker-v1.1 - The model, which is finetuned from [Alibaba-NLP/gte-multilingual-reranker-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-reranker-base) is currently under development and may undergo further changes as we refine and improve its performance. Underwent A100 x 8 with 12 hours for training. ## Training Data This model is trained on 328K Korean Triplets, which aggregates several publicly available datasets, ensuring rich linguistic diversity: - kor_nli (train): [https://huggingface.co/datasets/kor_nli](https://huggingface.co/datasets/kor_nli) - mnli_ko (train): [https://huggingface.co/datasets/kozistr/mnli_ko](https://huggingface.co/datasets/kozistr/mnli_ko) - ko-wiki-reranking (train): [https://huggingface.co/datasets/upskyy/ko-wiki-reranking](https://huggingface.co/datasets/upskyy/ko-wiki-reranking) - mr_tydi_korean (train): [https://huggingface.co/datasets/castorini/mr-tydi](https://huggingface.co/datasets/castorini/mr-tydi) - klue_nli (train): [https://huggingface.co/datasets/klue/klue](https://huggingface.co/datasets/klue/klue) These combined resources ensure coverage across a wide range of topics, styles, and complexities in Korean language data, enabling the model to capture nuanced semantic differences. ## Key Features - Hard Negative Mining: Integrated [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) to mine challenging negatives. This approach sharpens the model’s ability to distinguish subtle contrasts, boosting robustness and improving ranking quality. - Teacher-Student Distillation: Leveraged [BAAI/bge-reranker-v2.5-gemma2-lightweight](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight) as a teacher model. The student reranker learned from teacher-provided positive/negative scores, accelerating convergence and achieving better final performance. ## Intended Use - Search & Information Retrieval: Improve document ranking for Korean-language search queries. - Question Answering (QA): Enhance QA pipelines by reordering candidate answers for improved relevance. - Content Recommendation: Refine recommendation engines that rely on textual signals to deliver more accurate suggestions. ## Limitations & Future Work - Preview Release: The model is still in the refinement phase. Expect future updates to improve stability, generalization, and performance. - Need for Evaluation: Developing and standardizing benchmarks for generalized Korean retrieval tasks (especially for rerankers) will be an ongoing effort. ## Evaluation The [AutoRAG Benchmark](https://github.com/Marker-Inc-Korea/AutoRAG-example-korean-embedding-benchmark) serves as both the evaluation dataset and the toolkit for reporting these metrics. ### Model: `sigridjineth/ko-reranker-v1.1-preview` \| top_k \| Execution Time \| F1 \| Recall \| Precision \| MAP \| MRR \| NDCG \| Is Best \| \|-------\|----------------\|--------\|--------\|-----------\|--------\|--------\|--------\|---------\| \| 1 \| 0.0438 \| 0.6754 \| 0.6754 \| 0.6754 \| 0.6754 \| 0.6754 \| 0.6754 \| True \| \| 3 \| 0.0486 \| 0.3684 \| 0.7368 \| 0.2456 \| 0.7032 \| 0.7032 \| 0.7119 \| False \| \| 5 \| 0.0446 \| 0.3684 \| 0.7368 \| 0.2456 \| 0.7032 \| 0.7032 \| 0.7119 \| False \| --- ### Model: `Alibaba-NLP/gte-multilingual-reranker-base` \| top_k \| Execution Time \| F1 \| Recall \| Precision \| MAP \| MRR \| NDCG \| Is Best \| \|-------\|----------------\|--------\|--------\|-----------\|--------\|--------\|--------\|---------\| \| 1 \| 0.0481 \| 0.6316 \| 0.6316 \| 0.6316 \| 0.6316 \| 0.6316 \| 0.6316 \| True \| \| 3 \| 0.0427 \| 0.3596 \| 0.7193 \| 0.2398 \| 0.6725 \| 0.6725 \| 0.6846 \| False \| \| 5 \| 0.0442 \| 0.3596 \| 0.7193 \| 0.2398 \| 0.6725 \| 0.6725 \| 0.6846 \| False \| --- ### Model: `dragonkue/bge-reranker-v2-m3-ko` \| top_k \| Execution Time \| F1 \| Recall \| Precision \| MAP \| MRR \| NDCG \| Is Best \| \|-------\|----------------\|--------\|--------\|-----------\|--------\|--------\|--------\|---------\| \| 1 \| 0.0814 \| 0.6930 \| 0.6930 \| 0.6930 \| 0.6930 \| 0.6930 \| 0.6930 \| True \| \| 3 \| 0.0813 \| 0.3596 \| 0.7193 \| 0.2398 \| 0.7061 \| 0.7061 \| 0.7096 \| False \| \| 5 \| 0.0824 \| 0.3596 \| 0.7193 \| 0.2398 \| 0.7061 \| 0.7061 \| 0.7096 \| False \| ``` Evaluation Results (k=1,3,5,10): Accuracy@1: 0.8070 F1@1: 0.8070 Recall@1: 0.8070 Precision@1: 0.8070 Accuracy@3: 0.9211 F1@3: 0.4605 Recall@3: 0.9211 Precision@3: 0.3070 Accuracy@5: 0.9474 F1@5: 0.3158 Recall@5: 0.9474 Precision@5: 0.1895 Accuracy@10: 0.9737 F1@10: 0.1770 Recall@10: 0.9737 Precision@10: 0.0974 Total inference time (all queries): 142.64 sec Average inference time (per query): 1.2512 sec ``` ## Usage (transformers>=4.36.0) ```python import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer model_name_or_path = "sigridjineth/ko-reranker-v1.1-preview" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) model = AutoModelForSequenceClassification.from_pretrained( model_name_or_path, trust_remote_code=True, torch_dtype=torch.float16 ) model.eval() pairs = [ ["중국의 수도는","베이징"], ["2024년 대한민국 대통령은?", "대한민국 대통령은 윤석열이다"], ["파이썬에서 퀵 소트를 구현하기","quick sort로 코테 1등 먹어보자"] ] with torch.no_grad(): inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) scores = model(**inputs, return_dict=True).logits.view(-1, ).float() print(scores) # Example output: # tensor([1.2315, 0.5923, 0.3041]) ``` ## Usage with Infinity [Infinity](https://github.com/michaelfeil/infinity) is an MIT-licensed inference REST API server that can easily host and serve models. For instance: ```bash docker run --gpus all -v $PWD/data:/app/.cache -p "7997":"7997" \ michaelf34/infinity:0.0.68 \ v2 --model-id Alibaba-NLP/gte-multilingual-reranker-base --revision "main" \ --dtype bfloat16 --batch-size 32 --device cuda --engine torch --port 7997 ``` ## References ``` @misc{zhang2024mgtegeneralizedlongcontexttext, title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval}, author={Xin Zhang and Yanzhao Zhang and Dingkun Long and Wen Xie and Ziqi Dai and Jialong Tang and Huan Lin and Baosong Yang and Pengjun Xie and Fei Huang and Meishan Zhang and Wenjie Li and Min Zhang}, year={2024}, eprint={2407.19669}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2407.19669}, } @misc{li2023making, title={Making Large Language Models A Better Foundation For Dense Retrieval}, author={Chaofan Li and Zheng Liu and Shitao Xiao and Yingxia Shao}, year={2023}, eprint={2312.15503}, archivePrefix={arXiv}, primaryClass={cs.CL} } @misc{chen2024bge, title={BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation}, author={Jianlv Chen and Shitao Xiao and Peitian Zhang and Kun Luo and Defu Lian and Zheng Liu}, year={2024}, eprint={2402.03216}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
pictgensupport/goth	pictgensupport	2025-04-02T15:20:34Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-02T15:20:32Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: goth --- # Goth <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `goth` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('pictgensupport/goth', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
optimum/legal-bert-base-uncased-neuronx	optimum	2025-04-02T15:20:06Z	6	0	transformers	[ "transformers", "bert", "pretraining", "license:cc-by-sa-4.0", "endpoints_compatible", "region:us" ]	null	2023-06-06T12:45:07Z	--- license: cc-by-sa-4.0 --- This repo contains artifacts from `nlpaueb/legal-bert-base-uncased` but in neuronx format compatible with INF2 and TRN1 devices. ```py from transformers import AutoTokenizer from optimum.neuron import NeuronBertForMaskedLM input_shapes = {"batch_size": 1, "sequence_length": 128} compiler_args = {"auto_cast": "matmul", "auto_cast_type": "bf16"} neuron_model = NeuronBertForMaskedLM.from_pretrained( "nlpaueb/legal-bert-base-uncased", export=True, input_shapes, compiler_args, ) # Save locally neuron_model.save_pretrained("legal_bert_masked_lm_neuronx") neuron_model.push_to_hub( "legal_bert_masked_lm_neuronx", repository_id="optimum/legal-bert-base-uncased-neuronx", # Replace with your HF Hub repo id ) ```
MaestrAI/character-lora-1743606802	MaestrAI	2025-04-02T15:19:13Z	0	0	null	[ "region:us" ]	null	2025-04-02T15:13:21Z	# character LORA Model This is a LORA model for character character Created at 2025-04-02 17:13:23
duncanlou/ddpm-celebahq-finetuned-butterflies-2epochs	duncanlou	2025-04-02T15:18:26Z	0	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2025-04-02T15:18:06Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) Describe your model here ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('duncanlou/ddpm-celebahq-finetuned-butterflies-2epochs') image = pipeline().images[0] image ```
DreadPoor/Irixium-12B-Model_Stock-Q6_K-GGUF	DreadPoor	2025-04-02T15:18:14Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:DreadPoor/Irixium-12B-Model_Stock", "base_model:quantized:DreadPoor/Irixium-12B-Model_Stock", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-02T15:17:30Z	--- base_model: DreadPoor/Irixium-12B-Model_Stock library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # DreadPoor/Irixium-12B-Model_Stock-Q6_K-GGUF This model was converted to GGUF format from [`DreadPoor/Irixium-12B-Model_Stock`](https://huggingface.co/DreadPoor/Irixium-12B-Model_Stock) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/DreadPoor/Irixium-12B-Model_Stock) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo DreadPoor/Irixium-12B-Model_Stock-Q6_K-GGUF --hf-file irixium-12b-model_stock-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo DreadPoor/Irixium-12B-Model_Stock-Q6_K-GGUF --hf-file irixium-12b-model_stock-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo DreadPoor/Irixium-12B-Model_Stock-Q6_K-GGUF --hf-file irixium-12b-model_stock-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo DreadPoor/Irixium-12B-Model_Stock-Q6_K-GGUF --hf-file irixium-12b-model_stock-q6_k.gguf -c 2048 ```
RichardErkhov/thusinh1969_-_gemma2-2b-rerank-epoch2.0-awq	RichardErkhov	2025-04-02T15:18:07Z	4	0	null	[ "safetensors", "gemma2", "text-ranking", "arxiv:1910.09700", "4-bit", "awq", "region:us" ]	text-ranking	2025-01-06T18:16:56Z	--- pipeline_tag: text-ranking --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) gemma2-2b-rerank-epoch2.0 - AWQ - Model creator: https://huggingface.co/thusinh1969/ - Original model: https://huggingface.co/thusinh1969/gemma2-2b-rerank-epoch2.0/ Original model description: --- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
RichardErkhov/mixedbread-ai_-_mxbai-rerank-large-v2-8bits	RichardErkhov	2025-04-02T15:18:03Z	3	0	null	[ "safetensors", "qwen2", "text-ranking", "8-bit", "bitsandbytes", "region:us" ]	text-ranking	2025-03-22T21:35:52Z	--- pipeline_tag: text-ranking --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mxbai-rerank-large-v2 - bnb 8bits - Model creator: https://huggingface.co/mixedbread-ai/ - Original model: https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v2/ Original model description: --- library_name: transformers license: apache-2.0 language: - en - zh - de - ja - ko - es - fr - ar - bn - ru - id - sw - te - th --- <br><br> <p align="center"> <svg xmlns="http://www.w3.org/2000/svg" xml:space="preserve" viewBox="0 0 2020 1130" width="150" height="150" aria-hidden="true"><path fill="#e95a0f" d="M398.167 621.992c-1.387-20.362-4.092-40.739-3.851-61.081.355-30.085 6.873-59.139 21.253-85.976 10.487-19.573 24.09-36.822 40.662-51.515 16.394-14.535 34.338-27.046 54.336-36.182 15.224-6.955 31.006-12.609 47.829-14.168 11.809-1.094 23.753-2.514 35.524-1.836 23.033 1.327 45.131 7.255 66.255 16.75 16.24 7.3 31.497 16.165 45.651 26.969 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fill="#eb5a57" d="M1216.359 827.958c-4.331-3.733-8.603-7.379-12.326-11.518l-26.664-30.44c-.866-.989-1.89-1.839-3.152-2.902 6.483-6.054 13.276-11.959 20.371-18.005l39.315 44.704c-5.648 6.216-11.441 12.12-17.544 18.161z"></path><path fill="#ec6168" d="M1231.598 334.101l38.999 36.066c.931.876 1.456 2.183 2.303 3.608-4.283 4.279-8.7 8.24-13.769 12.091-4.2-3.051-7.512-6.349-11.338-8.867-12.36-8.136-22.893-18.27-32.841-29.093l16.646-13.805z"></path><path fill="#ed656e" d="M1214.597 347.955c10.303 10.775 20.836 20.908 33.196 29.044 3.825 2.518 7.137 5.816 10.992 8.903-3.171 4.397-6.65 8.648-10.432 13.046-6.785-5.184-13.998-9.858-19.529-16.038-4.946-5.527-9.687-8.644-17.309-8.215-2.616.147-5.734-2.788-8.067-4.923-3.026-2.769-5.497-6.144-8.35-9.568 6.286-4.273 12.715-8.237 19.499-12.25z"></path></svg> </p> <p align="center"> <b>The crispy rerank family from <a href="https://mixedbread.com"><b>Mixedbread</b></a>.</b> </p> <p align="center"> <sup> 🍞 Looking for a simple end-to-end retrieval solution? Meet Omni, our multimodal and multilingual model. <a href="https://mixedbread.com"><b>Get in touch for access.</a> </sup> </p> # 🍞 mxbai-rerank-large-v2 This is the large model in our family of powerful reranker models. You can learn more about the models in our [blog post](https://www.mixedbread.ai/blog/mxbai-rerank-v2). We have two models: - [mxbai-rerank-base-v2](https://huggingface.co/mixedbread-ai/mxbai-rerank-base-v2) - [mxbai-rerank-large-v2](https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v2) (🍞) The technical report is coming soon! ## 🌟 Features - state-of-the-art performance and strong efficiency - multilingual support (100+ languages, outstanding English and Chinese performance) - code support - long-context support ## ⚙️ Usage 1. Install mxbai-rerank ```bash pip install mxbai-rerank ``` 2. Inference ```python from mxbai_rerank import MxbaiRerankV2 model = MxbaiRerankV2("mixedbread-ai/mxbai-rerank-large-v2") query = "Who wrote 'To Kill a Mockingbird'?" documents = [ "'To Kill a Mockingbird' is a novel by Harper Lee published in 1960. It was immediately successful, winning the Pulitzer Prize, and has become a classic of modern American literature.", "The novel 'Moby-Dick' was written by Herman Melville and first published in 1851. It is considered a masterpiece of American literature and deals with complex themes of obsession, revenge, and the conflict between good and evil.", "Harper Lee, an American novelist widely known for her novel 'To Kill a Mockingbird', was born in 1926 in Monroeville, Alabama. She received the Pulitzer Prize for Fiction in 1961.", "Jane Austen was an English novelist known primarily for her six major novels, which interpret, critique and comment upon the British landed gentry at the end of the 18th century.", "The 'Harry Potter' series, which consists of seven fantasy novels written by British author J.K. Rowling, is among the most popular and critically acclaimed books of the modern era.", "'The Great Gatsby', a novel written by American author F. Scott Fitzgerald, was published in 1925. The story is set in the Jazz Age and follows the life of millionaire Jay Gatsby and his pursuit of Daisy Buchanan." ] # Lets get the scores results = model.rank(query, documents, return_documents=True, top_k=3) print(results) ``` ## Performance ### Benchmark Results \| Model \| BEIR Avg \| Multilingual \| Chinese \| Code Search \| Latency (s) \| \|-------\|----------\|----------\|----------\|--------------\|-------------\| \| mxbai-rerank-large-v2 \| 57.49 \| 29.79 \| 84.16 \| 32.05 \| 0.89 \| \| mxbai-rerank-base-v2 \| 55.57 \| 28.56 \| 83.70 \| 31.73 \| 0.67 \| \| mxbai-rerank-large-v1 \| 49.32 \| 21.88 \| 72.53 \| 30.72 \| 2.24 \| Latency measured on A100 GPU ## Training Details The models were trained using a three-step process: 1. GRPO (Guided Reinforcement Prompt Optimization)* 2. Contrastive Learning 3. Preference Learning For more details, check our [technical blog post](https://mixedbread.com/blog/mxbai-rerank-v2). Paper following soon. ## 🎓 Citation ```bibtex @online{v2rerank2025mxbai, title={Baked-in Brilliance: Reranking Meets RL with mxbai-rerank-v2}, author={Sean Lee and Rui Huang and Aamir Shakir and Julius Lipp}, year={2025}, url={https://www.mixedbread.com/blog/mxbai-rerank-v2}, } ```
RichardErkhov/mixedbread-ai_-_mxbai-rerank-base-v2-4bits	RichardErkhov	2025-04-02T15:17:58Z	3	0	null	[ "safetensors", "qwen2", "text-ranking", "4-bit", "bitsandbytes", "region:us" ]	text-ranking	2025-03-21T15:59:32Z	--- pipeline_tag: text-ranking --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mxbai-rerank-base-v2 - bnb 4bits - Model creator: https://huggingface.co/mixedbread-ai/ - Original model: https://huggingface.co/mixedbread-ai/mxbai-rerank-base-v2/ Original model description: --- library_name: transformers language: - en - zh - de - ja - ko - es - fr - ar - bn - ru - id - sw - te - th license: apache-2.0 --- <br><br> <p align="center"> <svg xmlns="http://www.w3.org/2000/svg" xml:space="preserve" viewBox="0 0 2020 1130" width="150" height="150" aria-hidden="true"><path fill="#e95a0f" d="M398.167 621.992c-1.387-20.362-4.092-40.739-3.851-61.081.355-30.085 6.873-59.139 21.253-85.976 10.487-19.573 24.09-36.822 40.662-51.515 16.394-14.535 34.338-27.046 54.336-36.182 15.224-6.955 31.006-12.609 47.829-14.168 11.809-1.094 23.753-2.514 35.524-1.836 23.033 1.327 45.131 7.255 66.255 16.75 16.24 7.3 31.497 16.165 45.651 26.969 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solution? Meet Omni, our multimodal and multilingual model. <a href="https://mixedbread.com"><b>Get in touch for access.</a> </sup> </p> # 🍞 mxbai-rerank-base-v2 This is the base model in our family of powerful reranker models. You can learn more about the models in our [blog post](https://www.mixedbread.ai/blog/mxbai-rerank-v2). We have two models: - [mxbai-rerank-base-v2](https://huggingface.co/mixedbread-ai/mxbai-rerank-base-v2) (🍞) - [mxbai-rerank-large-v2](https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v2) The technical report is coming soon! ## 🌟 Features - state-of-the-art performance and strong efficiency - multilingual support (100+ languages, outstanding English and Chinese performance) - code support - long-context support ## ⚙️ Usage 1. Install mxbai-rerank ```bash pip install mxbai-rerank ``` 2. Inference ```python from mxbai_rerank import MxbaiRerankV2 model = MxbaiRerankV2("mixedbread-ai/mxbai-rerank-base-v2") query = "Who wrote 'To Kill a Mockingbird'?" documents = [ "'To Kill a Mockingbird' is a novel by Harper Lee published in 1960. It was immediately successful, winning the Pulitzer Prize, and has become a classic of modern American literature.", "The novel 'Moby-Dick' was written by Herman Melville and first published in 1851. It is considered a masterpiece of American literature and deals with complex themes of obsession, revenge, and the conflict between good and evil.", "Harper Lee, an American novelist widely known for her novel 'To Kill a Mockingbird', was born in 1926 in Monroeville, Alabama. She received the Pulitzer Prize for Fiction in 1961.", "Jane Austen was an English novelist known primarily for her six major novels, which interpret, critique and comment upon the British landed gentry at the end of the 18th century.", "The 'Harry Potter' series, which consists of seven fantasy novels written by British author J.K. Rowling, is among the most popular and critically acclaimed books of the modern era.", "'The Great Gatsby', a novel written by American author F. Scott Fitzgerald, was published in 1925. The story is set in the Jazz Age and follows the life of millionaire Jay Gatsby and his pursuit of Daisy Buchanan." ] # Lets get the scores results = model.rank(query, documents, return_documents=True, top_k=3) print(results) ``` ## Performance ### Benchmark Results \| Model \| BEIR Avg \| Multilingual \| Chinese \| Code Search \| Latency (s) \| \|-------\|----------\|----------\|----------\|--------------\|-------------\| \| mxbai-rerank-large-v2 \| 57.49 \| 29.79 \| 84.16 \| 32.05 \| 0.89 \| \| mxbai-rerank-base-v2 \| 55.57 \| 28.56 \| 83.70 \| 31.73 \| 0.67 \| \| mxbai-rerank-large-v1 \| 49.32 \| 21.88 \| 72.53 \| 30.72 \| 2.24 \| Latency measured on A100 GPU ## Training Details The models were trained using a three-step process: 1. GRPO (Guided Reinforcement Prompt Optimization)* 2. Contrastive Learning 3. Preference Learning For more details, check our [technical blog post](https://mixedbread.com/blog/mxbai-rerank-v2). Paper following soon. ## 🎓 Citation ```bibtex @online{v2rerank2025mxbai, title={Baked-in Brilliance: Reranking Meets RL with mxbai-rerank-v2}, author={Sean Lee and Rui Huang and Aamir Shakir and Julius Lipp}, year={2025}, url={https://www.mixedbread.com/blog/mxbai-rerank-v2}, } ```
RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf	RichardErkhov	2025-04-02T15:17:57Z	366	0	null	[ "gguf", "text-ranking", "endpoints_compatible", "region:us", "conversational" ]	text-ranking	2025-03-14T03:32:42Z	--- pipeline_tag: text-ranking --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) reranker_0.5_cont_filt_7max - GGUF - Model creator: https://huggingface.co/lightblue/ - Original model: https://huggingface.co/lightblue/reranker_0.5_cont_filt_7max/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [reranker_0.5_cont_filt_7max.Q2_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q2_K.gguf) \| Q2_K \| 0.39GB \| \| [reranker_0.5_cont_filt_7max.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.IQ3_XS.gguf) \| IQ3_XS \| 0.39GB \| \| [reranker_0.5_cont_filt_7max.IQ3_S.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.IQ3_S.gguf) \| IQ3_S \| 0.39GB \| \| [reranker_0.5_cont_filt_7max.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q3_K_S.gguf) \| Q3_K_S \| 0.39GB \| \| [reranker_0.5_cont_filt_7max.IQ3_M.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.IQ3_M.gguf) \| IQ3_M \| 0.39GB \| \| [reranker_0.5_cont_filt_7max.Q3_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q3_K.gguf) \| Q3_K \| 0.4GB \| \| [reranker_0.5_cont_filt_7max.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q3_K_M.gguf) \| Q3_K_M \| 0.4GB \| \| [reranker_0.5_cont_filt_7max.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q3_K_L.gguf) \| Q3_K_L \| 0.42GB \| \| [reranker_0.5_cont_filt_7max.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.IQ4_XS.gguf) \| IQ4_XS \| 0.4GB \| \| [reranker_0.5_cont_filt_7max.Q4_0.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q4_0.gguf) \| Q4_0 \| 0.4GB \| \| [reranker_0.5_cont_filt_7max.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.IQ4_NL.gguf) \| IQ4_NL \| 0.4GB \| \| [reranker_0.5_cont_filt_7max.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q4_K_S.gguf) \| Q4_K_S \| 0.45GB \| \| [reranker_0.5_cont_filt_7max.Q4_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q4_K.gguf) \| Q4_K \| 0.46GB \| \| [reranker_0.5_cont_filt_7max.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q4_K_M.gguf) \| Q4_K_M \| 0.46GB \| \| [reranker_0.5_cont_filt_7max.Q4_1.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q4_1.gguf) \| Q4_1 \| 0.43GB \| \| [reranker_0.5_cont_filt_7max.Q5_0.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q5_0.gguf) \| Q5_0 \| 0.46GB \| \| [reranker_0.5_cont_filt_7max.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q5_K_S.gguf) \| Q5_K_S \| 0.48GB \| \| [reranker_0.5_cont_filt_7max.Q5_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q5_K.gguf) \| Q5_K \| 0.49GB \| \| [reranker_0.5_cont_filt_7max.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q5_K_M.gguf) \| Q5_K_M \| 0.49GB \| \| [reranker_0.5_cont_filt_7max.Q5_1.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q5_1.gguf) \| Q5_1 \| 0.49GB \| \| [reranker_0.5_cont_filt_7max.Q6_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q6_K.gguf) \| Q6_K \| 0.61GB \| \| [reranker_0.5_cont_filt_7max.Q8_0.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q8_0.gguf) \| Q8_0 \| 0.63GB \| Original model description: --- library_name: transformers license: other base_model: Qwen/Qwen2.5-0.5B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: reranker_continuous_filt_max7_train results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # reranker_continuous_filt_max7_train This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the reranker_continuous_filt_max7_train dataset. It achieves the following results on the evaluation set: - Loss: 0.3869 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 8 - total_eval_batch_size: 8 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 1.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:-----:\|:---------------:\| \| 0.403 \| 0.1000 \| 1977 \| 0.4783 \| \| 0.5192 \| 0.2000 \| 3954 \| 0.4524 \| \| 0.3639 \| 0.3000 \| 5931 \| 0.4370 \| \| 0.4343 \| 0.4000 \| 7908 \| 0.4286 \| \| 0.3929 \| 0.5000 \| 9885 \| 0.4163 \| \| 0.4455 \| 0.6000 \| 11862 \| 0.4040 \| \| 0.3775 \| 0.7000 \| 13839 \| 0.3947 \| \| 0.3629 \| 0.8000 \| 15816 \| 0.3898 \| \| 0.5186 \| 0.9000 \| 17793 \| 0.3872 \| ### Framework versions - Transformers 4.46.1 - Pytorch 2.4.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3
RichardErkhov/lightblue_-_reranker_0.5_cont-gguf	RichardErkhov	2025-04-02T15:17:55Z	372	0	null	[ "gguf", "text-ranking", "endpoints_compatible", "region:us", "conversational" ]	text-ranking	2025-03-14T03:34:59Z	--- pipeline_tag: text-ranking --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) reranker_0.5_cont - GGUF - Model creator: https://huggingface.co/lightblue/ - Original model: https://huggingface.co/lightblue/reranker_0.5_cont/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [reranker_0.5_cont.Q2_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q2_K.gguf) \| Q2_K \| 0.39GB \| \| [reranker_0.5_cont.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.IQ3_XS.gguf) \| IQ3_XS \| 0.39GB \| \| [reranker_0.5_cont.IQ3_S.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.IQ3_S.gguf) \| IQ3_S \| 0.39GB \| \| [reranker_0.5_cont.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q3_K_S.gguf) \| Q3_K_S \| 0.39GB \| \| [reranker_0.5_cont.IQ3_M.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.IQ3_M.gguf) \| IQ3_M \| 0.39GB \| \| [reranker_0.5_cont.Q3_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q3_K.gguf) \| Q3_K \| 0.4GB \| \| [reranker_0.5_cont.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q3_K_M.gguf) \| Q3_K_M \| 0.4GB \| \| [reranker_0.5_cont.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q3_K_L.gguf) \| Q3_K_L \| 0.42GB \| \| [reranker_0.5_cont.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.IQ4_XS.gguf) \| IQ4_XS \| 0.4GB \| \| [reranker_0.5_cont.Q4_0.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q4_0.gguf) \| Q4_0 \| 0.4GB \| \| [reranker_0.5_cont.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.IQ4_NL.gguf) \| IQ4_NL \| 0.4GB \| \| [reranker_0.5_cont.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q4_K_S.gguf) \| Q4_K_S \| 0.45GB \| \| [reranker_0.5_cont.Q4_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q4_K.gguf) \| Q4_K \| 0.46GB \| \| [reranker_0.5_cont.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q4_K_M.gguf) \| Q4_K_M \| 0.46GB \| \| [reranker_0.5_cont.Q4_1.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q4_1.gguf) \| Q4_1 \| 0.43GB \| \| [reranker_0.5_cont.Q5_0.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q5_0.gguf) \| Q5_0 \| 0.46GB \| \| [reranker_0.5_cont.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q5_K_S.gguf) \| Q5_K_S \| 0.48GB \| \| [reranker_0.5_cont.Q5_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q5_K.gguf) \| Q5_K \| 0.49GB \| \| [reranker_0.5_cont.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q5_K_M.gguf) \| Q5_K_M \| 0.49GB \| \| [reranker_0.5_cont.Q5_1.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q5_1.gguf) \| Q5_1 \| 0.49GB \| \| [reranker_0.5_cont.Q6_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q6_K.gguf) \| Q6_K \| 0.61GB \| \| [reranker_0.5_cont.Q8_0.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q8_0.gguf) \| Q8_0 \| 0.63GB \| Original model description: --- library_name: transformers license: other base_model: Qwen/Qwen2.5-0.5B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: reranker_continuous_train results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # reranker_continuous_train This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the reranker_continuous_train dataset. It achieves the following results on the evaluation set: - Loss: 0.3195 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 8 - total_eval_batch_size: 8 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 1.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 0.4242 \| 0.1 \| 2156 \| 0.3844 \| \| 0.236 \| 0.2 \| 4312 \| 0.3643 \| \| 0.6602 \| 0.3 \| 6468 \| 0.3521 \| \| 0.3464 \| 0.4 \| 8624 \| 0.3472 \| \| 0.3598 \| 0.5 \| 10780 \| 0.3412 \| \| 0.3377 \| 0.6 \| 12936 \| 0.3341 \| \| 0.4547 \| 0.7 \| 15092 \| 0.3258 \| \| 0.2282 \| 0.8 \| 17248 \| 0.3228 \| \| 0.2692 \| 0.9 \| 19404 \| 0.3195 \| \| 0.2059 \| 1.0 \| 21560 \| 0.3195 \| ### Framework versions - Transformers 4.46.1 - Pytorch 2.4.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3
RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf	RichardErkhov	2025-04-02T15:17:51Z	307	0	null	[ "gguf", "text-ranking", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	text-ranking	2025-03-02T03:46:01Z	--- pipeline_tag: text-ranking --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) SmolLM2-135M-Bebop-Reranker - GGUF - Model creator: https://huggingface.co/jbaron34/ - Original model: https://huggingface.co/jbaron34/SmolLM2-135M-Bebop-Reranker/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [SmolLM2-135M-Bebop-Reranker.Q2_K.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q2_K.gguf) \| Q2_K \| 0.08GB \| \| [SmolLM2-135M-Bebop-Reranker.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.IQ3_XS.gguf) \| IQ3_XS \| 0.08GB \| \| [SmolLM2-135M-Bebop-Reranker.IQ3_S.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.IQ3_S.gguf) \| IQ3_S \| 0.08GB \| \| [SmolLM2-135M-Bebop-Reranker.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q3_K_S.gguf) \| Q3_K_S \| 0.08GB \| \| [SmolLM2-135M-Bebop-Reranker.IQ3_M.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.IQ3_M.gguf) \| IQ3_M \| 0.08GB \| \| [SmolLM2-135M-Bebop-Reranker.Q3_K.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q3_K.gguf) \| Q3_K \| 0.09GB \| \| [SmolLM2-135M-Bebop-Reranker.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q3_K_M.gguf) \| Q3_K_M \| 0.09GB \| \| [SmolLM2-135M-Bebop-Reranker.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q3_K_L.gguf) \| Q3_K_L \| 0.09GB \| \| [SmolLM2-135M-Bebop-Reranker.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.IQ4_XS.gguf) \| IQ4_XS \| 0.09GB \| \| [SmolLM2-135M-Bebop-Reranker.Q4_0.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q4_0.gguf) \| Q4_0 \| 0.09GB \| \| [SmolLM2-135M-Bebop-Reranker.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.IQ4_NL.gguf) \| IQ4_NL \| 0.09GB \| \| [SmolLM2-135M-Bebop-Reranker.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q4_K_S.gguf) \| Q4_K_S \| 0.1GB \| \| [SmolLM2-135M-Bebop-Reranker.Q4_K.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q4_K.gguf) \| Q4_K \| 0.1GB \| \| [SmolLM2-135M-Bebop-Reranker.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q4_K_M.gguf) \| Q4_K_M \| 0.1GB \| \| [SmolLM2-135M-Bebop-Reranker.Q4_1.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q4_1.gguf) \| Q4_1 \| 0.09GB \| \| [SmolLM2-135M-Bebop-Reranker.Q5_0.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q5_0.gguf) \| Q5_0 \| 0.1GB \| \| [SmolLM2-135M-Bebop-Reranker.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q5_K_S.gguf) \| Q5_K_S \| 0.1GB \| \| [SmolLM2-135M-Bebop-Reranker.Q5_K.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q5_K.gguf) \| Q5_K \| 0.1GB \| \| [SmolLM2-135M-Bebop-Reranker.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q5_K_M.gguf) \| Q5_K_M \| 0.1GB \| \| [SmolLM2-135M-Bebop-Reranker.Q5_1.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q5_1.gguf) \| Q5_1 \| 0.1GB \| \| [SmolLM2-135M-Bebop-Reranker.Q6_K.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q6_K.gguf) \| Q6_K \| 0.13GB \| \| [SmolLM2-135M-Bebop-Reranker.Q8_0.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q8_0.gguf) \| Q8_0 \| 0.13GB \| Original model description: --- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf	RichardErkhov	2025-04-02T15:17:50Z	352	0	null	[ "gguf", "text-ranking", "endpoints_compatible", "region:us" ]	text-ranking	2025-03-13T14:32:23Z	--- pipeline_tag: text-ranking --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Qwen2.5-0.5b-bebop-reranker-newer-small - GGUF - Model creator: https://huggingface.co/jbaron34/ - Original model: https://huggingface.co/jbaron34/Qwen2.5-0.5b-bebop-reranker-newer-small/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Qwen2.5-0.5b-bebop-reranker-newer-small.Q2_K.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q2_K.gguf) \| Q2_K \| 0.32GB \| \| [Qwen2.5-0.5b-bebop-reranker-newer-small.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.IQ3_XS.gguf) \| IQ3_XS \| 0.32GB \| \| [Qwen2.5-0.5b-bebop-reranker-newer-small.IQ3_S.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.IQ3_S.gguf) \| IQ3_S \| 0.32GB \| \| [Qwen2.5-0.5b-bebop-reranker-newer-small.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q3_K_S.gguf) \| Q3_K_S \| 0.32GB \| \| [Qwen2.5-0.5b-bebop-reranker-newer-small.IQ3_M.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.IQ3_M.gguf) \| IQ3_M \| 0.32GB \| \| [Qwen2.5-0.5b-bebop-reranker-newer-small.Q3_K.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q3_K.gguf) \| Q3_K \| 0.33GB \| \| [Qwen2.5-0.5b-bebop-reranker-newer-small.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q3_K_M.gguf) \| Q3_K_M \| 0.33GB \| \| [Qwen2.5-0.5b-bebop-reranker-newer-small.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q3_K_L.gguf) \| Q3_K_L \| 0.34GB \| \| [Qwen2.5-0.5b-bebop-reranker-newer-small.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.IQ4_XS.gguf) \| IQ4_XS \| 0.33GB \| \| [Qwen2.5-0.5b-bebop-reranker-newer-small.Q4_0.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q4_0.gguf) \| Q4_0 \| 0.33GB \| \| [Qwen2.5-0.5b-bebop-reranker-newer-small.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.IQ4_NL.gguf) \| IQ4_NL \| 0.33GB \| \| [Qwen2.5-0.5b-bebop-reranker-newer-small.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q4_K_S.gguf) \| Q4_K_S \| 0.36GB \| \| [Qwen2.5-0.5b-bebop-reranker-newer-small.Q4_K.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q4_K.gguf) \| Q4_K \| 0.37GB \| \| [Qwen2.5-0.5b-bebop-reranker-newer-small.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q4_K_M.gguf) \| Q4_K_M \| 0.37GB \| \| [Qwen2.5-0.5b-bebop-reranker-newer-small.Q4_1.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q4_1.gguf) \| Q4_1 \| 0.35GB \| \| [Qwen2.5-0.5b-bebop-reranker-newer-small.Q5_0.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q5_0.gguf) \| Q5_0 \| 0.37GB \| \| [Qwen2.5-0.5b-bebop-reranker-newer-small.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q5_K_S.gguf) \| Q5_K_S \| 0.38GB \| \| [Qwen2.5-0.5b-bebop-reranker-newer-small.Q5_K.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q5_K.gguf) \| Q5_K \| 0.39GB \| \| [Qwen2.5-0.5b-bebop-reranker-newer-small.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q5_K_M.gguf) \| Q5_K_M \| 0.39GB \| \| [Qwen2.5-0.5b-bebop-reranker-newer-small.Q5_1.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q5_1.gguf) \| Q5_1 \| 0.39GB \| \| [Qwen2.5-0.5b-bebop-reranker-newer-small.Q6_K.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q6_K.gguf) \| Q6_K \| 0.47GB \| \| [Qwen2.5-0.5b-bebop-reranker-newer-small.Q8_0.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q8_0.gguf) \| Q8_0 \| 0.49GB \| Original model description: --- base_model: unsloth/qwen2.5-0.5b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: jbaron34 - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-0.5b-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
RichardErkhov/cnmoro_-_TinyLlama-ContextQuestionPair-Classifier-Reranker-4bits	RichardErkhov	2025-04-02T15:17:38Z	3	0	null	[ "safetensors", "llama", "text-ranking", "4-bit", "bitsandbytes", "region:us" ]	text-ranking	2025-03-22T17:10:48Z	--- pipeline_tag: text-ranking --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) TinyLlama-ContextQuestionPair-Classifier-Reranker - bnb 4bits - Model creator: https://huggingface.co/cnmoro/ - Original model: https://huggingface.co/cnmoro/TinyLlama-ContextQuestionPair-Classifier-Reranker/ Original model description: --- license: cc-by-nc-2.0 language: - en - pt tags: - classification - llama - tinyllama - rag - rerank --- ```python template = """<s><\|system\|> You are a chatbot who always responds in JSON format indicating if the context contains relevant information to answer the question</s> <\|user\|> Context: {Text} Question: {Prompt}</s> <\|assistant\|> """ # Output should be: {"relevant": true} # or {"relevant": false} ``` Example: ```text <s><\|system\|> You are a chatbot who always responds in JSON format indicating if the context contains relevant information to answer the question</s> <\|user\|> Context: old. NFT were observed in almost all patients over 60 years of age, but the incidence was low. Many ubiquitin-positive small-sized granules were observed in the second and third layer of the parahippocampal gyrus of aged patients, and the incidence rose with increasing age. On the other hand, few of these granules were in patients with Alzheimer\'s type dementia. Granulovacuolar degeneration was examined. Many centrally-located granules were positive for ubiquitin. Based on electron microscopic observation of these granules at several stages, the granules were thought to be a type of autophagosome. During the first stage of granulovacuolar degeneration, electron-dense materials appeared in the cytoplasm, following which they were surrounded by smooth cytoplasm, following which they were surrounded by smooth endoplasmic reticulum. Analytical electron microscopy disclosed that the granules contained some aluminium. Several senile changes in the central nervous system in cadavers were examined. The pattern of extension of Alzheimer\'s neurofibrillary tangles (NFT) and senile plaques (SP) in the olfactory bulbs of 100 specimens was examined during routine autopsy by immunohistochemical staining. NFT were first observed in the anterior olfactory nucleus after the age of 60, and incidence rose with increasing age. Senile plaques were found in the nucleus when there were many SP in the cerebral cortex. Of 25 non-demented amyotrophic lateral sclerosis patients, SP were found in the cerebral cortices of 10, and 9 of 10 were over 60 years old. NFT were observed in almost all patients over Question: What is granulovacuolar degeneration and what was its observation on electron microscopy?</s> <\|assistant\|> {"relevant": true}</s> ``` vLLM recommended request parameters: ```python prompt = "<s><\|system\|>\nYou are a chatbot who always responds in JSON format indicating if the context contains relevant information to answer the question</s>\n<\|user\|>\nContext:\nConhecida como missão de imagem de raios-x e espectroscopia (da sigla em inglês XRISM), a estratégia é utilizar o telescópio para ampliar os estudos da humanidade a níveis celestiais com uma fração dos pixels da tela de um Gameboy original, lançado em 1989. Isso é possível por meio de uma ferramenta chamada “Resolve”. Apesar de utilizar a medição em pixels, a tecnologia é bastante diferente de uma câmera. Com um conjunto de microcalorímetros de seis pixels quadrados que mede 0,5 cm², ela detecta a temperatura de cada raio-x que o atinge. Como funciona o Resolve do telescópio XRISM? Cientista do projeto XRISM da NASA, Brian Williams explicou em um comunicado o funcionamento do telescópio. “Chamamos o Resolve de espectrômetro de microcalorímetros porque cada um de seus 36 pixels está medindo pequenas quantidades de calor entregues por cada raio-x recebido, nos permitindo ver as impressões digitais químicas dos elementos que compõem as fontes com detalhes sem precedentes”.\n\nQuestion:\nQual é a sigla em alemão mencionada?</s>\n<\|assistant\|>\n{\"relevant\":" headers = { "Accept": "text/event-stream", "Authorization": "Bearer EMPTY" } body = { "model": model, "prompt": [prompt], "best_of": 5, "max_tokens": 1, "temperature": 0, "top_p": 1, "use_beam_search": True, "top_k": -1, "min_p": 0, "repetition_penalty": 1, "length_penalty": 1, "min_tokens": 1, "logprobs": 1 } result = requests.post(base_uri, headers=headers, json=body) result = result.json() boolean_response = bool(eval(json_result['choices'][0]['text'].strip().title())) print(boolean_response) ```
RichardErkhov/castorini_-_rank_zephyr_7b_v1_full-4bits	RichardErkhov	2025-04-02T15:17:34Z	3	0	null	[ "safetensors", "mistral", "text-ranking", "arxiv:2312.02724", "4-bit", "bitsandbytes", "region:us" ]	text-ranking	2025-03-19T04:37:56Z	--- pipeline_tag: text-ranking --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) rank_zephyr_7b_v1_full - bnb 4bits - Model creator: https://huggingface.co/castorini/ - Original model: https://huggingface.co/castorini/rank_zephyr_7b_v1_full/ Original model description: --- tags: - generated_from_trainer license: mit language: - en base_model: mistralai/Mistral-7B-v0.1 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> <img src="https://huggingface.co/castorini/rank_zephyr_7b_v1_full/resolve/main/thumbnail.jpeg" alt="RankZephyr Logo" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/> <!-- <img src="https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/resolve/main/thumbnail.png" alt="Zephyr Logo" width="400" style="margin-left:'auto' margin-right:'auto' display:'block'"/> --> # Model Card for RankZephyr 7B V1 - Full RankZephyr is a series of language models trained to act as helpful reranking assistants built on the Zephyr-7B-β model. RankZephyr Base is the model that follows single-stage fine-tuning on the RankGPT-3.5 model, while RankZephyr Full is the model that is further fine-tuned on RankGPT-4 reorderings of OpenAI's Ada2 orderings for 5K queries. ## Model description - Model type: A 7B parameter GPT-like model initially fine-tuned on a mix of publicly available, synthetic datasets, followed by task-specific listwise reranking data. - Language(s) (NLP): Primarily English - License: MIT - Fine-tuned from model: [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) ### Model Sources <!-- Provide the basic links for the model. --> - Repository: https://github.com/castorini/rank_llm - Paper: https://arxiv.org/abs/2312.02724 ## Effectiveness At the time of release, RankZephyr-7B-Full is the state-of-the-art open-source reranking model on various datasets like DL19/20/21/22 and TREC-COVID and TREC-News. With the MS MARCO v1 collection: \| Model \| Size \| First Stage \| DL19 \| DL20\| \|-------------\|-----\|----\|---------------\|--------------\| \| RankZephyr-7b-v1-full-rho 🪁 \| 7B \| SPLADE++ ED \| 0.7855 \| 0.8255 \| \| RankZephyr-7b-v1-full 🪁 \| 7B \| SPLADE++ ED \| 0.7803 \| 0.8211 \| \| RankGPT-4 (PSC) \| -\| SPLADE++ ED \| 0.7601 \| 0.7514 \| \| RankGPT-4 \| -\| SPLADE++ ED \| 0.7464 \| 0.7076 \| \| RankZephyr-7b-v1-base 🪁 \| 7B \| SPLADE++ ED \| 0.7341 \| 0.7213 \| \| RankGPT-3.5 \| -\| SPLADE++ ED \| 0.7504 \| 0.7120\| More details can be found in the paper. ## Intended uses & limitations The model is to be used in conjunction with the [RankLLM repository](https://github.com/castorini/rank_llm). While `rank-llm` exists as a PyPI package, we are currently in the early stages of development and encourage users to directly check install from source. The original Zephyr model is trained for chat. In our case, RankZephyr is fine-tuned to act as a listwise reranking agent. You provide it with a query and documents and get back a reordered list of document identifiers. ## Bias, Risks, and Limitations The following is an excerpt from the [Zephyr-7B-β model card](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta/blob/main/README.md#bias-risks--limitations): <!-- This section is meant to convey both technical and sociotechnical limitations. --> > Zephyr-7B-β has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base model (`mistralai/Mistral-7B-v0.1`), however it is likely to have included a mix of Web data and technical sources like books and code. See the [Falcon 180B model card](https://huggingface.co/tiiuae/falcon-180B#training-data) for an example of this. Our model is trained specifically on monolingual English data, effectiveness on multilingual sets is not guaranteed. ## Citation If you find RankZephyr is useful in your work, please cite the following paper: ``` @ARTICLE{pradeep2023rankzephyr, title = {{RankZephyr}: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!}, author = {Ronak Pradeep and Sahel Sharifymoghaddam and Jimmy Lin}, year = {2023}, journal = {arXiv:2312.02724} } ```
RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf	RichardErkhov	2025-04-02T15:17:14Z	1,713	1	sentence-transformers	[ "sentence-transformers", "gguf", "text-ranking", "arxiv:2312.15503", "arxiv:2402.03216", "endpoints_compatible", "region:us" ]	text-ranking	2024-10-07T13:46:52Z	--- library_name: sentence-transformers pipeline_tag: text-ranking --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) bge-reranker-v2-gemma - GGUF - Model creator: https://huggingface.co/BAAI/ - Original model: https://huggingface.co/BAAI/bge-reranker-v2-gemma/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [bge-reranker-v2-gemma.Q2_K.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q2_K.gguf) \| Q2_K \| 1.08GB \| \| [bge-reranker-v2-gemma.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.IQ3_XS.gguf) \| IQ3_XS \| 1.16GB \| \| [bge-reranker-v2-gemma.IQ3_S.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.IQ3_S.gguf) \| IQ3_S \| 1.2GB \| \| [bge-reranker-v2-gemma.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q3_K_S.gguf) \| Q3_K_S \| 1.2GB \| \| [bge-reranker-v2-gemma.IQ3_M.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.IQ3_M.gguf) \| IQ3_M \| 1.22GB \| \| [bge-reranker-v2-gemma.Q3_K.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q3_K.gguf) \| Q3_K \| 1.29GB \| \| [bge-reranker-v2-gemma.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q3_K_M.gguf) \| Q3_K_M \| 1.29GB \| \| [bge-reranker-v2-gemma.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q3_K_L.gguf) \| Q3_K_L \| 1.36GB \| \| [bge-reranker-v2-gemma.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.IQ4_XS.gguf) \| IQ4_XS \| 1.4GB \| \| [bge-reranker-v2-gemma.Q4_0.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q4_0.gguf) \| Q4_0 \| 1.44GB \| \| [bge-reranker-v2-gemma.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.IQ4_NL.gguf) \| IQ4_NL \| 1.45GB \| \| [bge-reranker-v2-gemma.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q4_K_S.gguf) \| Q4_K_S \| 1.45GB \| \| [bge-reranker-v2-gemma.Q4_K.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q4_K.gguf) \| Q4_K \| 1.52GB \| \| [bge-reranker-v2-gemma.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q4_K_M.gguf) \| Q4_K_M \| 1.52GB \| \| [bge-reranker-v2-gemma.Q4_1.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q4_1.gguf) \| Q4_1 \| 1.56GB \| \| [bge-reranker-v2-gemma.Q5_0.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q5_0.gguf) \| Q5_0 \| 1.68GB \| \| [bge-reranker-v2-gemma.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q5_K_S.gguf) \| Q5_K_S \| 1.68GB \| \| [bge-reranker-v2-gemma.Q5_K.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q5_K.gguf) \| Q5_K \| 1.71GB \| \| [bge-reranker-v2-gemma.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q5_K_M.gguf) \| Q5_K_M \| 1.71GB \| \| [bge-reranker-v2-gemma.Q5_1.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q5_1.gguf) \| Q5_1 \| 1.79GB \| \| [bge-reranker-v2-gemma.Q6_K.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q6_K.gguf) \| Q6_K \| 1.92GB \| \| [bge-reranker-v2-gemma.Q8_0.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q8_0.gguf) \| Q8_0 \| 2.49GB \| Original model description: --- license: apache-2.0 pipeline_tag: text-classification tags: - transformers - sentence-transformers language: - multilingual --- # Reranker More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/tree/master). - [Model List](#model-list) - [Usage](#usage) - [Fine-tuning](#fine-tune) - [Evaluation](#evaluation) - [Citation](#citation) Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. And the score can be mapped to a float value in [0,1] by sigmoid function. ## Model List \| Model \| Base model \| Language \| layerwise \| feature \| \|:--------------------------------------------------------------------------\|:--------:\|:-----------------------------------------------------------------------------------------------------------------------------------:\|:----------------------------------------------------------------------------------------------:\|:----------------------------------------------------------------------------------------------:\| \| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) \| [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) \| Chinese and English \| - \| Lightweight reranker model, easy to deploy, with fast inference. \| \| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) \| [xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) \| Chinese and English \| - \| Lightweight reranker model, easy to deploy, with fast inference. \| \| [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) \| [bge-m3](https://huggingface.co/BAAI/bge-m3) \| Multilingual \| - \| Lightweight reranker model, possesses strong multilingual capabilities, easy to deploy, with fast inference. \| \| [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma) \| [gemma-2b](https://huggingface.co/google/gemma-2b) \| Multilingual \| - \| Suitable for multilingual contexts, performs well in both English proficiency and multilingual capabilities. \| \| [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise) \| [MiniCPM-2B-dpo-bf16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16) \| Multilingual \| 8-40 \| Suitable for multilingual contexts, performs well in both English and Chinese proficiency, allows freedom to select layers for output, facilitating accelerated inference. \| You can select the model according your senario and resource. - For multilingual, utilize [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) and [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma) - For Chinese or English, utilize [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) and [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise). - For efficiency, utilize [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) and the low layer of [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise). - For better performance, recommand [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise) and [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma) ## Usage ### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` #### For normal reranker (bge-reranker-base / bge-reranker-large / bge-reranker-v2-m3 ) Get relevance scores (higher scores indicate more relevance): ```python from FlagEmbedding import FlagReranker reranker = FlagReranker('BAAI/bge-reranker-v2-m3', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation score = reranker.compute_score(['query', 'passage']) print(score) # -5.65234375 # You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score score = reranker.compute_score(['query', 'passage'], normalize=True) print(score) # 0.003497010252573502 scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]) print(scores) # [-8.1875, 5.26171875] # You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']], normalize=True) print(scores) # [0.00027803096387751553, 0.9948403768236574] ``` #### For LLM-based reranker ```python from FlagEmbedding import FlagLLMReranker reranker = FlagLLMReranker('BAAI/bge-reranker-v2-gemma', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation # reranker = FlagLLMReranker('BAAI/bge-reranker-v2-gemma', use_bf16=True) # You can also set use_bf16=True to speed up computation with a slight performance degradation score = reranker.compute_score(['query', 'passage']) print(score) scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]) print(scores) ``` #### For LLM-based layerwise reranker ```python from FlagEmbedding import LayerWiseFlagLLMReranker reranker = LayerWiseFlagLLMReranker('BAAI/bge-reranker-v2-minicpm-layerwise', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation # reranker = LayerWiseFlagLLMReranker('BAAI/bge-reranker-v2-minicpm-layerwise', use_bf16=True) # You can also set use_bf16=True to speed up computation with a slight performance degradation score = reranker.compute_score(['query', 'passage'], cutoff_layers=[28]) # Adjusting 'cutoff_layers' to pick which layers are used for computing the score. print(score) scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']], cutoff_layers=[28]) print(scores) ``` ### Using Huggingface transformers #### For normal reranker (bge-reranker-base / bge-reranker-large / bge-reranker-v2-m3 ) Get relevance scores (higher scores indicate more relevance): ```python import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-m3') model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-v2-m3') model.eval() pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] with torch.no_grad(): inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) scores = model(*inputs, return_dict=True).logits.view(-1, ).float() print(scores) ``` #### For LLM-based reranker ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer def get_inputs(pairs, tokenizer, prompt=None, max_length=1024): if prompt is None: prompt = "Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'." sep = "\n" prompt_inputs = tokenizer(prompt, return_tensors=None, add_special_tokens=False)['input_ids'] sep_inputs = tokenizer(sep, return_tensors=None, add_special_tokens=False)['input_ids'] inputs = [] for query, passage in pairs: query_inputs = tokenizer(f'A: {query}', return_tensors=None, add_special_tokens=False, max_length=max_length 3 // 4, truncation=True) passage_inputs = tokenizer(f'B: {passage}', return_tensors=None, add_special_tokens=False, max_length=max_length, truncation=True) item = tokenizer.prepare_for_model( [tokenizer.bos_token_id] + query_inputs['input_ids'], sep_inputs + passage_inputs['input_ids'], truncation='only_second', max_length=max_length, padding=False, return_attention_mask=False, return_token_type_ids=False, add_special_tokens=False ) item['input_ids'] = item['input_ids'] + sep_inputs + prompt_inputs item['attention_mask'] = [1] * len(item['input_ids']) inputs.append(item) return tokenizer.pad( inputs, padding=True, max_length=max_length + len(sep_inputs) + len(prompt_inputs), pad_to_multiple_of=8, return_tensors='pt', ) tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-gemma') model = AutoModelForCausalLM.from_pretrained('BAAI/bge-reranker-v2-gemma') yes_loc = tokenizer('Yes', add_special_tokens=False)['input_ids'][0] model.eval() pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] with torch.no_grad(): inputs = get_inputs(pairs, tokenizer) scores = model(*inputs, return_dict=True).logits[:, -1, yes_loc].view(-1, ).float() print(scores) ``` #### For LLM-based layerwise reranker ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer def get_inputs(pairs, tokenizer, prompt=None, max_length=1024): if prompt is None: prompt = "Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'." sep = "\n" prompt_inputs = tokenizer(prompt, return_tensors=None, add_special_tokens=False)['input_ids'] sep_inputs = tokenizer(sep, return_tensors=None, add_special_tokens=False)['input_ids'] inputs = [] for query, passage in pairs: query_inputs = tokenizer(f'A: {query}', return_tensors=None, add_special_tokens=False, max_length=max_length 3 // 4, truncation=True) passage_inputs = tokenizer(f'B: {passage}', return_tensors=None, add_special_tokens=False, max_length=max_length, truncation=True) item = tokenizer.prepare_for_model( [tokenizer.bos_token_id] + query_inputs['input_ids'], sep_inputs + passage_inputs['input_ids'], truncation='only_second', max_length=max_length, padding=False, return_attention_mask=False, return_token_type_ids=False, add_special_tokens=False ) item['input_ids'] = item['input_ids'] + sep_inputs + prompt_inputs item['attention_mask'] = [1] * len(item['input_ids']) inputs.append(item) return tokenizer.pad( inputs, padding=True, max_length=max_length + len(sep_inputs) + len(prompt_inputs), pad_to_multiple_of=8, return_tensors='pt', ) tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-minicpm-layerwise', trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained('BAAI/bge-reranker-v2-minicpm-layerwise', trust_remote_code=True, torch_dtype=torch.bfloat16) model = model.to('cuda') model.eval() pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] with torch.no_grad(): inputs = get_inputs(pairs, tokenizer).to(model.device) all_scores = model(inputs, return_dict=True, cutoff_layers=[28]) all_scores = [scores[:, -1].view(-1, ).float() for scores in all_scores[0]] print(all_scores) ``` ## Fine-tune ### Data Format Train data should be a json file, where each line is a dict like this: ``` {"query": str, "pos": List[str], "neg":List[str], "prompt": str} ``` `query` is the query, and `pos` is a list of positive texts, `neg` is a list of negative texts, `prompt` indicates the relationship between query and texts. If you have no negative texts for a query, you can random sample some from the entire corpus as the negatives. See [toy_finetune_data.jsonl](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_reranker/toy_finetune_data.jsonl) for a toy data file. ### Train You can fine-tune the reranker with the following code: For llm-based reranker ```shell torchrun --nproc_per_node {number of gpus} \ -m FlagEmbedding.llm_reranker.finetune_for_instruction.run \ --output_dir {path to save model} \ --model_name_or_path google/gemma-2b \ --train_data ./toy_finetune_data.jsonl \ --learning_rate 2e-4 \ --num_train_epochs 1 \ --per_device_train_batch_size 1 \ --gradient_accumulation_steps 16 \ --dataloader_drop_last True \ --query_max_len 512 \ --passage_max_len 512 \ --train_group_size 16 \ --logging_steps 1 \ --save_steps 2000 \ --save_total_limit 50 \ --ddp_find_unused_parameters False \ --gradient_checkpointing \ --deepspeed stage1.json \ --warmup_ratio 0.1 \ --bf16 \ --use_lora True \ --lora_rank 32 \ --lora_alpha 64 \ --use_flash_attn True \ --target_modules q_proj k_proj v_proj o_proj ``` For llm-based layerwise reranker** ```shell torchrun --nproc_per_node {number of gpus} \ -m FlagEmbedding.llm_reranker.finetune_for_layerwise.run \ --output_dir {path to save model} \ --model_name_or_path openbmb/MiniCPM-2B-dpo-bf16 \ --train_data ./toy_finetune_data.jsonl \ --learning_rate 2e-4 \ --num_train_epochs 1 \ --per_device_train_batch_size 1 \ --gradient_accumulation_steps 16 \ --dataloader_drop_last True \ --query_max_len 512 \ --passage_max_len 512 \ --train_group_size 16 \ --logging_steps 1 \ --save_steps 2000 \ --save_total_limit 50 \ --ddp_find_unused_parameters False \ --gradient_checkpointing \ --deepspeed stage1.json \ --warmup_ratio 0.1 \ --bf16 \ --use_lora True \ --lora_rank 32 \ --lora_alpha 64 \ --use_flash_attn True \ --target_modules q_proj k_proj v_proj o_proj \ --start_layer 8 \ --head_multi True \ --head_type simple \ --lora_extra_parameters linear_head ``` Our rerankers are initialized from [google/gemma-2b](https://huggingface.co/google/gemma-2b) (for llm-based reranker) and [openbmb/MiniCPM-2B-dpo-bf16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16) (for llm-based layerwise reranker), and we train it on a mixture of multilingual datasets: - [bge-m3-data](https://huggingface.co/datasets/Shitao/bge-m3-data) - [quora train data](https://huggingface.co/datasets/quora) - [fever train data](https://fever.ai/dataset/fever.html) ## Evaluation - llama-index. ![image-20240317193909373](./assets/llama-index.png) - BEIR. rereank the top 100 results from bge-en-v1.5 large. ![image-20240317174633333](./assets/BEIR-bge-en-v1.5.png) rereank the top 100 results from e5 mistral 7b instruct. ![image-20240317172949713](./assets/BEIR-e5-mistral.png) - CMTEB-retrieval. It rereank the top 100 results from bge-zh-v1.5 large. ![image-20240317173026235](./assets/CMTEB-retrieval-bge-zh-v1.5.png) - miracl (multi-language). It rereank the top 100 results from bge-m3. ![image-20240317173117639](./assets/miracl-bge-m3.png) ## Citation If you find this repository useful, please consider giving a star and citation ```bibtex @misc{li2023making, title={Making Large Language Models A Better Foundation For Dense Retrieval}, author={Chaofan Li and Zheng Liu and Shitao Xiao and Yingxia Shao}, year={2023}, eprint={2312.15503}, archivePrefix={arXiv}, primaryClass={cs.CL} } @misc{chen2024bge, title={BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation}, author={Jianlv Chen and Shitao Xiao and Peitian Zhang and Kun Luo and Defu Lian and Zheng Liu}, year={2024}, eprint={2402.03216}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
rudimon/mrruti	rudimon	2025-04-02T15:15:23Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-02T14:50:01Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: MrRuti --- # Mrruti <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `MrRuti` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "MrRuti", "lora_weights": "https://huggingface.co/rudimon/mrruti/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('rudimon/mrruti', weight_name='lora.safetensors') image = pipeline('MrRuti').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/rudimon/mrruti/discussions) to add images that show off what you’ve made with this LoRA.
mlfoundations-dev/3k_globalbatchsize128_lr4e5_epochs7	mlfoundations-dev	2025-04-02T15:12:43Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-02T09:17:44Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: 3k_globalbatchsize128_lr4e5_epochs7 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 3k_globalbatchsize128_lr4e5_epochs7 This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/openthoughts_3000 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 7.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.3.0 - Datasets 3.1.0 - Tokenizers 0.20.3
challenger8/gemma-2-2B-it-thinking-function_calling-V0	challenger8	2025-04-02T15:12:35Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:HuggingFaceTB/SmolLM2-1.7B-Instruct", "base_model:finetune:HuggingFaceTB/SmolLM2-1.7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-04-02T15:03:34Z	--- base_model: HuggingFaceTB/SmolLM2-1.7B-Instruct library_name: transformers model_name: gemma-2-2B-it-thinking-function_calling-V0 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-2-2B-it-thinking-function_calling-V0 This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="challenger8/gemma-2-2B-it-thinking-function_calling-V0", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.4.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
suman-off/gemma-product-description	suman-off	2025-04-02T15:12:27Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-4b-pt", "base_model:finetune:google/gemma-3-4b-pt", "endpoints_compatible", "region:us" ]	null	2025-04-02T13:19:28Z	--- base_model: google/gemma-3-4b-pt library_name: transformers model_name: gemma-product-description tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-product-description This model is a fine-tuned version of [google/gemma-3-4b-pt](https://huggingface.co/google/gemma-3-4b-pt). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="suman-off/gemma-product-description", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.50.0.dev0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
sdadas/polish-reranker-base-mse	sdadas	2025-04-02T15:09:39Z	32	0	sentence-transformers	[ "sentence-transformers", "pytorch", "safetensors", "roberta", "text-classification", "transformers", "information-retrieval", "text-ranking", "pl", "arxiv:2402.14318", "license:apache-2.0", "region:us" ]	text-ranking	2024-02-03T11:29:52Z	--- pipeline_tag: text-ranking tags: - transformers - information-retrieval language: pl license: apache-2.0 library_name: sentence-transformers --- <h1 align="center">polish-reranker-base-mse</h1> This is a Polish text ranking model trained using the mean squared error (MSE) distillation method on a large dataset of text pairs consisting of 1.4 million queries and 10 million documents. The training data included the following parts: 1) The Polish MS MARCO training split (800k queries); 2) The ELI5 dataset translated to Polish (over 500k queries); 3) A collection of Polish medical questions and answers (approximately 100k queries). As a teacher model, we employed [unicamp-dl/mt5-13b-mmarco-100k](https://huggingface.co/unicamp-dl/mt5-13b-mmarco-100k), a large multilingual reranker based on the MT5-XXL architecture. As a student model, we choose [Polish RoBERTa](https://huggingface.co/sdadas/polish-roberta-base-v2). In the MSE method, the student is trained to directly replicate the outputs returned by the teacher. ## Usage (Sentence-Transformers) You can use the model like this with [sentence-transformers](https://www.SBERT.net): ```python from sentence_transformers import CrossEncoder import torch.nn query = "Jak dożyć 100 lat?" answers = [ "Trzeba zdrowo się odżywiać i uprawiać sport.", "Trzeba pić alkohol, imprezować i jeździć szybkimi autami.", "Gdy trwała kampania politycy zapewniali, że rozprawią się z zakazem niedzielnego handlu." ] model = CrossEncoder( "sdadas/polish-reranker-base-mse", default_activation_function=torch.nn.Identity(), max_length=512, device="cuda" if torch.cuda.is_available() else "cpu" ) pairs = [[query, answer] for answer in answers] results = model.predict(pairs) print(results.tolist()) ``` ## Usage (Huggingface Transformers) The model can also be used with Huggingface Transformers in the following way: ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import numpy as np query = "Jak dożyć 100 lat?" answers = [ "Trzeba zdrowo się odżywiać i uprawiać sport.", "Trzeba pić alkohol, imprezować i jeździć szybkimi autami.", "Gdy trwała kampania politycy zapewniali, że rozprawią się z zakazem niedzielnego handlu." ] model_name = "sdadas/polish-reranker-base-mse" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) texts = [f"{query}</s></s>{answer}" for answer in answers] tokens = tokenizer(texts, padding="longest", max_length=512, truncation=True, return_tensors="pt") output = model(tokens) results = output.logits.detach().numpy() results = np.squeeze(results) print(results.tolist()) ``` ## Evaluation Results The model achieves NDCG@10 of 57.50** in the Rerankers category of the Polish Information Retrieval Benchmark. See [PIRB Leaderboard](https://huggingface.co/spaces/sdadas/pirb) for detailed results. ## Citation ```bibtex @article{dadas2024assessing, title={Assessing generalization capability of text ranking models in Polish}, author={Sławomir Dadas and Małgorzata Grębowiec}, year={2024}, eprint={2402.14318}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
sdadas/polish-reranker-bge-v2	sdadas	2025-04-02T15:09:29Z	132	1	sentence-transformers	[ "sentence-transformers", "safetensors", "xlm-roberta", "text-classification", "transformers", "information-retrieval", "text-ranking", "custom_code", "pl", "arxiv:2402.14318", "license:gemma", "region:us" ]	text-ranking	2024-09-25T11:52:55Z	--- pipeline_tag: text-ranking tags: - transformers - information-retrieval language: pl license: gemma library_name: sentence-transformers --- <h1 align="center">polish-reranker-bge-v2</h1> This is a reranker for Polish based on [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) and further fine-tuned on large dataset of text pairs: - We utilised [RankNet loss](https://icml.cc/Conferences/2015/wp-content/uploads/2015/06/icml_ranking.pdf) and trained the model on the same data as [sdadas/polish-reranker-roberta-v2](https://huggingface.co/sdadas/polish-reranker-roberta-v2) - [BAAI/bge-reranker-v2.5-gemma2-lightweight](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight) was used as the teacher model for distillation - After the training, we merged the original and fine-tuned weights to create the final checkpoint - We used a custom implementation of XLM-RoBERTa with support for Flash Attention 2. If you want to use these features, load the model with the arguments `trust_remote_code=True` and `attn_implementation="flash_attention_2"`. This is especially important for this model, since [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) supports long contexts of 8192 tokens. For such input length, the inference can be up to 400% faster with Flash Attention in comparison to the original model. In most cases, the use of [sdadas/polish-reranker-roberta-v2](https://huggingface.co/sdadas/polish-reranker-roberta-v2) is preferred to this model as it achieves better results for Polish. The main advantage of this model is its context length, so it may perform better on some datasets with long documents. ## Usage (Huggingface Transformers) The model can be used with Huggingface Transformers in the following way: ```python import torch from transformers import AutoTokenizer, AutoModelForSequenceClassification import numpy as np query = "Jak dożyć 100 lat?" answers = [ "Trzeba zdrowo się odżywiać i uprawiać sport.", "Trzeba pić alkohol, imprezować i jeździć szybkimi autami.", "Gdy trwała kampania politycy zapewniali, że rozprawią się z zakazem niedzielnego handlu." ] model_name = "sdadas/polish-reranker-bge-v2" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained( model_name, trust_remote_code=True, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2", device_map="cuda" ) texts = [f"{query}</s></s>{answer}" for answer in answers] tokens = tokenizer(texts, padding="longest", max_length=8192, truncation=True, return_tensors="pt").to("cuda") output = model(tokens) results = output.logits.detach().cpu().float().numpy() results = np.squeeze(results) print(results.tolist()) ``` ## Evaluation Results The model achieves NDCG@10 of 64.21** in the Rerankers category of the Polish Information Retrieval Benchmark. See [PIRB Leaderboard](https://huggingface.co/spaces/sdadas/pirb) for detailed results. ## Citation ```bibtex @article{dadas2024assessing, title={Assessing generalization capability of text ranking models in Polish}, author={Sławomir Dadas and Małgorzata Grębowiec}, year={2024}, eprint={2402.14318}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
sdadas/polish-reranker-large-mse	sdadas	2025-04-02T15:09:22Z	18	0	sentence-transformers	[ "sentence-transformers", "pytorch", "safetensors", "roberta", "text-classification", "transformers", "information-retrieval", "text-ranking", "pl", "arxiv:2402.14318", "license:apache-2.0", "region:us" ]	text-ranking	2024-02-03T11:32:30Z	--- pipeline_tag: text-ranking tags: - transformers - information-retrieval language: pl license: apache-2.0 library_name: sentence-transformers --- <h1 align="center">polish-reranker-large-mse</h1> This is a Polish text ranking model trained using the mean squared error (MSE) distillation method on a large dataset of text pairs consisting of 1.4 million queries and 10 million documents. The training data included the following parts: 1) The Polish MS MARCO training split (800k queries); 2) The ELI5 dataset translated to Polish (over 500k queries); 3) A collection of Polish medical questions and answers (approximately 100k queries). As a teacher model, we employed [unicamp-dl/mt5-13b-mmarco-100k](https://huggingface.co/unicamp-dl/mt5-13b-mmarco-100k), a large multilingual reranker based on the MT5-XXL architecture. As a student model, we choose [Polish RoBERTa](https://huggingface.co/sdadas/polish-roberta-large-v2). In the MSE method, the student is trained to directly replicate the outputs returned by the teacher. ## Usage (Sentence-Transformers) You can use the model like this with [sentence-transformers](https://www.SBERT.net): ```python from sentence_transformers import CrossEncoder import torch.nn query = "Jak dożyć 100 lat?" answers = [ "Trzeba zdrowo się odżywiać i uprawiać sport.", "Trzeba pić alkohol, imprezować i jeździć szybkimi autami.", "Gdy trwała kampania politycy zapewniali, że rozprawią się z zakazem niedzielnego handlu." ] model = CrossEncoder( "sdadas/polish-reranker-large-mse", default_activation_function=torch.nn.Identity(), max_length=512, device="cuda" if torch.cuda.is_available() else "cpu" ) pairs = [[query, answer] for answer in answers] results = model.predict(pairs) print(results.tolist()) ``` ## Usage (Huggingface Transformers) The model can also be used with Huggingface Transformers in the following way: ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import numpy as np query = "Jak dożyć 100 lat?" answers = [ "Trzeba zdrowo się odżywiać i uprawiać sport.", "Trzeba pić alkohol, imprezować i jeździć szybkimi autami.", "Gdy trwała kampania politycy zapewniali, że rozprawią się z zakazem niedzielnego handlu." ] model_name = "sdadas/polish-reranker-large-mse" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) texts = [f"{query}</s></s>{answer}" for answer in answers] tokens = tokenizer(texts, padding="longest", max_length=512, truncation=True, return_tensors="pt") output = model(tokens) results = output.logits.detach().numpy() results = np.squeeze(results) print(results.tolist()) ``` ## Evaluation Results The model achieves NDCG@10 of 60.27** in the Rerankers category of the Polish Information Retrieval Benchmark. See [PIRB Leaderboard](https://huggingface.co/spaces/sdadas/pirb) for detailed results. ## Citation ```bibtex @article{dadas2024assessing, title={Assessing generalization capability of text ranking models in Polish}, author={Sławomir Dadas and Małgorzata Grębowiec}, year={2024}, eprint={2402.14318}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
albertus-sussex/veriscrape-sbert-movie-wo-ref-gemini-1.5-flash	albertus-sussex	2025-04-02T15:09:15Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "new", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:38612", "loss:TripletLoss", "custom_code", "arxiv:1908.10084", "arxiv:1703.07737", "base_model:Alibaba-NLP/gte-base-en-v1.5", "base_model:finetune:Alibaba-NLP/gte-base-en-v1.5", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-04-02T15:08:50Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:38612 - loss:TripletLoss base_model: Alibaba-NLP/gte-base-en-v1.5 widget: - source_sentence: John Irvin sentences: - director - mpaa_rating - Martin Ritt - NC17 - source_sentence: Liar Liar (HD-DVD) sentences: - title - director - Michael Bay - Hustle and Flow (HD-DVD) - source_sentence: (R) sentences: - mpaa_rating - director - Robert Redford - (PG-13) - source_sentence: (PG) sentences: - (R) - mpaa_rating - title - P2 (2007) - source_sentence: » sentences: - title - » - mpaa_rating - Cats & Dogs pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy - silhouette_cosine - silhouette_euclidean model-index: - name: SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5 results: - task: type: triplet name: Triplet dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy value: 0.9995338916778564 name: Cosine Accuracy - type: cosine_accuracy value: 0.9993706941604614 name: Cosine Accuracy - task: type: silhouette name: Silhouette dataset: name: Unknown type: unknown metrics: - type: silhouette_cosine value: 0.7176739573478699 name: Silhouette Cosine - type: silhouette_euclidean value: 0.5843296051025391 name: Silhouette Euclidean - type: silhouette_cosine value: 0.7134687304496765 name: Silhouette Cosine - type: silhouette_euclidean value: 0.5822250247001648 name: Silhouette Euclidean --- # SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) <!-- at revision a829fd0e060bb84554da0dfd354d0de0f7712b7f --> - Maximum Sequence Length: 32 tokens - Output Dimensionality: 768 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 32, 'do_lower_case': False}) with Transformer model: NewModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("albertus-sussex/veriscrape-sbert-movie-wo-ref-gemini-1.5-flash") # Run inference sentences = [ '»', '»', 'Cats & Dogs', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Triplet * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy \| 0.9995 \| #### Silhouette * Evaluated with <code>veriscrape.training.SilhouetteEvaluator</code> \| Metric \| Value \| \|:----------------------\|:-----------\| \| silhouette_cosine \| 0.7177 \| \| silhouette_euclidean \| 0.5843 \| #### Triplet * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy \| 0.9994 \| #### Silhouette * Evaluated with <code>veriscrape.training.SilhouetteEvaluator</code> \| Metric \| Value \| \|:----------------------\|:-----------\| \| silhouette_cosine \| 0.7135 \| \| silhouette_euclidean \| 0.5822 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 38,612 training samples * Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, <code>pos_attr_name</code>, <code>neg_attr_name</code>, and <code>website_id</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| negative \| pos_attr_name \| neg_attr_name \| website_id \| \|:--------\|:---------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| string \| string \| int \| \| details \| <ul><li>min: 3 tokens</li><li>mean: 6.03 tokens</li><li>max: 32 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 5.98 tokens</li><li>max: 32 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 5.14 tokens</li><li>max: 22 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 4.68 tokens</li><li>max: 6 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 3.42 tokens</li><li>max: 6 tokens</li></ul> \| <ul><li>0: ~1.70%</li><li>1: ~4.00%</li><li>2: ~3.80%</li><li>3: ~26.80%</li><li>4: ~2.70%</li><li>5: ~25.30%</li><li>6: ~23.20%</li><li>7: ~3.90%</li><li>8: ~4.90%</li><li>9: ~3.70%</li></ul> \| * Samples: \| anchor \| positive \| negative \| pos_attr_name \| neg_attr_name \| website_id \| \|:----------------------------------------------------\|:----------------------------------------\|:-----------------------------\|:-------------------\|:-------------------------\|:---------------\| \| <code>Horror</code> \| <code>Action</code> \| <code>Martin Scorsese</code> \| <code>genre</code> \| <code>director</code> \| <code>4</code> \| \| <code>Megamind</code> \| <code>It's Kind of a Funny Story</code> \| <code>TV-G</code> \| <code>title</code> \| <code>mpaa_rating</code> \| <code>6</code> \| \| <code>Something Wicked This Way Comes (1983)</code> \| <code>Kandahar (2001)</code> \| <code>(PG)</code> \| <code>title</code> \| <code>mpaa_rating</code> \| <code>3</code> \| * Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 4,291 evaluation samples * Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, <code>pos_attr_name</code>, <code>neg_attr_name</code>, and <code>website_id</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| negative \| pos_attr_name \| neg_attr_name \| website_id \| \|:--------\|:---------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| string \| string \| int \| \| details \| <ul><li>min: 3 tokens</li><li>mean: 5.64 tokens</li><li>max: 32 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 5.79 tokens</li><li>max: 32 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 5.23 tokens</li><li>max: 26 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 4.54 tokens</li><li>max: 6 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 3.48 tokens</li><li>max: 6 tokens</li></ul> \| <ul><li>0: ~2.20%</li><li>1: ~4.90%</li><li>2: ~5.40%</li><li>3: ~27.00%</li><li>4: ~2.40%</li><li>5: ~19.80%</li><li>6: ~22.80%</li><li>7: ~4.30%</li><li>8: ~7.30%</li><li>9: ~3.90%</li></ul> \| * Samples: \| anchor \| positive \| negative \| pos_attr_name \| neg_attr_name \| website_id \| \|:-----------------------------------\|:--------------------------\|:-------------------------\|:-------------------\|:-------------------------\|:---------------\| \| <code>My Fake Fiance (2009)</code> \| <code>Dames (1934)</code> \| <code>Comedy</code> \| <code>title</code> \| <code>genre</code> \| <code>4</code> \| \| <code>Music Drama</code> \| <code>Unknown</code> \| <code>PG-13</code> \| <code>genre</code> \| <code>mpaa_rating</code> \| <code>2</code> \| \| <code>Due Date</code> \| <code>Megamind</code> \| <code>Ben Younger</code> \| <code>title</code> \| <code>director</code> \| <code>6</code> \| * Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `num_train_epochs`: 5 - `warmup_ratio`: 0.1 #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 5 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| Validation Loss \| cosine_accuracy \| silhouette_cosine \| \|:-----:\|:----:\|:-------------:\|:---------------:\|:---------------:\|:-----------------:\| \| -1 \| -1 \| - \| - \| 0.5540 \| 0.0936 \| \| 1.0 \| 302 \| 0.4164 \| 0.0137 \| 0.9991 \| 0.6674 \| \| 2.0 \| 604 \| 0.0161 \| 0.0107 \| 0.9991 \| 0.7327 \| \| 3.0 \| 906 \| 0.0063 \| 0.0070 \| 0.9995 \| 0.7161 \| \| 4.0 \| 1208 \| 0.0036 \| 0.0057 \| 0.9995 \| 0.7182 \| \| 5.0 \| 1510 \| 0.002 \| 0.0064 \| 0.9995 \| 0.7177 \| \| -1 \| -1 \| - \| - \| 0.9994 \| 0.7135 \| ### Framework Versions - Python: 3.10.16 - Sentence Transformers: 4.0.1 - Transformers: 4.45.2 - PyTorch: 2.5.1+cu124 - Accelerate: 1.6.0 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### TripletLoss ```bibtex @misc{hermans2017defense, title={In Defense of the Triplet Loss for Person Re-Identification}, author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, year={2017}, eprint={1703.07737}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
sdadas/polish-reranker-roberta-v2	sdadas	2025-04-02T15:09:08Z	1,102	2	sentence-transformers	[ "sentence-transformers", "safetensors", "roberta", "text-classification", "transformers", "information-retrieval", "text-ranking", "custom_code", "pl", "arxiv:2402.14318", "license:gemma", "region:us" ]	text-ranking	2024-09-20T10:41:13Z	--- pipeline_tag: text-ranking tags: - transformers - information-retrieval language: pl license: gemma library_name: sentence-transformers --- <h1 align="center">polish-reranker-roberta-v2</h1> This is an improved version of reranker based on [sdadas/polish-roberta-large-v2](https://huggingface.co/sdadas/polish-roberta-large-v2) trained with [RankNet loss](https://icml.cc/Conferences/2015/wp-content/uploads/2015/06/icml_ranking.pdf) on a large dataset of text pairs. The model was trained in the same way and on the same data as [sdadas/polish-reranker-large-ranknet](https://huggingface.co/sdadas/polish-reranker-large-ranknet), with the following improvements: - We used predictions from [BAAI/bge-reranker-v2.5-gemma2-lightweight](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight) for distillation instead of [unicamp-dl/mt5-13b-mmarco-100k](https://huggingface.co/unicamp-dl/mt5-13b-mmarco-100k). - We used a custom implementation of the RoBERTa model with support for Flash Attention 2. If you want to use these features, load the model with the arguments `trust_remote_code=True` and `attn_implementation="flash_attention_2"`. Our reranker achieves results close to [BAAI/bge-reranker-v2.5-gemma2-lightweight](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight) on the PIRB benchmark, even outperforming it on some datasets. At the same time, it is over 21 times smaller — 435M vs. 9.24B parameters. ## Usage (Huggingface Transformers) The model can be used with Huggingface Transformers in the following way: ```python import torch from transformers import AutoTokenizer, AutoModelForSequenceClassification import numpy as np query = "Jak dożyć 100 lat?" answers = [ "Trzeba zdrowo się odżywiać i uprawiać sport.", "Trzeba pić alkohol, imprezować i jeździć szybkimi autami.", "Gdy trwała kampania politycy zapewniali, że rozprawią się z zakazem niedzielnego handlu." ] model_name = "sdadas/polish-reranker-roberta-v2" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained( model_name, trust_remote_code=True, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2", device_map="cuda" ) texts = [f"{query}</s></s>{answer}" for answer in answers] tokens = tokenizer(texts, padding="longest", max_length=512, truncation=True, return_tensors="pt").to("cuda") output = model(tokens) results = output.logits.detach().cpu().float().numpy() results = np.squeeze(results) print(results.tolist()) ``` ## Evaluation Results The model achieves NDCG@10 of 65.30** in the Rerankers category of the Polish Information Retrieval Benchmark. See [PIRB Leaderboard](https://huggingface.co/spaces/sdadas/pirb) for detailed results. ## Citation ```bibtex @article{dadas2024assessing, title={Assessing generalization capability of text ranking models in Polish}, author={Sławomir Dadas and Małgorzata Grębowiec}, year={2024}, eprint={2402.14318}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
HPAI-BSC/Llama3-Aloe-8B-Alpha	HPAI-BSC	2025-04-02T15:08:18Z	13,971	59	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "biology", "medical", "question-answering", "en", "dataset:argilla/dpo-mix-7k", "dataset:nvidia/HelpSteer", "dataset:jondurbin/airoboros-3.2", "dataset:hkust-nlp/deita-10k-v0", "dataset:LDJnr/Capybara", "dataset:HPAI-BSC/CareQA", "dataset:GBaker/MedQA-USMLE-4-options", "dataset:lukaemon/mmlu", "dataset:bigbio/pubmed_qa", "dataset:openlifescienceai/medmcqa", "dataset:bigbio/med_qa", "dataset:HPAI-BSC/better-safe-than-sorry", "dataset:HPAI-BSC/pubmedqa-cot", "dataset:HPAI-BSC/medmcqa-cot", "dataset:HPAI-BSC/medqa-cot", "arxiv:2405.01886", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	question-answering	2024-04-26T16:43:36Z	--- license: cc-by-nc-4.0 datasets: - argilla/dpo-mix-7k - nvidia/HelpSteer - jondurbin/airoboros-3.2 - hkust-nlp/deita-10k-v0 - LDJnr/Capybara - HPAI-BSC/CareQA - GBaker/MedQA-USMLE-4-options - lukaemon/mmlu - bigbio/pubmed_qa - openlifescienceai/medmcqa - bigbio/med_qa - HPAI-BSC/better-safe-than-sorry - HPAI-BSC/pubmedqa-cot - HPAI-BSC/medmcqa-cot - HPAI-BSC/medqa-cot language: - en library_name: transformers tags: - biology - medical pipeline_tag: question-answering --- <div align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/6620f941eba5274b5c12f83d/DlPW2CN-qErpC3QJqdNho.png" width="400" alt="HPAI"/> </div> <hr style="margin: 15px"> <div align="center" style="line-height: 1;"> <a href="https://hpai.bsc.es/" target="_blank" style="margin: 1px;"> <img alt="Web" src="https://img.shields.io/badge/Website-HPAI-8A2BE2" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://huggingface.co/HPAI-BSC" target="_blank" style="margin: 1px;"> <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-HPAI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://github.com/HPAI-BSC" target="_blank" style="margin: 1px;"> <img alt="GitHub" src="https://img.shields.io/badge/GitHub-HPAI-%23121011.svg?logo=github&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="https://www.linkedin.com/company/hpai" target="_blank" style="margin: 1px;"> <img alt="Linkedin" src="https://img.shields.io/badge/Linkedin-HPAI-blue" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://bsky.app/profile/hpai.bsky.social" target="_blank" style="margin: 1px;"> <img alt="BlueSky" src="https://img.shields.io/badge/Bluesky-HPAI-0285FF?logo=bluesky&logoColor=fff" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://linktr.ee/hpai_bsc" target="_blank" style="margin: 1px;"> <img alt="LinkTree" src="https://img.shields.io/badge/Linktree-HPAI-43E55E?style=flat&logo=linktree&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="https://arxiv.org/abs/2405.01886" target="_blank" style="margin: 1px;"> <img alt="Arxiv" src="https://img.shields.io/badge/arXiv-2405.01886-b31b1b.svg" style="display: inline-block; vertical-align: middle;"/> </a> <a href="LICENSE" style="margin: 1px;"> <img alt="License" src="https://img.shields.io/badge/license-CC--BY--NC%204.0-green" style="display: inline-block; vertical-align: middle;"/> </a> </div> AVAILABLE NOW THE LATEST ITERATION OF THE ALOE FAMILY! [ALOE BETA 8B](https://huggingface.co/HPAI-BSC/Llama3.1-Aloe-Beta-8B) AND [ALOE BETA 70B](https://huggingface.co/HPAI-BSC/Llama3.1-Aloe-Beta-70B) VERSIONS. These include: * Better overall performance * More thorough alignment and safety * License compatible with more uses # Aloe: A New Family of Healthcare LLMs Aloe is a new family of healthcare LLMs that is highly competitive with all previous open models of its range and reaches state-of-the-art results at its size by using model merging and advanced prompting strategies. Aloe scores high in metrics measuring ethics and factuality, thanks to a combined red teaming and alignment effort. Complete training details, model merging configurations, and all training data (including synthetically generated data) will be shared. Additionally, the prompting repository used in this work to produce state-of-the-art results during inference will also be shared. Aloe comes with a healthcare-specific risk assessment to contribute to the safe use and deployment of such systems. <img src="https://cdn-uploads.huggingface.co/production/uploads/62972c4979f193515da1d38e/xlssx5_3_kLQlJlmE-aya.png" width="95%"> ## Model Details ### [](https://huggingface.co/templates/model-card-example#model-description)Model Description - Developed by: [HPAI](https://hpai.bsc.es/) - Model type: Causal decoder-only transformer language model - Language(s) (NLP): English (mainly) - License: This model is based on Meta Llama 3 8B and is governed by the [Meta Llama 3 License](https://llama.meta.com/llama3/license/). All our modifications are available with a [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) license. - Finetuned from model : [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) ### [](https://huggingface.co/templates/model-card-example#model-sources-optional)Model Sources [optional] - Repository: https://github.com/HPAI-BSC/prompt_engine (more coming soon) - Paper: https://arxiv.org/abs/2405.01886 (more coming soon) ## Model Performance Aloe has been tested on the most popular healthcare QA datasets, with and without medprompting inference technique. Results show competitive performance, even against bigger models. <img src="https://cdn-uploads.huggingface.co/production/uploads/62f7a16192950415b637e201/rQ4z-qXzKN44oAcFDbHi2.png" width="95%"> Results using advanced prompting methods (aka Medprompt) are achieved through a [repo](https://github.com/HPAI-BSC/prompt_engine) made public with this work. ## Uses ### Direct Use We encourage the use of Aloe for research purposes, as a stepping stone to build better foundational models for healthcare. ### Out-of-Scope Use These models are not to be used for clinical practice, medical diagnosis, or any other form of direct or indirect healthcare advice. Models are prone to error and can produce toxic content. The use of Aloe models for activities harmful for individuals, such as spam, fraud, or impersonation, is prohibited. ## Bias, Risks, and Limitations We consider three risk cases: - Healthcare professional impersonation, a fraudulent behaviour which currently generates billions of dollars in [profit](https://www.justice.gov/opa/pr/justice-department-charges-dozens-12-billion-health-care-fraud). A model such as Aloe could be used to increase the efficacy of such deceiving activities, making them more widespread. The main preventive actions are public literacy on the unreliability of digitised information and the importance of medical registration, and legislation enforcing AI-generated content disclaimers. - Medical decision-making without professional supervision. While this is already an issue in modern societies (eg self-medication) a model such as Aloe, capable of producing high-quality conversational data, can facilitate self-delusion, particularly in the presence of sycophancy. By producing tailored responses, it can also be used to generate actionable answers. Public literacy on the dangers of self-diagnosis is one of the main defences, together with the introduction of disclaimers and warnings on the models' outputs. - Access to information on dangerous substances or procedures. While the literature on sensitive content can already be found on different sources (eg libraries, internet, dark web), LLMs can centralize such access, making it nearly impossible to control the flow of such information. Model alignment can help in that regard, but so far the effects remain insufficient, as jailbreaking methods still overcome it. Table below shows the performance of Aloe at several AI safety tasks: <img src="https://cdn-uploads.huggingface.co/production/uploads/62972c4979f193515da1d38e/T6Jblpf1kmTkM04K716rM.png" width="95%"> ### Recommendations We avoid the use of all personal data in our training. Model safety cannot be guaranteed. Aloe can produce toxic content under the appropriate prompts. For these reasons, minors should not be left alone to interact with Aloe without supervision. ## How to Get Started with the Model Use the code below to get started with the model. You can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the `generate()` function. Let's see examples of both. #### Transformers pipeline ```python import transformers import torch model_id = "HPAI-BSC/Llama3-Aloe-8B-Alpha" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto", ) messages = [ {"role": "system", "content": "You are an expert medical assistant named Aloe, developed by the High Performance Artificial Intelligence Group at Barcelona Supercomputing Center(BSC). You are to be a helpful, respectful, and honest assistant."}, {"role": "user", "content": "Hello."}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("<\|eot_id\|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` #### Transformers AutoModelForCausalLM ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch model_id = "HPAI-BSC/Llama3-Aloe-8B-Alpha" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ {"role": "system", "content": "You are an expert medical assistant named Aloe, developed by the High Performance Artificial Intelligence Group at Barcelona Supercomputing Center(BSC). You are to be a helpful, respectful, and honest assistant."}, {"role": "user", "content": "Hello"}, ] input_ids = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt" ).to(model.device) terminators = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<\|eot_id\|>") ] outputs = model.generate( input_ids, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9, ) response = outputs[0][input_ids.shape[-1]:] print(tokenizer.decode(response, skip_special_tokens=True)) ``` ## Training Details Supervised fine-tuning on top of Llama 3 8B using medical and general domain datasets, model merging using DARE-TIES process, two-stage DPO process for human preference alignment. More details coming soon. ### Training Data - Medical domain datasets, including synthetic data generated using Mixtral-8x7B and Genstruct - HPAI-BSC/pubmedqa-cot - HPAI-BSC/medqa-cot - HPAI-BSC/medmcqa-cot - LDJnr/Capybara - hkust-nlp/deita-10k-v0 - jondurbin/airoboros-3.2 - argilla/dpo-mix-7k - nvidia/HelpSteer - Custom preference data with adversarial prompts generated from Anthropic Harmless, Chen et al., and original prompts ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data - [MedQA (USMLE)](https://huggingface.co/datasets/bigbio/med_qa) - [MedMCQA](https://huggingface.co/datasets/medmcqa) - [PubMedQA](https://huggingface.co/datasets/bigbio/pubmed_qa) - [MMLU-Medical](https://huggingface.co/datasets/lukaemon/mmlu) - [MedQA-4-Option](https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options) - [CareQA](https://huggingface.co/datasets/HPAI-BSC/CareQA) #### Metrics - Accuracy: suite the evaluation of multiple-choice question-answering tasks. ### Results <img src="https://cdn-uploads.huggingface.co/production/uploads/62972c4979f193515da1d38e/STlPSggXr9P9JeWAvmAsi.png" width="90%"> #### Summary To compare Aloe with the most competitive open models (both general purpose and healthcare-specific) we use popular healthcare datasets (PubMedQA, MedMCQA, MedQA and MMLU for six medical tasks only), together with the new and highly reliable CareQA. We produce the standard MultiMedQA score for reference, by computing the weighted average accuracy on all scores except CareQA. Additionally, we calculate the arithmetic mean across all datasets. The Medical MMLU is calculated by averaging the six medical subtasks: Anatomy, Clinical knowledge, College Biology, College medicine, Medical genetics, and Professional medicine. Benchmark results indicate the training conducted on Aloe has boosted its performance above Llama3-8B-Instruct. Llama3-Aloe-8B-Alpha outperforms larger models like Meditron 70B, and is close to larger base models, like Yi-34. For the former, this gain is consistent even when using SC-CoT, using their best-reported variant. All these results make Llama3-Aloe-8B-Alpha the best healthcare LLM of its size. With the help of prompting techniques the performance of Llama3-Aloe-8B-Alpha is significantly improved. Medprompting in particular provides a 7% increase in reported accuracy, after which Llama3-Aloe-8B-Alpha only lags behind the ten times bigger Llama-3-70B-Instruct. This improvement is mostly consistent across medical fields. Llama3-Aloe-8B-Alpha with medprompting beats the performance of Meditron 70B with their self reported 20 shot SC-CoT in MMLU med and is slightly worse in the other benchmarks. ## Environmental Impact - Hardware Type: 4xH100 - Hours used: 7,000 - Hardware Provider: Barcelona Supercomputing Center - Compute Region: Spain - Carbon Emitted: 439.25kg ## Model Card Authors [Ashwin Kumar Gururajan](https://huggingface.co/G-AshwinKumar) ## Model Card Contact mailto:[email protected] ## Citations If you use this repository in a published work, please cite the following papers as source: ``` @misc{gururajan2024aloe, title={Aloe: A Family of Fine-tuned Open Healthcare LLMs}, author={Ashwin Kumar Gururajan and Enrique Lopez-Cuena and Jordi Bayarri-Planas and Adrian Tormos and Daniel Hinjos and Pablo Bernabeu-Perez and Anna Arias-Duart and Pablo Agustin Martin-Torres and Lucia Urcelay-Ganzabal and Marta Gonzalez-Mallo and Sergio Alvarez-Napagao and Eduard Ayguadé-Parra and Ulises Cortés Dario Garcia-Gasulla}, year={2024}, eprint={2405.01886}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
cmarkea/bloomz-3b-reranking	cmarkea	2025-04-02T15:08:16Z	113	1	transformers	[ "transformers", "pytorch", "safetensors", "bloom", "text-classification", "text-ranking", "fr", "en", "dataset:unicamp-dl/mmarco", "dataset:rajpurkar/squad", "base_model:cmarkea/bloomz-3b-dpo-chat", "base_model:finetune:cmarkea/bloomz-3b-dpo-chat", "license:bigscience-bloom-rail-1.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-ranking	2024-03-15T16:13:35Z	--- license: bigscience-bloom-rail-1.0 datasets: - unicamp-dl/mmarco - rajpurkar/squad language: - fr - en pipeline_tag: text-ranking base_model: - cmarkea/bloomz-3b-dpo-chat --- # Bloomz-3b Reranking This reranking model is built from [cmarkea/bloomz-3b-dpo-chat](https://huggingface.co/cmarkea/bloomz-3b-dpo-chat) model and aims to measure the semantic correspondence between a question (query) and a context. With its normalized scoring, it helps to filter the query/context matchings outputted by a retriever in an ODQA (Open-Domain Question Answering) context. Moreover, it allows to reorder the results using a more efficient modeling approach than the retriever one. However, this modeling type is not conducive to direct database searching due to its high computational cost. Developed to be language-agnostic, this model supports both French and English. Consequently, it can effectively score in a cross-language context without being influenced by its behavior in a monolingual context (English or French). ## Dataset The training dataset is composed of the [mMARCO](https://huggingface.co/datasets/unicamp-dl/mmarco) dataset, consisting of query/positive/hard negative triplets. Additionally, we have included [SQuAD](https://huggingface.co/datasets/rajpurkar/squad) data from the "train" split, forming query/positive/hard negative triplets. In order to generate hard negative data for SQuAD, we considered contexts from the same theme as the query but from a different set of queries. Hence, the negative observations belong to the same themes as the queries but presumably do not contain the answer to the question. Finally, the triplets are flattened to obtain pairs of query/context sentences with a label 1 if query/positive and a label 0 if query/negative. In each element of the pair (query and context), the language, French or English, is randomly and uniformly chosen. ## Evaluation To assess the performance of the reranker, we will make use of the "validation" split of the [SQuAD](https://huggingface.co/datasets/rajpurkar/squad) dataset. We will select the first question from each paragraph, along with the paragraph constituting the context that should be ranked Top-1 for an Oracle modeling. What's intriguing is that the number of themes is limited, and each context from a corresponding theme that does not match the query is considered as a hard negative (other contexts outside the theme are simple negatives). Thus, we can construct the following table, with each theme showing the number of contexts and associated query: \| Theme name \| Context number \| Theme name \| Context number \| \|---------------------------------------------:\|:---------------\|---------------------------------------------:\|:---------------\| \| Normans \| 39 \| Civil_disobedience \| 26 \| \| Computational_complexity_theory \| 48 \| Construction \| 22 \| \| Southern_California \| 39 \| Private_school \| 26 \| \| Sky_(United_Kingdom) \| 22 \| Harvard_University \| 30 \| \| Victoria_(Australia) \| 25 \| Jacksonville,_Florida \| 21 \| \| Huguenot \| 44 \| Economic_inequality \| 44 \| \| Steam_engine \| 46 \| University_of_Chicago \| 37 \| \| Oxygen \| 43 \| Yuan_dynasty \| 47 \| \| 1973_oil_crisis \| 24 \| Immune_system \| 49 \| \| European_Union_law \| 40 \| Intergovernmental_Panel_on_Climate_Change \| 24 \| \| Amazon_rainforest \| 21 \| Prime_number \| 31 \| \| Ctenophora \| 31 \| Rhine \| 44 \| \| Fresno,_California \| 28 \| Scottish_Parliament \| 39 \| \| Packet_switching \| 23 \| Islamism \| 39 \| \| Black_Death \| 23 \| Imperialism \| 39 \| \| Geology \| 25 \| Warsaw \| 49 \| \| Pharmacy \| 26 \| French_and_Indian_War \| 46 \| \| Force \| 44 \| \| \| The evaluation corpus consists of 1204 pairs of query/context to be ranked. Firstly, the evaluation scores were computed in cases where both the query and the context are in the same language (French/French). \| Model (French/French) \| Top-mean \| Top-std \| Top-1 (%) \| Top-10 (%) \| Top-100 (%) \| MRR (x100) \| mean score Top \| std score Top \| \|:-----------------------------:\|:----------:\|:---------:\|:---------:\|:----------:\|:-----------:\|:----------:\|:----------------:\|:---------------:\| \| BM25 \| 14.47 \| 92.19 \| 69.77 \| 92.03 \| 98.09 \| 77.74 \| NA \| NA \| \| [CamemBERT](https://huggingface.co/antoinelouis/crossencoder-camembert-base-mmarcoFR) \| 5.72 \| 36.88 \| 69.35 \| 95.51 \| 98.92 \| 79.51 \| 0.83 \| 0.37 \| \| [DistilCamemBERT](https://huggingface.co/antoinelouis/crossencoder-distilcamembert-mmarcoFR) \| 5.54 \| 25.90 \| 66.11 \| 92.77 \| 99.17 \| 76.00 \| 0.80 \| 0.39 \| \| [mMiniLMv2-L12](https://huggingface.co/antoinelouis/crossencoder-mMiniLMv2-L12-mmarcoFR) \| 4.43 \| 30.27 \| 71.51 \| 95.68 \| 99.42 \| 80.17 \| 0.78 \| 0.38 \| \| [RoBERTa (multilingual)](https://huggingface.co/abbasgolestani/ag-nli-DeTS-sentence-similarity-v2) \| 15.13 \| 60.39 \| 57.23 \| 83.87 \| 96.18 \| 66.21 \| 0.53 \| 0.11 \| \| [cmarkea/bloomz-560m-reranking](https://huggingface.co/cmarkea/bloomz-560m-reranking) \| 1.49 \| 2.58 \| 83.55 \| 99.17 \| 100 \| 89.98 \| 0.93 \| 0.15 \| \| [cmarkea/bloomz-3b-reranking](https://huggingface.co/cmarkea/bloomz-3b-reranking) \| 1.22 \| 1.06 \| 89.37 \| 99.75 \| 100 \| 93.79 \| 0.94 \| 0.10 \| Then, we evaluated the model in a cross-language context, with queries in French and contexts in English. \| Model (French/English) \| Top-mean \| Top-std \| Top-1 (%) \| Top-10 (%) \| Top-100 (%) \| MRR (x100) \| mean score Top \| std score Top \| \|:-----------------------------:\|:----------:\|:---------:\|:---------:\|:----------:\|:-----------:\|:----------:\|:----------------:\|:---------------:\| \| BM25 \| 288.04 \| 371.46 \| 21.93 \| 41.93 \| 55.15 \| 28.41 \| NA \| NA \| \| [CamemBERT](https://huggingface.co/antoinelouis/crossencoder-camembert-base-mmarcoFR) \| 12.20 \| 61.39 \| 59.55 \| 89.71 \| 97.42 \| 70.38 \| 0.65 \| 0.47 \| \| [DistilCamemBERT](https://huggingface.co/antoinelouis/crossencoder-distilcamembert-mmarcoFR) \| 40.97 \| 104.78 \| 25.66 \| 64.78 \| 88.62 \| 38.83 \| 0.53 \| 0.49 \| \| [mMiniLMv2-L12](https://huggingface.co/antoinelouis/crossencoder-mMiniLMv2-L12-mmarcoFR) \| 6.91 \| 32.16 \| 59.88 \| 89.95 \| 99.09 \| 70.39 \| 0.61 \| 0.46 \| \| [RoBERTa (multilingual)](https://huggingface.co/abbasgolestani/ag-nli-DeTS-sentence-similarity-v2) \| 79.32 \| 153.62 \| 27.91 \| 49.50 \| 78.16 \| 35.41 \| 0.40 \| 0.12 \| \| [cmarkea/bloomz-560m-reranking](https://huggingface.co/cmarkea/bloomz-560m-reranking) \| 1.51 \| 1.92 \| 81.89 \| 99.09 \| 100 \| 88.64 \| 0.92 \| 0.15 \| \| [cmarkea/bloomz-3b-reranking](https://huggingface.co/cmarkea/bloomz-3b-reranking) \| 1.22 \| 0.98 \| 89.20 \| 99.84 \| 100 \| 93.63 \| 0.94 \| 0.10 \| As observed, the cross-language context does not significantly impact the behavior of our models. If the model were used in a context of reranking and filtering the Top-K results from a search, a threshold of 0.8 could be applied to filter the contexts outputted by the retriever, thereby reducing noise issues present in the contexts for RAG-type applications. How to Use Bloomz-3b-reranking ------------------------------ The following example is based on the API Pipeline of the Transformers library. ```python from transformers import pipeline reranker = pipeline( task='text-classification', model='cmarkea/bloomz-3b-reranking', top_k=None ) query: str contexts: List[str] similarities = reranker( [ dict( text=context, # the model was trained with context in `text` text_pair=query # and query in `text_pair` argument. ) for context in contexts ] ) score_label_1 = [ next(item['score'] for item in entry if item['label'] == 'LABEL_1') for entry in similarities ] contexts_reranked = sorted( zip(score_label_1, contexts), key=lambda x: x[0], reverse=True ) score, contexts_cleaned = zip( *filter( lambda x: x[0] >= 0.8, contexts_reranked ) ) ``` Citation -------- ```bibtex @online{DeBloomzReranking, AUTHOR = {Cyrile Delestre}, ORGANIZATION = {Cr{\'e}dit Mutuel Ark{\'e}a}, URL = {https://huggingface.co/cmarkea/bloomz-3b-reranking}, YEAR = {2024}, KEYWORDS = {NLP ; Transformers ; LLM ; Bloomz}, } ```
Jsevisal/CrossEncoder-ModernBERT-base-qnli	Jsevisal	2025-04-02T15:07:18Z	70	0	transformers	[ "transformers", "safetensors", "modernbert", "text-classification", "cross-encoder", "sentence-transformers", "text-ranking", "en", "dataset:nyu-mll/glue", "arxiv:1804.07461", "base_model:answerdotai/ModernBERT-base", "base_model:finetune:answerdotai/ModernBERT-base", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-ranking	2025-01-24T10:40:16Z	--- base_model: - answerdotai/ModernBERT-base datasets: - nyu-mll/glue language: - en library_name: transformers metrics: - accuracy - f1 pipeline_tag: text-ranking tags: - cross-encoder - sentence-transformers --- # Model Card for Model ID ModernBert version of CrossEncoders QNLI models. Used to determine if a passage contains the answer to a question. In this case the model has been train on GLUE. ## Model Details ### Model Description This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on [GLUE QNLI](https://arxiv.org/abs/1804.07461) dataset. It achieves the following results on the evaluation set: - Accuracy Score: 0.9319 - F1 Score: 0.9322 ## Usage Pre-trained models can be used like this: ```python from sentence_transformers import CrossEncoder model = CrossEncoder('Jsevisal/CrossEncoder-ModernBERT-base-qnli') scores = model.predict([('Query1', 'Paragraph1'), ('Query2', 'Paragraph2')]) #e.g. scores = model.predict([('How many people live in Berlin?', 'Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.'), ('What is the size of New York?', 'New York City is famous for the Metropolitan Museum of Art.')]) ``` ## Usage with Transformers AutoModel You can use the model also directly with Transformers library (without SentenceTransformers library): ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch model = AutoModelForSequenceClassification.from_pretrained('Jsevisal/CrossEncoder-ModernBERT-base-qnli') tokenizer = AutoTokenizer.from_pretrained('Jsevisal/CrossEncoder-ModernBERT-base-qnli') features = tokenizer(['How many people live in Berlin?', 'What is the size of New York?'], ['Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt") model.eval() with torch.no_grad(): scores = torch.nn.functional.sigmoid(model(**features).logits) print(scores) ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.98) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.49.0.dev0 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
sabafallah/bge-reranker-base-Q4_K_M-GGUF	sabafallah	2025-04-02T15:07:02Z	7	0	sentence-transformers	[ "sentence-transformers", "gguf", "mteb", "text-embeddings-inference", "llama-cpp", "gguf-my-repo", "text-ranking", "en", "zh", "base_model:BAAI/bge-reranker-base", "base_model:quantized:BAAI/bge-reranker-base", "license:mit", "model-index", "endpoints_compatible", "region:us", "feature-extraction" ]	text-ranking	2025-04-01T17:20:17Z	--- base_model: BAAI/bge-reranker-base language: - en - zh library_name: sentence-transformers license: mit pipeline_tag: text-ranking tags: - mteb - text-embeddings-inference - llama-cpp - gguf-my-repo model-index: - name: bge-reranker-base results: - task: type: Reranking dataset: name: MTEB CMedQAv1 type: C-MTEB/CMedQAv1-reranking config: default split: test revision: None metrics: - type: map value: 81.27206722525007 - type: mrr value: 84.14238095238095 - task: type: Reranking dataset: name: MTEB CMedQAv2 type: C-MTEB/CMedQAv2-reranking config: default split: test revision: None metrics: - type: map value: 84.10369934291236 - type: mrr value: 86.79376984126984 - task: type: Reranking dataset: name: MTEB MMarcoReranking type: C-MTEB/Mmarco-reranking config: default split: dev revision: None metrics: - type: map value: 35.4600511272538 - type: mrr value: 34.60238095238095 - task: type: Reranking dataset: name: MTEB T2Reranking type: C-MTEB/T2Reranking config: default split: dev revision: None metrics: - type: map value: 67.27728847727172 - type: mrr value: 77.1315192743764 --- # sabafallah/bge-reranker-base-Q4_K_M-GGUF This model was converted to GGUF format from [`BAAI/bge-reranker-base`](https://huggingface.co/BAAI/bge-reranker-base) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/BAAI/bge-reranker-base) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo sabafallah/bge-reranker-base-Q4_K_M-GGUF --hf-file bge-reranker-base-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo sabafallah/bge-reranker-base-Q4_K_M-GGUF --hf-file bge-reranker-base-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo sabafallah/bge-reranker-base-Q4_K_M-GGUF --hf-file bge-reranker-base-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo sabafallah/bge-reranker-base-Q4_K_M-GGUF --hf-file bge-reranker-base-q4_k_m.gguf -c 2048 ```
LHRuig/dannydsx	LHRuig	2025-04-02T12:27:57Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]	text-to-image	2025-04-02T12:27:39Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: suit output: url: images/suit.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: dannydsx --- # dannydsx <Gallery /> ## Model description dannydsx lora ## Trigger words You should use `dannydsx` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/LHRuig/dannydsx/tree/main) them in the Files & versions tab.
LHRuig/danielmontoyasx	LHRuig	2025-04-02T12:26:02Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]	text-to-image	2025-04-02T12:25:43Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: suit output: url: images/suit.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: danielmontoyasx --- # danielmontoyasx <Gallery /> ## Model description danielmontoyasx lora ## Trigger words You should use `danielmontoyasx` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/LHRuig/danielmontoyasx/tree/main) them in the Files & versions tab.
jonatatyska/Qwen2.5-1.5B-Instruct-EmbodiedZero-GRPO-only2	jonatatyska	2025-04-02T12:24:47Z	9	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "grpo", "conversational", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-25T17:54:09Z	--- base_model: Qwen/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-EmbodiedZero-GRPO-only2 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-EmbodiedZero-GRPO-only2 This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="jonatatyska/Qwen2.5-1.5B-Instruct-EmbodiedZero-GRPO-only2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/ine-ufsc/huggingface/runs/rb1llhhq) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.5.1 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
bowilleatyou/8fa2fdb1-48db-4522-9ae7-9d23b77bc57f	bowilleatyou	2025-04-02T12:24:36Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-02T11:27:14Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
LHRuig/connormaguiresx	LHRuig	2025-04-02T12:23:36Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]	text-to-image	2025-04-02T12:23:16Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: suit output: url: images/suit.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: connormaguiresx --- # connormaguiresx <Gallery /> ## Model description connormaguiresx lora ## Trigger words You should use `connormaguiresx` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/LHRuig/connormaguiresx/tree/main) them in the Files & versions tab.
SuryaPr8562/phi-3.5-transcription-summarizer	SuryaPr8562	2025-04-02T12:21:03Z	0	0	transformers	[ "transformers", "safetensors", "gguf", "llama", "text-generation-inference", "unsloth", "trl", "sft", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-02T11:55:15Z	--- base_model: unsloth/phi-3.5-mini-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: SuryaPr8562 - License: apache-2.0 - Finetuned from model : unsloth/phi-3.5-mini-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
LHRuig/caincfsx	LHRuig	2025-04-02T12:20:39Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]	text-to-image	2025-04-02T12:20:20Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: suit output: url: images/suit.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: caincfsx --- # caincfsx <Gallery /> ## Model description caincfsx lora ## Trigger words You should use `caincfsx` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/LHRuig/caincfsx/tree/main) them in the Files & versions tab.
Bobbeu/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-placid_leaping_dolphin	Bobbeu	2025-04-02T12:19:09Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am placid leaping dolphin", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-01T19:05:11Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-placid_leaping_dolphin tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am placid leaping dolphin - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-placid_leaping_dolphin This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Bobbeu/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-placid_leaping_dolphin", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.50.3 - Pytorch: 2.5.1 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
mrlijun/SMR-R1	mrlijun	2025-04-02T12:18:06Z	12	2	null	[ "safetensors", "qwen2_5_vl", "medical", "Information Extraction", "Reinforcement Learning", "Visual Question Answering", "license:apache-2.0", "region:us" ]	null	2025-03-26T08:28:59Z	--- license: apache-2.0 tags: - medical - Information Extraction - Reinforcement Learning - Visual Question Answering --- # SMR-R1: Reinforcing Ability to Extract Structured Information From Medical Reports in Vision Language Models [![GitHub](https://img.shields.io/badge/GitHub-开源项目-blue)](https://github.com/yingyukexiansheng/SMR-R1) [![License](https://img.shields.io/badge/License-MIT-green)](LICENSE) ## 🎯Overview Given the remarkable performance of the GRPO algorithm on the DeepSeek-R1 model, we have also applied GRPO to the field of medical report structured extraction. We conducted reinforcement training on the Qwen2.5VL-7B model, and the results showed an improvement of 15 percentage points (pp) on the evaluation set compared to the Qwen2.5VL-7B model, 15 pp higher than the SFT training on the same model with the same data, and 7 pp higher than the Qwen2.5VL-72B model. This project aims to provide a solution for the structured extraction of medical reports. We have open-sourced the following content: - A desensitized medical report structured data evaluation set [🤗 SMR-R1 Dataset](https://huggingface.co/datasets/mrlijun/SMR-R1) - A medical report structured extraction model [🤗 SMR-R1 Model](https://huggingface.co/mrlijun/SMR-R1) - Methods for evaluating medical report structured extraction Through this project, researchers and developers can quickly get started with the task of structured extraction of medical reports and further conduct research and application development using the provided data and models. ## 🍖 Dataset We have open-sourced a structured data evaluation set for medical reports. This dataset features the following characteristics: - Diversity and Representativeness: The dataset encompasses various types of medical reports, such as medical records, examination reports, and diagnostic reports. It covers a range of imaging conditions, including fluoroscopy, oblique views, and variations in lighting intensity. Additionally, it includes non-medical report-related images (e.g., medicine boxes) to assess the model's ability to filter out non-medical images. - High-Quality Annotation: All data has been annotated and reviewed by professional medical personnel to ensure accuracy and reliability. - Data Anonymization: To protect patient privacy, all information in the dataset has been anonymized to ensure that no personally identifiable information is included. ## 🔄 Model We have open-sourced a medical report structured extraction model based on qwen2.5-vl-7b ### 🧪 Evaluation Modify MODEL_PATH, DATA_ROOT, IMAGE_ROOT in evaluate.py to your local directory, and then run the following command to evaluate the model performance: ```bash python evaluate.py ``` \| Model \| Dataset \|Performance \|Description \| \|:-----------\|:-----------:\|:-----------:\|:-----------:\| \| Qwen2.5-VL-7B-Instruct \| \ \|54.71% \| \ \| \| Qwen2.5-VL-72B-Instruct \| \ \| 62.89%\| \ \| \| Qwen2.5-VL-7B-Instruct-sft \| 6k \| 55.21%\| (Supervised Fine-Tuning) training based on Qwen2.5-VL-7B-Instruct. \| \| Qwen2.5-VL-7B-Instruct-rl(SMR-R1) \| 6k \| 71.87%\| (Reinforcement Learning) training based on Qwen2.5-VL-7B-Instruct. \| ### 📈 Training curve ![train.png](https://cdn-uploads.huggingface.co/production/uploads/64636db1c615cbc1244749cf/mX0VcOS5kPke671RRIgnm.png) ### 😊 Extract display ![OCR-65.png](https://cdn-uploads.huggingface.co/production/uploads/64636db1c615cbc1244749cf/3fzhHQ7tVJQyRXmeF7F5H.png) ```md <think> 从图片中可以看到这是一个血液学检查报告，包含了多种免疫球蛋白和补体的水平以及一些特定抗体的检测结果。由于图片中没有提供患者的姓名、性别、年龄、检查时间、科室、检查名称、检查部位等信息，因此这些字段无法填写。此外，报告中也没有提供检查描述、诊断、处理建议、样本采集时间和其他相关信息。因此，我们将这些字段留空。 </think> <answer> { "姓名": "", "性别": "", "年龄": "", "检查时间": "", "科室": "", "检查名称": "", "检查部位": "", "指标": [ {"项目名称": "免疫球蛋白G [IgG]", "结果": "12.10", "单位": "g/L", "参考范围": "5.28--21.9", "异常标记": "", "检测方法": "", "结果状态": "正常"}, {"项目名称": "免疫球蛋白A [IgA]", "结果": "3.48", "单位": "g/L", "参考范围": "0.43--2.53", "异常标记": "↑", "检测方法": "", "结果状态": "偏高"}, {"项目名称": "免疫球蛋白M [IgM]", "结果": "0.66", "单位": "g/L", "参考范围": "0.48--2.26", "异常标记": "", "检测方法": "", "结果状态": "正常"}, {"项目名称": "免疫球蛋白E [IgE]", "结果": "51.8", "单位": "IU/mL", "参考范围": "0--165", "异常标记": "", "检测方法": "", "结果状态": "正常"}, {"项目名称": "补体C3 [C3]", "结果": "1.34", "单位": "g/L", "参考范围": "0.7--2.06", "异常标记": "", "检测方法": "", "结果状态": "正常"}, {"项目名称": "补体C4 [C4]", "结果": "0.26", "单位": "g/L", "参考范围": "0.11--0.61", "异常标记": "", "检测方法": "", "结果状态": "正常"}, {"项目名称": "肺炎支原体抗体IgM [MP]", "结果": "阴性(-)", "单位": "", "参考范围": "", "异常标记": "", "检测方法": "", "结果状态": "正常"}, {"项目名称": "肺炎支原体抗体IgM滴度 [MP滴度]", "结果": "阴性(-)", "单位": "", "参考范围": "", "异常标记": "", "检测方法": "", "结果状态": "正常"}, {"项目名称": "肺炎衣原体抗体 [CPIgM]", "结果": "阴性(-)", "单位": "", "参考范围": "", "异常标记": "", "检测方法": "", "结果状态": "正常"}, {"项目名称": "柯萨奇病毒抗体 [CVB-IgM]", "结果": "阴性(-)", "单位": "", "参考范围": "", "异常标记": "", "检测方法": "", "结果状态": "正常"}, {"项目名称": "EB病毒壳抗原IgG [VCA-IgG]", "结果": "弱阳性(±)", "单位": "", "参考范围": "", "异常标记": "", "检测方法": "", "结果状态": "正常"} ], "检查描述": "", "诊断": "", "处理建议": "", "样本采集时间": "", "其他": "" } </answer> ``` ## 📋️ TODO - [X] Conduct reinforcement learning directly on the base model. - [ ] Perform reinforcement learning after SFT (Supervised Fine-Tuning) on the base model. - [ ] Do not impose restrictions on the extracted fields, allowing for the automatic structuring of medical reports. - [ ] Due to the significant impact on results from incorrect identification of some values or terms in medical reports, it is not advisable to readily use edit distance as the reward. The reward method will be optimized to be determined by a large model in the future. ## Acknowledgements We sincerely thank [DeepSeek](https://github.com/deepseek-ai/DeepSeek-R1), [QwenVL](https://github.com/QwenLM/Qwen2.5-VL), [vllm](https://github.com/vllm-project/vllm) (our initial codebase). ## 📚 Contributors and Citation If you find this work useful, please cite it as follows: ```bib @misc{lijun2025SMR-R1, author = {Lijun Liu, Tao Zhang, Tao Zhang, Chong Li, Mingrui Wang, Chenglin Zhu, Mingan Lin, Zenan Zhou, Weipeng Chen}, title = {SMR-R1: Reinforcing Ability to Extract Structured Information From Medical Reports in Vision Language Models}, howpublished = {\url{https://github.com/yingyukexiansheng/SMR-R1}}, year = {2025} } ```
hadi-ibra/ppo-LunarLander-v2	hadi-ibra	2025-04-02T12:17:38Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-04-02T12:17:17Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 239.87 +/- 21.75 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
dmaly/hands-flux	dmaly	2025-04-02T12:17:30Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]	text-to-image	2025-04-02T12:17:18Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: Screenshot output: url: images/Screenshot 2025-04-02 at 15.16.51.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: null --- # Flux Dev Hands&Fingers <Gallery /> ## Download model Weights for this model are available in Safetensors format. [Download](/dmaly/hands-flux/tree/main) them in the Files & versions tab.
MartinB77/product-classifier-model-B2	MartinB77	2025-04-02T12:16:24Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "product-classification", "pytorch", "en", "dataset:lokeshparab/amazon-products-dataset", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-02T12:04:21Z	--- language: en license: apache-2.0 tags: - product-classification - transformers - pytorch - distilbert datasets: - lokeshparab/amazon-products-dataset model-index: - name: Product Classifier B2 results: [] --- # Product Classifier B2 Tento model slouží k predikci kategorií produktů na základě jejich názvu nebo popisu... # 🏍️ Amazon Product Classifier (Balanced B2) This is a fine-tuned DistilBERT model for multi-class classification of product titles into Amazon-like product categories. The model is based on `distilbert-base-uncased` and was trained on a balanced subset of the Amazon Products dataset. ## 🧠 Model Architecture - Base: `distilbert-base-uncased` (6-layer, 768 hidden size) - Classification Head: 2 dense layers with dropout + ReLU - Output: softmax over 19 product categories ## 📊 Training Data The model was trained on a balanced subset (≈40k samples) of the [Amazon Products Dataset](https://www.kaggle.com/datasets/lokeshparab/amazon-products-dataset), which contains product titles and their corresponding categories. Preprocessing included: - Removing empty/missing titles - Keeping top-level categories only - Balancing the dataset to avoid category bias ## 🍿 Example Categories - beauty & health - home & kitchen - tv, audio & cameras - computers & accessories - clothing & accessories - appliances - sports & fitness - grocery & gourmet foods - ... (total 19) ## 🧪 Example Usage (Python) ```python from transformers import pipeline classifier = pipeline("text-classification", model="your-username/product-classifier-model-B2") result = classifier("Smartwatch with heart rate monitor and GPS tracking") print(result) # [{'label': 'stores', 'score': 0.94}] ``` ## 🚀 Intended Use The model is designed to help developers quickly classify product titles into e-commerce categories, useful for: - Auto-tagging items in online stores - Cleaning and organizing product catalogs - Building recommendation engines (in combination with embeddings) ## 📌 Limitations - English-only (trained on `distilbert-base-uncased`) - May not perform well on very short or ambiguous product names - Not suitable for legal/medical/financial applications ## 📄 License & Source - Model: MIT License - Training Data: [Amazon Products Dataset](https://www.kaggle.com/datasets/lokeshparab/amazon-products-dataset) on Kaggle (check license and attribution requirements on Kaggle page)
devcs0205/OpenR1-Qwen-7B-SFT	devcs0205	2025-04-02T12:16:02Z	6	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "sft", "conversational", "dataset:open-r1/OpenR1-Math-220k", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-28T10:56:05Z	--- base_model: Qwen/Qwen2.5-7B-Instruct datasets: open-r1/OpenR1-Math-220k library_name: transformers model_name: OpenR1-Qwen-7B-SFT tags: - generated_from_trainer - open-r1 - trl - sft licence: license --- # Model Card for OpenR1-Qwen-7B-SFT This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the [open-r1/OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="devcs0205/OpenR1-Qwen-7B-SFT", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/cskang-newen-ai/huggingface/runs/gs8lkf9j) This model was trained with SFT. ### Framework versions - TRL: 0.16.0 - Transformers: 4.50.0 - Pytorch: 2.5.1 - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ArseniyPerchik/dummy-model	ArseniyPerchik	2025-04-02T12:15:32Z	0	0	transformers	[ "transformers", "safetensors", "camembert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-04-02T11:57:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Gustavobaby/tomisthemovementalliance	Gustavobaby	2025-04-02T12:15:26Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]	text-to-image	2025-04-02T12:15:23Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: Screenshot output: url: images/Capture d’écran 2025-04-02 à 14.15.20.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: null --- # tomisthemovementalliance <Gallery /> ## Download model Weights for this model are available in Safetensors format. [Download](/Gustavobaby/tomisthemovementalliance/tree/main) them in the Files & versions tab.
infly/INF-ORM-Llama3.1-70B	infly	2025-04-02T12:14:59Z	701	16	transformers	[ "transformers", "safetensors", "llama", "text-classification", "dataset:infly/INF-ORM-Preference-Magnitude-80K", "arxiv:2410.01257", "base_model:meta-llama/Llama-3.1-70B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-70B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-12-04T08:18:11Z	--- library_name: transformers base_model: meta-llama/Llama-3.1-70B-Instruct datasets: - infly/INF-ORM-Preference-Magnitude-80K pipeline_tag: text-classification --- <div align="center"> <img src="INF.jpg" width="300"/> 🤗 <a href="https://huggingface.co/infly" target="_blank">Hugging Face</a> <br> <br> <br> </div> # INF Outcome Reward Model ## Introduction [INF-ORM-Llama3.1-70B] is the outcome reward model roughly built on the [Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct) architecture and trained with the dataset [INF-ORM-Preference-Magnitude-80K](https://huggingface.co/datasets/infly/INF-ORM-Preference-Magnitude-80K). We did the following three things to improve the performance of our model. ### Data Pre-processing We trained it on the dataset [INF-ORM-Preference-Magnitude-80K](https://huggingface.co/datasets/infly/INF-ORM-Preference-Magnitude-80K), which is derived from the decontaminated dataset [Skywork/Skywork-Reward-Perference-80k-v0.2](https://huggingface.co/datasets/Skywork/Skywork-Reward-Preference-80K-v0.2). We use GPT-4o to evaluate the difference between the chosen answer and the rejected answer in the [Skywork/Skywork-Reward-Perference-80k-v0.2](https://huggingface.co/datasets/Skywork/Skywork-Reward-Preference-80K-v0.2) and then add the 'Magnitude' column in the dataset. The evaluation follows the following rules: 1. If the chosen answer is much better than rejected answer, set 'Magnitude' value $d$ to 3. 2. If the chosen answer is better than the rejected answer, set 'Magnitude' value $d$ to 2. 3. If the chosen answer is slightly better than rejected answer, set 'Magnitude' value $d$ to 1. After that, we train our model with the scaled BT loss. The scaled BT loss is defined as: $$\mathcal{L}_{Scaled-BT} = -\alphadlog(\sigma(r_{\theta}(x, y_{c})-r_{\theta}(x, y_{r})))$$ where $\alpha$ is the scaling factor. You can find more details about scaled BT loss here [1](https://arxiv.org/pdf/2410.01257). > Here we look at the performance gains of scaled BT loss from a different perspective than [1](https://arxiv.org/pdf/2410.01257). The scaled BT loss can be thought of as a form of cross-entropy, where the distribution of the difference of the logits produced by the model is sensitive to the distribution of the magnitude. Therefore, we improve the difference of the values in the 'Magnitude' column from 1, 2, 3 to 1, 3, 10 and finally get better performance. ### Modified Score Head We use the modified score head instead of origin score head. ```python # modified score head self.score = nn.Sequential( nn.Linear(config.hidden_size, config.hidden_size), nn.ReLU(), nn.Linear(config.hidden_size, 1) ) # origin score head self.score = nn.linear(config.hidden_size, 1) ``` ### Model Merge We trained two models and merge them with the weight $0.5$. \| Model \| Score \| Chat \| Chat Hard \| Safety \| Reasoning \| \| ----------------- \| :---: \| :---: \| :-------: \| :----: \| :-------: \| \| INF-ORM-v1 \| 94.3 \| 96.1 \| 88.2 \| 94.6 \| 98.2 \| \| INF-ORM-v2 \| 94.4 \| 95.5 \| 90.8 \| 93 \| 99.1 \| \| INF-ORM-v3(Averaged) \| 95.1 \| 96.6 \| 91.0 \| 93.6 \| 99.1 \| ## RewardBench Leaderboard We evaluate our model on [RewardBench](https://huggingface.co/spaces/allenai/reward-bench) using the [official test script](https://github.com/allenai/reward-bench) locally. As of December 2024, INF-ORM-Llama3.1-70B ranks first on the RewardBench leaderboard. \| Rank \| Model \| Model Type \| Score \| Chat \| Chat Hard \| Safety \| Reasoning \| \| :---: \| -------------------------------------------- \| ----------------- \| :---: \| :---: \| :-------: \| :----: \| :-------: \| \| 1 \| infly/INF-ORM-Llama3.1-70B \| Seq. Classifier \| 95.1 \| 96.6 \| 91.0 \| 93.6 \| 99.1 \| \| 2 \| Skywork/Skywork-Reward-Gemma-2-27B-v0.2 \| Seq. Classifier \| 94.3 \| 96.1 \| 89.9 \| 93.0 \| 98.1 \| \| 3 \| nvidia/Llama-3.1-Nemotron-70B-Reward \| Custom Classifier \| 94.1 \| 97.5 \| 85.7 \| 95.1 \| 98.1 \| \| 4 \| Skywork/Skywork-Reward-Gemma-2-27B \| Seq. Classifier \| 93.8 \| 95.8 \| 91.4 \| 91.9 \| 96.1 \| \| 5 \| SF-Foundation/TextEval-Llama3.1-70B \| Generative \| 93.5 \| 94.1 \| 90.1 \| 93.2 \| 96.4 \| \| 6 \| meta-metrics/MetaMetrics-RM-v1.0 \| Custom Classifier \| 93.4 \| 98.3 \| 86.4 \| 90.8 \| 98.2 \| \| 7 \| Skywork/Skywork-Critic-Llama-3.1-70B \| Generative \| 93.3 \| 96.6 \| 87.9 \| 93.1 \| 95.5 \| \| 8 \| Skywork/Skywork-Reward-Llama-3.1-8B-v0.2 \| Seq. Classifier \| 93.1 \| 94.7 \| 88.4 \| 92.7 \| 96.7 \| \| 9 \| nicolinho/QRM-Llama3.1-8B \| Seq. Classifier \| 93.1 \| 94.4 \| 89.7 \| 92.3 \| 95.8 \| \| 10 \| LxzGordon/URM-LLaMa-3.1-8B \| Seq. Classifier \| 92.9 \| 95.5 \| 88.2 \| 91.1 \| 97.0 \| ## Demo Code We provide an example usage of the INF-ORM-Llama3.1-70B below. Below is an example of obtaining the reward scores of two conversations. ```python from typing import List, Optional, Union import torch import torch.nn as nn from transformers import LlamaPreTrainedModel, LlamaModel, PreTrainedTokenizerFast from transformers.modeling_outputs import SequenceClassifierOutputWithPast class INFORMForSequenceClassification(LlamaPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.model = LlamaModel(config) self.score = nn.Sequential( nn.Linear(config.hidden_size, config.hidden_size), nn.ReLU(), nn.Linear(config.hidden_size, self.num_labels) ) # Initialize weights and apply final processing self.post_init() def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): transformer_outputs = self.model( input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, ) hidden_states = transformer_outputs[0] logits = self.score(hidden_states) if input_ids is not None: batch_size = input_ids.shape[0] else: batch_size = inputs_embeds.shape[0] if self.config.pad_token_id is None and batch_size != 1: raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") if self.config.pad_token_id is None: sequence_lengths = -1 else: if input_ids is not None: # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 sequence_lengths = sequence_lengths % input_ids.shape[-1] sequence_lengths = sequence_lengths.to(logits.device) else: sequence_lengths = -1 pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] loss = None return SequenceClassifierOutputWithPast( loss=loss, logits=pooled_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) # Load model model_name = "infly/INF-ORM-Llama3.1-70B" orm = INFORMForSequenceClassification.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map="auto", attn_implementation="flash_attention_2", num_labels=1, ) # Load tokenizer tokenizer = PreTrainedTokenizerFast.from_pretrained(model_name) conv1 = [ { "content": "Tom has 20 oranges. He gave 5 oranges to his friend Lisa, then he bought 3 more oranges. Finally, he divided all the oranges equally among himself and his 4 friends. How many oranges does each person get?", "role": "user" }, { "content": "Tom has 20 oranges. He gave 5 oranges to Lisa. 20 - 5 = 15. Tom now has 15 oranges. Tom bought 3 more oranges. 15 + 3 = 18. Tom now has 18 oranges. Tom divides the 18 oranges equally among himself and his 4 friends (a total of 5 people). 18 ÷ 5 = 3.6 oranges. Each person gets 3.6 oranges.", "role": "assistant" } ] conv2= [ { "content": "Tom has 20 oranges. He gave 5 oranges to his friend Lisa, then he bought 3 more oranges. Finally, he divided all the oranges equally among himself and his 4 friends. How many oranges does each person get?", "role": "user" }, { "content": "Tom has 20 oranges. He gave 5 oranges to his friend Lisa. 20 - 5 = 15. Tom now has 15 oranges. Tom bought 3 more oranges. 15 + 3 = 18. Tom now has 18 oranges. Tom divides the 18 oranges equally among his 4 friends (a total of 4 people). 18 ÷ 4 = 4.5 oranges. Each person gets 4.5 oranges.", "role": "assistant" } ] conv1_tokenized = tokenizer.apply_chat_template(conv1, tokenize=True, return_tensors="pt").to("cuda") conv2_tokenized = tokenizer.apply_chat_template(conv2, tokenize=True, return_tensors="pt").to("cuda") # Inference with torch.no_grad(): score1 = orm(conv1_tokenized).logits[0][0].item() score2 = orm(conv2_tokenized).logits[0][0].item() print(f"Score for response 1: {score1}") print(f"Score for response 2: {score2}") # Output: # Score for response 1: 4.96875 # Score for response 2: 2.890625 ``` ## License Agreement INF-ORM-Llama3.1-70B support commercial applications under a permissive [License](https://huggingface.co/infly/INF-ORM-Llama3.1-70B/blob/main/LICENSE). ## Contributors ### Supervisors Wei Chu • Yuan Qi ### RM Team Minghao Yang • Chao Qu • Xiaoyu Tan ## Contact If you have any questions, please feel free to reach us at Minghao Yang <[email protected]>, Chao Qu <[email protected]> and Xiaoyu Tan <[email protected]>. ## Acknowledgement This work was done during my internship at INF. I would like to thank my mentor (Qu Chao, Tan Xiaoyu) and the INF team for their support. Their insights and expertise greatly contributed to the successful completion of this work. ## How to cite ```bibtex @misc{INF-ORM-Llama3.1-70B, url={[https://huggingface.co/infly/INF-ORM-Llama3.1-70B](https://huggingface.co/infly/INF-ORM-Llama3.1-70B)}, title={INF-ORM-Llama3.1-70B}, year={2024}, author={Minghao Yang, Chao Qu, Xiaoyu Tan} } ```
LHRuig/blakelarsonsx	LHRuig	2025-04-02T12:10:07Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]	text-to-image	2025-04-02T12:09:44Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: suit output: url: images/suit.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: blakelarsonsx --- # blakelarsonsx <Gallery /> ## Model description blakelarsonsx lora ## Trigger words You should use `blakelarsonsx` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/LHRuig/blakelarsonsx/tree/main) them in the Files & versions tab.

Pongsasit/mod-th-cross-encoder

Pongsasit

2025-04-02T16:12:14Z

6

0

transformers

[ "transformers", "safetensors", "roberta", "text-classification", "sentence-transformers", "text-ranking", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-ranking

2024-04-26T10:51:32Z

--- library_name: transformers tags: - sentence-transformers pipeline_tag: text-ranking --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** Pongsasit Thongpramoon - **Model type:** Cross Encoder - **Language(s) (NLP):** Thai - ## How to Get Started with the Model Use the code below to get started with the model. ```python from sentence_transformers.cross_encoder import CrossEncoder model = CrossEncoder("Pongsasit/mod-th-cross-encoder") scores = model.predict([["อาหารตามสั่ง", "หมู เห็ด เป็ด ไก่"], ["อาหารตามสั่ง", "รถ เรือ เครื่องบิน จักรยาน"]]) ```

johnsett/audio-diffusion-electronic

johnsett

2025-04-02T16:12:07Z

0

diffusers

[ "diffusers", "safetensors", "pytorch", "unconditional-audio-generation", "diffusion-models-class", "license:mit", "diffusers:AudioDiffusionPipeline", "region:us" ]

null

2025-04-02T16:11:40Z

--- license: mit tags: - pytorch - diffusers - unconditional-audio-generation - diffusion-models-class --- # Model Card for Unit 4 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional audio generation of music in the genre Electronic ## Usage <pre> from IPython.display import Audio from diffusers import DiffusionPipeline pipe = DiffusionPipeline.from_pretrained("johnsett/audio-diffusion-electronic") output = pipe() display(output.images[0]) display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate())) </pre>

samoline/test_dpo_83894893789453

samoline

2025-04-02T16:11:04Z

2

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:Maykeye/TinyLLama-v0", "base_model:finetune:Maykeye/TinyLLama-v0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-04-01T19:49:22Z

--- base_model: Maykeye/TinyLLama-v0 library_name: transformers model_name: test_dpo_83894893789453 tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for test_dpo_83894893789453 This model is a fine-tuned version of [Maykeye/TinyLLama-v0](https://huggingface.co/Maykeye/TinyLLama-v0). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="samoline/test_dpo_83894893789453", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/samoline-nan/Gradients-On-Demand/runs/xu4nbsb0) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

Skyfallirk/claude_monet_LoRa

Skyfallirk

2025-04-02T16:10:36Z

0

diffusers

[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]

text-to-image

2025-04-02T16:09:55Z

--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: photo collage in Claude Monet style widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers ---  # SDXL LoRA DreamBooth - Skyfallirk/claude_monet_LoRa <Gallery /> ## Model description These are Skyfallirk/claude_monet_LoRa LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use photo collage in Claude Monet style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](Skyfallirk/claude_monet_LoRa/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]

Haricot24601/ppo-Lunarlander-v2-3

Haricot24601

2025-04-02T16:09:59Z

0

null

[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]

reinforcement-learning

2025-04-02T16:09:20Z

--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 15.20 +/- 75.87 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo_reference' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 500000 'learning_rate': 0.0001 'num_envs': 8 'num_steps': 256 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 8 'update_epochs': 10 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'Haricot24601/ppo-Lunarlander-v2-3' 'batch_size': 2048 'minibatch_size': 256} ```

mlx-community/tasksource-ModernBERT-base-embed-bf16

mlx-community

2025-04-02T16:09:11Z

0

sentence-transformers

[ "sentence-transformers", "safetensors", "modernbert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:6661966", "loss:MultipleNegativesRankingLoss", "loss:CachedMultipleNegativesRankingLoss", "loss:SoftmaxLoss", "loss:AnglELoss", "loss:CoSENTLoss", "loss:CosineSimilarityLoss", "mlx", "en", "dataset:tomaarsen/natural-questions-hard-negatives", "dataset:tomaarsen/gooaq-hard-negatives", "dataset:bclavie/msmarco-500k-triplets", "dataset:sentence-transformers/all-nli", "dataset:sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1", "dataset:sentence-transformers/gooaq", "dataset:sentence-transformers/natural-questions", "dataset:tasksource/merged-2l-nli", "dataset:tasksource/merged-3l-nli", "dataset:tasksource/zero-shot-label-nli", "dataset:MoritzLaurer/dataset_train_nli", "dataset:google-research-datasets/paws", "dataset:nyu-mll/glue", "dataset:mwong/fever-evidence-related", "dataset:tasksource/sts-companion", "base_model:answerdotai/ModernBERT-base", "base_model:finetune:answerdotai/ModernBERT-base", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2025-04-02T15:42:12Z

--- language: - en tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:6661966 - loss:MultipleNegativesRankingLoss - loss:CachedMultipleNegativesRankingLoss - loss:SoftmaxLoss - loss:AnglELoss - loss:CoSENTLoss - loss:CosineSimilarityLoss - mlx base_model: answerdotai/ModernBERT-base widget: - source_sentence: Daniel went to the kitchen. Sandra went back to the kitchen. Daniel moved to the garden. Sandra grabbed the apple. Sandra went back to the office. Sandra dropped the apple. Sandra went to the garden. Sandra went back to the bedroom. Sandra went back to the office. Mary went back to the office. Daniel moved to the bathroom. Sandra grabbed the apple. Sandra travelled to the garden. Sandra put down the apple there. Mary went back to the bathroom. Daniel travelled to the garden. Mary took the milk. Sandra grabbed the apple. Mary left the milk there. Sandra journeyed to the bedroom. John travelled to the office. John went back to the garden. Sandra journeyed to the garden. Mary grabbed the milk. Mary left the milk. Mary grabbed the milk. Mary went to the hallway. John moved to the hallway. Mary picked up the football. Sandra journeyed to the kitchen. Sandra left the apple. Mary discarded the milk. John journeyed to the garden. Mary dropped the football. Daniel moved to the bathroom. Daniel journeyed to the kitchen. Mary travelled to the bathroom. Daniel went to the bedroom. Mary went to the hallway. Sandra got the apple. Sandra went back to the hallway. Mary moved to the kitchen. Sandra dropped the apple there. Sandra grabbed the milk. Sandra journeyed to the bathroom. John went back to the kitchen. Sandra went to the kitchen. Sandra travelled to the bathroom. Daniel went to the garden. Daniel moved to the kitchen. Sandra dropped the milk. Sandra got the milk. Sandra put down the milk. John journeyed to the garden. Sandra went back to the hallway. Sandra picked up the apple. Sandra got the football. Sandra moved to the garden. Daniel moved to the bathroom. Daniel travelled to the garden. Sandra went back to the bathroom. Sandra discarded the football. sentences: - In the adulthood stage, it can jump, walk, run - The chocolate is bigger than the container. - The football before the bathroom was in the garden. - source_sentence: Almost everywhere the series converges then . sentences: - The series then converges almost everywhere . - Scrivener dated the manuscript to the 12th century , C. R. Gregory to the 13th century . Currently the manuscript is dated by the INTF to the 12th century . - Both daughters died before he did , Tosca in 1976 and Janear in 1981 . - source_sentence: how are you i'm doing good thank you you im not good having cough and colg sentences: - 'This example tweet expresses the emotion: happiness' - This example utterance is about cooking recipies. - This example text from a US presidential speech is about macroeconomics - source_sentence: A man is doing pull-ups sentences: - The man is doing exercises in a gym - A black and white dog with a large branch is running in the field - There is no man drawing - source_sentence: A chef is preparing some food sentences: - The man is lifting weights - A chef is preparing a meal - A dog is in a sandy area with the sand that is being stirred up into the air and several plants are in the background datasets: - tomaarsen/natural-questions-hard-negatives - tomaarsen/gooaq-hard-negatives - bclavie/msmarco-500k-triplets - sentence-transformers/all-nli - sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1 - sentence-transformers/gooaq - sentence-transformers/natural-questions - tasksource/merged-2l-nli - tasksource/merged-3l-nli - tasksource/zero-shot-label-nli - MoritzLaurer/dataset_train_nli - google-research-datasets/paws - nyu-mll/glue - mwong/fever-evidence-related - tasksource/sts-companion pipeline_tag: sentence-similarity library_name: sentence-transformers --- # mlx-community/tasksource-ModernBERT-base-embed-bf16 The Model [mlx-community/tasksource-ModernBERT-base-embed-bf16](https://huggingface.co/mlx-community/tasksource-ModernBERT-base-embed-bf16) was converted to MLX format from [tasksource/ModernBERT-base-embed](https://huggingface.co/tasksource/ModernBERT-base-embed) using mlx-lm version **0.0.3**. ## Use with mlx ```bash pip install mlx-embeddings ``` ```python from mlx_embeddings import load, generate import mlx.core as mx model, tokenizer = load("mlx-community/tasksource-ModernBERT-base-embed-bf16") # For text embeddings output = generate(model, processor, texts=["I like grapes", "I like fruits"]) embeddings = output.text_embeds # Normalized embeddings # Compute dot product between normalized embeddings similarity_matrix = mx.matmul(embeddings, embeddings.T) print("Similarity matrix between texts:") print(similarity_matrix) ```

deeplogixio/deeplogix

deeplogixio

2025-04-02T16:08:48Z

0

null

[ "en", "license:mit", "region:us" ]

null

2025-04-02T15:05:13Z

--- license: mit language: - en --- # Deeplogix AI RPC Deeplogix AI RPC is a Python module for seamlessly running remote private AI models from your Python code as if these models were installed locally. ## Installation Use the package manager [pip](https://pip.pypa.io/en/stable/) for install. Development version from TestPyPi: ```bash pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple deeplogix ``` Production version from PyPi: ```bash pip install deeplogix ``` ## Usage For module built-in demos: ```bash python -m deeplogix --demo ``` Or for your A.I. script: ```bash python -m deeplogix ./path/to/your/script.py ``` Or just: ```bash python -m deeplogix ``` And it will print usage options. When you run Deeplogix module first time - it will ask you hostId and token, which could be obtained at [Deeplogix](https://www.deeplogix.io/) site after sign up. ## License [MIT](https://choosealicense.com/licenses/mit/)

wilzeelbub/testo

wilzeelbub

2025-04-02T16:07:06Z

0

1

null

[ "region:us" ]

null

2025-04-02T16:04:37Z

from datasets import load_dataset dataset = load_dataset("imdb") # Example with IMDB dataset from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments # Load pre-trained model and tokenizer model_name = "bert-base-uncased" tokenizer = BertTokenizer.from_pretrained(model_name) model = BertForSequenceClassification.from_pretrained(model_name) # Tokenize the dataset def tokenize_function(examples): return tokenizer(examples['text'], padding="max_length", truncation=True) tokenized_datasets = dataset.map(tokenize_function, batched=True) # Define training arguments training_args = TrainingArguments( output_dir="./results", evaluation_strategy="epoch", learning_rate=2e-5, per_device_train_batch_size=16, num_train_epochs=3, ) # Create Trainer instance trainer = Trainer( model=model, args=training_args, train_dataset=tokenized_datasets["train"], eval_dataset=tokenized_datasets["test"], ) # Train the model trainer.train() model.save_pretrained("./my_model") tokenizer.save_pretrained("./my_model") # Upload to Hugging Face from huggingface_hub import HfApi, HfFolder # Log in to your Hugging Face account HfFolder.save_token("YOUR_HUGGINGFACE_TOKEN") api = HfApi() api.upload_folder( folder_path="./my_model", path_in_repo="my_model", repo_id="your_username/my_model", repo_type="model", ) from transformers import pipeline classifier = pipeline("text-classification", model="your_username/my_model") result = classifier("This is an example sentence.") print(result)

mlx-community/tasksource-ModernBERT-base-embed-4bit

mlx-community

2025-04-02T16:06:41Z

0

sentence-transformers

[ "sentence-transformers", "safetensors", "modernbert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:6661966", "loss:MultipleNegativesRankingLoss", "loss:CachedMultipleNegativesRankingLoss", "loss:SoftmaxLoss", "loss:AnglELoss", "loss:CoSENTLoss", "loss:CosineSimilarityLoss", "mlx", "en", "dataset:tomaarsen/natural-questions-hard-negatives", "dataset:tomaarsen/gooaq-hard-negatives", "dataset:bclavie/msmarco-500k-triplets", "dataset:sentence-transformers/all-nli", "dataset:sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1", "dataset:sentence-transformers/gooaq", "dataset:sentence-transformers/natural-questions", "dataset:tasksource/merged-2l-nli", "dataset:tasksource/merged-3l-nli", "dataset:tasksource/zero-shot-label-nli", "dataset:MoritzLaurer/dataset_train_nli", "dataset:google-research-datasets/paws", "dataset:nyu-mll/glue", "dataset:mwong/fever-evidence-related", "dataset:tasksource/sts-companion", "base_model:answerdotai/ModernBERT-base", "base_model:finetune:answerdotai/ModernBERT-base", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2025-04-02T15:41:22Z

--- language: - en tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:6661966 - loss:MultipleNegativesRankingLoss - loss:CachedMultipleNegativesRankingLoss - loss:SoftmaxLoss - loss:AnglELoss - loss:CoSENTLoss - loss:CosineSimilarityLoss - mlx base_model: answerdotai/ModernBERT-base widget: - source_sentence: Daniel went to the kitchen. Sandra went back to the kitchen. Daniel moved to the garden. Sandra grabbed the apple. Sandra went back to the office. Sandra dropped the apple. Sandra went to the garden. Sandra went back to the bedroom. Sandra went back to the office. Mary went back to the office. Daniel moved to the bathroom. Sandra grabbed the apple. Sandra travelled to the garden. Sandra put down the apple there. Mary went back to the bathroom. Daniel travelled to the garden. Mary took the milk. Sandra grabbed the apple. Mary left the milk there. Sandra journeyed to the bedroom. John travelled to the office. John went back to the garden. Sandra journeyed to the garden. Mary grabbed the milk. Mary left the milk. Mary grabbed the milk. Mary went to the hallway. John moved to the hallway. Mary picked up the football. Sandra journeyed to the kitchen. Sandra left the apple. Mary discarded the milk. John journeyed to the garden. Mary dropped the football. Daniel moved to the bathroom. Daniel journeyed to the kitchen. Mary travelled to the bathroom. Daniel went to the bedroom. Mary went to the hallway. Sandra got the apple. Sandra went back to the hallway. Mary moved to the kitchen. Sandra dropped the apple there. Sandra grabbed the milk. Sandra journeyed to the bathroom. John went back to the kitchen. Sandra went to the kitchen. Sandra travelled to the bathroom. Daniel went to the garden. Daniel moved to the kitchen. Sandra dropped the milk. Sandra got the milk. Sandra put down the milk. John journeyed to the garden. Sandra went back to the hallway. Sandra picked up the apple. Sandra got the football. Sandra moved to the garden. Daniel moved to the bathroom. Daniel travelled to the garden. Sandra went back to the bathroom. Sandra discarded the football. sentences: - In the adulthood stage, it can jump, walk, run - The chocolate is bigger than the container. - The football before the bathroom was in the garden. - source_sentence: Almost everywhere the series converges then . sentences: - The series then converges almost everywhere . - Scrivener dated the manuscript to the 12th century , C. R. Gregory to the 13th century . Currently the manuscript is dated by the INTF to the 12th century . - Both daughters died before he did , Tosca in 1976 and Janear in 1981 . - source_sentence: how are you i'm doing good thank you you im not good having cough and colg sentences: - 'This example tweet expresses the emotion: happiness' - This example utterance is about cooking recipies. - This example text from a US presidential speech is about macroeconomics - source_sentence: A man is doing pull-ups sentences: - The man is doing exercises in a gym - A black and white dog with a large branch is running in the field - There is no man drawing - source_sentence: A chef is preparing some food sentences: - The man is lifting weights - A chef is preparing a meal - A dog is in a sandy area with the sand that is being stirred up into the air and several plants are in the background datasets: - tomaarsen/natural-questions-hard-negatives - tomaarsen/gooaq-hard-negatives - bclavie/msmarco-500k-triplets - sentence-transformers/all-nli - sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1 - sentence-transformers/gooaq - sentence-transformers/natural-questions - tasksource/merged-2l-nli - tasksource/merged-3l-nli - tasksource/zero-shot-label-nli - MoritzLaurer/dataset_train_nli - google-research-datasets/paws - nyu-mll/glue - mwong/fever-evidence-related - tasksource/sts-companion pipeline_tag: sentence-similarity library_name: sentence-transformers --- # mlx-community/ModernBERT-base-embed-4bit The Model [mlx-community/tasksource-ModernBERT-base-embed-4bit](https://huggingface.co/mlx-community/tasksource-ModernBERT-base-embed-4bit) was converted to MLX format from [tasksource/ModernBERT-base-embed](https://huggingface.co/tasksource/ModernBERT-base-embed) using mlx-lm version **0.0.3**. ## Use with mlx ```bash pip install mlx-embeddings ``` ```python from mlx_embeddings import load, generate import mlx.core as mx model, tokenizer = load("mlx-community/tasksource-ModernBERT-base-embed-4bit") # For text embeddings output = generate(model, processor, texts=["I like grapes", "I like fruits"]) embeddings = output.text_embeds # Normalized embeddings # Compute dot product between normalized embeddings similarity_matrix = mx.matmul(embeddings, embeddings.T) print("Similarity matrix between texts:") print(similarity_matrix) ```

meister1378/gemma2-9b-3753_step

meister1378

2025-04-02T16:05:27Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma2", "trl", "en", "base_model:HumanF-MarkrAI/Gukbap-Gemma2-9B", "base_model:finetune:HumanF-MarkrAI/Gukbap-Gemma2-9B", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-04-02T16:04:07Z

--- base_model: HumanF-MarkrAI/Gukbap-Gemma2-9B tags: - text-generation-inference - transformers - unsloth - gemma2 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** meister1378 - **License:** apache-2.0 - **Finetuned from model :** HumanF-MarkrAI/Gukbap-Gemma2-9B This gemma2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

mlx-community/nomicai-modernbert-embed-base-bf16

mlx-community

2025-04-02T16:05:15Z

0

sentence-transformers

[ "sentence-transformers", "safetensors", "modernbert", "feature-extraction", "sentence-similarity", "mteb", "transformers.js", "mlx", "en", "base_model:answerdotai/ModernBERT-base", "base_model:finetune:answerdotai/ModernBERT-base", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2025-04-02T15:07:20Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb - transformers.js - mlx license: apache-2.0 language: - en base_model: - answerdotai/ModernBERT-base - nomic-ai/modernbert-embed-unsupervised base_model_relation: finetune model-index: - name: binarize_False results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 78.13432835820896 - type: ap value: 42.190424731303246 - type: f1 value: 72.34446401534811 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.093825 - type: ap value: 90.03727505544286 - type: f1 value: 93.0874055138833 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 48.428000000000004 - type: f1 value: 47.74311520203536 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 23.898 - type: map_at_10 value: 39.775 - type: map_at_100 value: 40.827000000000005 - type: map_at_1000 value: 40.837 - type: map_at_20 value: 40.604 - type: map_at_3 value: 34.519 - type: map_at_5 value: 37.307 - type: mrr_at_1 value: 24.395 - type: mrr_at_10 value: 39.963 - type: mrr_at_100 value: 41.014 - type: mrr_at_1000 value: 41.024 - type: mrr_at_20 value: 40.791 - type: mrr_at_3 value: 34.732 - type: mrr_at_5 value: 37.480999999999995 - type: ndcg_at_1 value: 23.898 - type: ndcg_at_10 value: 48.962 - type: ndcg_at_100 value: 53.386 - type: ndcg_at_1000 value: 53.634 - type: ndcg_at_20 value: 51.898999999999994 - type: ndcg_at_3 value: 38.034 - type: ndcg_at_5 value: 43.036 - type: precision_at_1 value: 23.898 - type: precision_at_10 value: 7.852 - type: precision_at_100 value: 0.9769999999999999 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 4.4990000000000006 - type: precision_at_3 value: 16.073999999999998 - type: precision_at_5 value: 12.063 - type: recall_at_1 value: 23.898 - type: recall_at_10 value: 78.521 - type: recall_at_100 value: 97.724 - type: recall_at_1000 value: 99.644 - type: recall_at_20 value: 89.972 - type: recall_at_3 value: 48.222 - type: recall_at_5 value: 60.313 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 47.69067314293749 - type: v_measures value: - 0.4953006738713271 - 0.500982950617211 - 0.490168788349858 - 0.4924060458428337 - 0.475176328561399 - 0.47446297663785564 - 0.46948807073019405 - 0.4772028638329531 - 0.48735189935310713 - 0.48641173887761663 - 0.5575029526712674 - 0.5574020390232136 - 0.5536066904942645 - 0.5536169413675474 - 0.5566938602585987 - 0.5561143054736898 - 0.561846457174852 - 0.5511643632282144 - 0.5514762015499715 - 0.551824471283655 - 0.5148077891863135 - 0.29015461701593837 - 0.4430422977323321 - 0.40857527197890686 - 0.3479983114229163 - 0.27582001934225003 - 0.29595564003512503 - 0.22528676611734755 - 0.3073271865740206 - 1.0 - 0.2749401557058413 - 0.4953006738713271 - 0.500982950617211 - 0.490168788349858 - 0.4924060458428337 - 0.475176328561399 - 0.47446297663785564 - 0.46948807073019405 - 0.4772028638329531 - 0.48735189935310713 - 0.48641173887761663 - 0.5575029526712674 - 0.5574020390232136 - 0.5536066904942645 - 0.5536169413675474 - 0.5566938602585987 - 0.5561143054736898 - 0.561846457174852 - 0.5511643632282144 - 0.5514762015499715 - 0.551824471283655 - 0.5148077891863135 - 0.29015461701593837 - 0.4430422977323321 - 0.40857527197890686 - 0.3479983114229163 - 0.27582001934225003 - 0.29595564003512503 - 0.22528676611734755 - 0.3073271865740206 - 1.0 - 0.2749401557058413 - 0.4953006738713271 - 0.500982950617211 - 0.490168788349858 - 0.4924060458428337 - 0.475176328561399 - 0.47446297663785564 - 0.46948807073019405 - 0.4772028638329531 - 0.48735189935310713 - 0.48641173887761663 - 0.5575029526712674 - 0.5574020390232136 - 0.5536066904942645 - 0.5536169413675474 - 0.5566938602585987 - 0.5561143054736898 - 0.561846457174852 - 0.5511643632282144 - 0.5514762015499715 - 0.551824471283655 - 0.5148077891863135 - 0.29015461701593837 - 0.4430422977323321 - 0.40857527197890686 - 0.3479983114229163 - 0.27582001934225003 - 0.29595564003512503 - 0.22528676611734755 - 0.3073271865740206 - 1.0 - 0.2749401557058413 - 0.4953006738713271 - 0.500982950617211 - 0.490168788349858 - 0.4924060458428337 - 0.475176328561399 - 0.47446297663785564 - 0.46948807073019405 - 0.4772028638329531 - 0.48735189935310713 - 0.48641173887761663 - 0.5575029526712674 - 0.5574020390232136 - 0.5536066904942645 - 0.5536169413675474 - 0.5566938602585987 - 0.5561143054736898 - 0.561846457174852 - 0.5511643632282144 - 0.5514762015499715 - 0.551824471283655 - 0.5148077891863135 - 0.29015461701593837 - 0.4430422977323321 - 0.40857527197890686 - 0.3479983114229163 - 0.27582001934225003 - 0.29595564003512503 - 0.22528676611734755 - 0.3073271865740206 - 1.0 - 0.2749401557058413 - 0.4953006738713271 - 0.500982950617211 - 0.490168788349858 - 0.4924060458428337 - 0.475176328561399 - 0.47446297663785564 - 0.46948807073019405 - 0.4772028638329531 - 0.48735189935310713 - 0.48641173887761663 - 0.5575029526712674 - 0.5574020390232136 - 0.5536066904942645 - 0.5536169413675474 - 0.5566938602585987 - 0.5561143054736898 - 0.561846457174852 - 0.5511643632282144 - 0.5514762015499715 - 0.551824471283655 - 0.5148077891863135 - 0.29015461701593837 - 0.4430422977323321 - 0.40857527197890686 - 0.3479983114229163 - 0.27582001934225003 - 0.29595564003512503 - 0.22528676611734755 - 0.3073271865740206 - 1.0 - 0.2749401557058413 - 0.4953006738713271 - 0.500982950617211 - 0.490168788349858 - 0.4924060458428337 - 0.475176328561399 - 0.47446297663785564 - 0.46948807073019405 - 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type: map_at_100 value: 45.152 - type: map_at_1000 value: 45.279 - type: map_at_20 value: 44.553 - type: map_at_3 value: 40.321 - type: map_at_5 value: 42.201 - type: mrr_at_1 value: 40.2 - type: mrr_at_10 value: 49.755 - type: mrr_at_100 value: 50.468 - type: mrr_at_1000 value: 50.513 - type: mrr_at_20 value: 50.192 - type: mrr_at_3 value: 47.163 - type: mrr_at_5 value: 48.686 - type: ndcg_at_1 value: 40.2 - type: ndcg_at_10 value: 49.963 - type: ndcg_at_100 value: 54.978 - type: ndcg_at_1000 value: 56.979 - type: ndcg_at_20 value: 51.983000000000004 - type: ndcg_at_3 value: 45.086999999999996 - type: ndcg_at_5 value: 47.309 - type: precision_at_1 value: 40.2 - type: precision_at_10 value: 9.328 - type: precision_at_100 value: 1.443 - type: precision_at_1000 value: 0.19 - type: precision_at_20 value: 5.558 - type: precision_at_3 value: 21.364 - type: precision_at_5 value: 15.222 - type: recall_at_1 value: 32.677 - type: recall_at_10 value: 61.71 - type: recall_at_100 value: 82.431 - type: recall_at_1000 value: 94.896 - type: recall_at_20 value: 68.73700000000001 - type: recall_at_3 value: 47.431 - type: recall_at_5 value: 53.739000000000004 - type: map_at_1 value: 27.734166666666667 - type: map_at_10 value: 36.858 - type: map_at_100 value: 38.043833333333325 - type: map_at_1000 value: 38.15541666666667 - type: map_at_20 value: 37.521249999999995 - type: map_at_3 value: 34.07658333333333 - type: map_at_5 value: 35.62683333333333 - type: mrr_at_1 value: 32.676249999999996 - type: mrr_at_10 value: 40.999 - type: mrr_at_100 value: 41.835 - type: mrr_at_1000 value: 41.8895 - type: mrr_at_20 value: 41.4865 - type: mrr_at_3 value: 38.645 - type: mrr_at_5 value: 39.99725000000001 - type: ndcg_at_1 value: 32.676249999999996 - type: ndcg_at_10 value: 42.08016666666666 - type: ndcg_at_100 value: 47.082750000000004 - type: ndcg_at_1000 value: 49.276583333333335 - type: ndcg_at_20 value: 44.04808333333334 - type: ndcg_at_3 value: 37.43375 - type: ndcg_at_5 value: 39.623000000000005 - type: precision_at_1 value: 32.676249999999996 - type: precision_at_10 value: 7.271 - type: precision_at_100 value: 1.1458333333333333 - type: precision_at_1000 value: 0.152 - type: precision_at_20 value: 4.282916666666667 - type: precision_at_3 value: 17.061416666666666 - type: precision_at_5 value: 12.05466666666667 - type: recall_at_1 value: 27.734166666666667 - type: recall_at_10 value: 53.33574999999999 - type: recall_at_100 value: 75.16275 - type: recall_at_1000 value: 90.34891666666665 - type: recall_at_20 value: 60.4935 - type: recall_at_3 value: 40.377916666666664 - type: recall_at_5 value: 46.0195 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: BeIR/cqadupstack config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 32.71 - type: map_at_10 value: 43.297000000000004 - type: map_at_100 value: 44.607 - type: map_at_1000 value: 44.729 - type: map_at_20 value: 44.013999999999996 - type: map_at_3 value: 40.213 - type: map_at_5 value: 42.004000000000005 - 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type: recall_at_5 value: 51.946999999999996 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: BeIR/cqadupstack config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 41.644999999999996 - type: map_at_10 value: 53.825 - type: map_at_100 value: 54.82 - type: map_at_1000 value: 54.87499999999999 - type: map_at_20 value: 54.43 - type: map_at_3 value: 50.705 - type: map_at_5 value: 52.501 - type: mrr_at_1 value: 47.524 - type: mrr_at_10 value: 57.260999999999996 - type: mrr_at_100 value: 57.902 - type: mrr_at_1000 value: 57.931999999999995 - type: mrr_at_20 value: 57.689 - type: mrr_at_3 value: 55.089 - type: mrr_at_5 value: 56.38999999999999 - type: ndcg_at_1 value: 47.524 - type: ndcg_at_10 value: 59.41499999999999 - type: ndcg_at_100 value: 63.258 - type: ndcg_at_1000 value: 64.376 - type: ndcg_at_20 value: 61.149 - type: ndcg_at_3 value: 54.381 - type: ndcg_at_5 value: 56.89999999999999 - type: precision_at_1 value: 47.524 - type: precision_at_10 value: 9.386 - type: precision_at_100 value: 1.221 - type: precision_at_1000 value: 0.136 - type: precision_at_20 value: 5.223 - type: precision_at_3 value: 24.096 - type: precision_at_5 value: 16.364 - type: recall_at_1 value: 41.644999999999996 - type: recall_at_10 value: 72.386 - type: recall_at_100 value: 88.794 - type: recall_at_1000 value: 96.75399999999999 - type: recall_at_20 value: 78.74 - type: recall_at_3 value: 59.028000000000006 - type: recall_at_5 value: 65.197 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: BeIR/cqadupstack config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 28.648 - type: map_at_10 value: 36.388999999999996 - type: map_at_100 value: 37.372 - type: map_at_1000 value: 37.457 - type: map_at_20 value: 36.912 - type: map_at_3 value: 34.076 - type: map_at_5 value: 35.415 - type: mrr_at_1 value: 30.508000000000003 - type: mrr_at_10 value: 38.132 - type: mrr_at_100 value: 39.04 - 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type: recall_at_20 value: 64.095 - type: recall_at_3 value: 41.665 - type: recall_at_5 value: 47.632000000000005 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: BeIR/cqadupstack config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 25.653 - type: map_at_10 value: 32.151 - type: map_at_100 value: 33.152 - type: map_at_1000 value: 33.243 - type: map_at_20 value: 32.717 - type: map_at_3 value: 30.287 - type: map_at_5 value: 31.25 - type: mrr_at_1 value: 28.988000000000003 - type: mrr_at_10 value: 35.131 - type: mrr_at_100 value: 36.002 - type: mrr_at_1000 value: 36.069 - type: mrr_at_20 value: 35.61 - type: mrr_at_3 value: 33.308 - type: mrr_at_5 value: 34.259 - type: ndcg_at_1 value: 28.988000000000003 - type: ndcg_at_10 value: 35.988 - type: ndcg_at_100 value: 40.764 - type: ndcg_at_1000 value: 43.112 - type: ndcg_at_20 value: 37.852999999999994 - type: ndcg_at_3 value: 32.562000000000005 - type: ndcg_at_5 value: 33.983000000000004 - 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0.4702417082210781 - 0.4763216048226018 - 0.49932879417585735 - 0.5129628835129124 - 0.514824404624281 - 0.48829262296803855 - 0.49853262011854643 - 0.48457750518082765 - 0.5020774116970983 - 0.5001897357021557 - 0.4702417082210781 - 0.4763216048226018 - 0.49932879417585735 - 0.5129628835129124 - 0.514824404624281 - 0.48829262296803855 - 0.49853262011854643 - 0.48457750518082765 - 0.5020774116970983 - 0.5001897357021557 - 0.4702417082210781 - 0.4763216048226018 - 0.49932879417585735 - 0.5129628835129124 - 0.514824404624281 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: None config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 85.75430649102938 - type: cos_sim_ap value: 73.62842656477649 - type: cos_sim_f1 value: 67.76023680315738 - type: cos_sim_precision value: 63.61741547012506 - type: cos_sim_recall value: 72.4802110817942 - type: dot_accuracy value: 85.7423854085951 - type: dot_ap value: 73.59147637253723 - type: dot_f1 value: 67.69498693867396 - type: dot_precision value: 64.03859731701577 - type: dot_recall value: 71.79419525065963 - type: euclidean_accuracy value: 85.7423854085951 - type: euclidean_ap value: 73.6288990409654 - type: euclidean_f1 value: 67.80415430267064 - type: euclidean_precision value: 63.79711493718009 - type: euclidean_recall value: 72.34828496042216 - type: manhattan_accuracy value: 85.69470107885796 - type: manhattan_ap value: 73.49219614602531 - type: manhattan_f1 value: 67.60809797550613 - type: manhattan_precision value: 64.22127255460589 - type: manhattan_recall value: 71.37203166226914 - type: max_accuracy value: 85.75430649102938 - type: max_ap value: 73.6288990409654 - type: max_f1 value: 67.80415430267064 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: None config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.08293553770326 - type: cos_sim_ap value: 86.21246419992926 - type: cos_sim_f1 value: 78.49922526377924 - type: cos_sim_precision value: 75.35769939084857 - type: cos_sim_recall value: 81.9140745303357 - type: dot_accuracy value: 89.08681647067955 - type: dot_ap value: 86.19733517196862 - type: dot_f1 value: 78.51132446157838 - type: dot_precision value: 75.70233755093287 - type: dot_recall value: 81.53680320295658 - type: euclidean_accuracy value: 89.07517367175069 - type: euclidean_ap value: 86.21198725320203 - type: euclidean_f1 value: 78.49867139061116 - type: euclidean_precision value: 75.38276155372839 - type: euclidean_recall value: 81.88327687095781 - type: manhattan_accuracy value: 89.0538285403811 - type: manhattan_ap value: 86.17785515765131 - type: manhattan_f1 value: 78.48184098593084 - type: manhattan_precision value: 74.34396308285694 - type: manhattan_recall value: 83.10748383122882 - type: max_accuracy value: 89.08681647067955 - type: max_ap value: 86.21246419992926 - type: max_f1 value: 78.51132446157838 --- # mlx-community/modernbert-embed-base-bf16 The Model [mlx-community/nomicai-modernbert-embed-base-bf16](https://huggingface.co/mlx-community/nomicai-modernbert-embed-base-bf16) was converted to MLX format from [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) using mlx-lm version **0.0.3**. ## Use with mlx ```bash pip install mlx-embeddings ``` ```python from mlx_embeddings import load, generate import mlx.core as mx model, tokenizer = load("mlx-community/nomicai-modernbert-embed-base-bf16") # For text embeddings output = generate(model, processor, texts=["I like grapes", "I like fruits"]) embeddings = output.text_embeds # Normalized embeddings # Compute dot product between normalized embeddings similarity_matrix = mx.matmul(embeddings, embeddings.T) print("Similarity matrix between texts:") print(similarity_matrix) ```

veeru7474/llam321

veeru7474

2025-04-02T16:03:12Z

0

transformers

[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-04-02T16:02:39Z

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

optimum/bert-base-cased-squad2-neuronx

optimum

2025-04-02T16:00:50Z

0

null

[ "bert", "license:apache-2.0", "region:us" ]

null

2025-04-02T15:58:56Z

--- license: apache-2.0 --- **INFERENTIA2 ONLY** ```py from transformers import AutoTokenizer from optimum.neuron import NeuronBertForQuestionAnswering input_shapes = {"batch_size": 1, "sequence_length": 128} compiler_args = {"auto_cast": "matmul", "auto_cast_type": "bf16"} neuron_model = NeuronBertForQuestionAnswering.from_pretrained( "deepset/bert-base-cased-squad2", export=True, **input_shapes, **compiler_args, ) # Save locally neuron_model.save_pretrained("bert_base_cased_squad2_neuronx") neuron_model.push_to_hub( "bert_base_cased_squad2_neuronx", repository_id="optimum/bert-base-cased-squad2-neuronx", # Replace with your HF Hub repo id ) ```

mlx-community/nomicai-modernbert-embed-base-4bit

mlx-community

2025-04-02T15:59:08Z

0

sentence-transformers

[ "sentence-transformers", "safetensors", "modernbert", "feature-extraction", "sentence-similarity", "mteb", "transformers.js", "mlx", "en", "base_model:answerdotai/ModernBERT-base", "base_model:finetune:answerdotai/ModernBERT-base", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2025-04-02T13:59:42Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb - transformers.js - mlx license: apache-2.0 language: - en base_model: - answerdotai/ModernBERT-base - nomic-ai/modernbert-embed-unsupervised base_model_relation: finetune model-index: - name: binarize_False results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 78.13432835820896 - type: ap value: 42.190424731303246 - type: f1 value: 72.34446401534811 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.093825 - type: ap value: 90.03727505544286 - type: f1 value: 93.0874055138833 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 48.428000000000004 - type: f1 value: 47.74311520203536 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 23.898 - type: map_at_10 value: 39.775 - type: map_at_100 value: 40.827000000000005 - type: map_at_1000 value: 40.837 - type: map_at_20 value: 40.604 - type: map_at_3 value: 34.519 - type: map_at_5 value: 37.307 - type: mrr_at_1 value: 24.395 - type: mrr_at_10 value: 39.963 - type: mrr_at_100 value: 41.014 - type: mrr_at_1000 value: 41.024 - type: mrr_at_20 value: 40.791 - type: mrr_at_3 value: 34.732 - type: mrr_at_5 value: 37.480999999999995 - type: ndcg_at_1 value: 23.898 - type: ndcg_at_10 value: 48.962 - type: ndcg_at_100 value: 53.386 - type: ndcg_at_1000 value: 53.634 - type: ndcg_at_20 value: 51.898999999999994 - type: ndcg_at_3 value: 38.034 - type: ndcg_at_5 value: 43.036 - type: precision_at_1 value: 23.898 - type: precision_at_10 value: 7.852 - type: precision_at_100 value: 0.9769999999999999 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 4.4990000000000006 - type: precision_at_3 value: 16.073999999999998 - type: precision_at_5 value: 12.063 - type: recall_at_1 value: 23.898 - type: recall_at_10 value: 78.521 - type: recall_at_100 value: 97.724 - type: recall_at_1000 value: 99.644 - type: recall_at_20 value: 89.972 - type: recall_at_3 value: 48.222 - type: recall_at_5 value: 60.313 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 47.69067314293749 - type: v_measures value: - 0.4953006738713271 - 0.500982950617211 - 0.490168788349858 - 0.4924060458428337 - 0.475176328561399 - 0.47446297663785564 - 0.46948807073019405 - 0.4772028638329531 - 0.48735189935310713 - 0.48641173887761663 - 0.5575029526712674 - 0.5574020390232136 - 0.5536066904942645 - 0.5536169413675474 - 0.5566938602585987 - 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type: recall_at_1 value: 18.476 - type: recall_at_10 value: 43.442 - type: recall_at_100 value: 67.376 - type: recall_at_1000 value: 86.874 - type: recall_at_20 value: 51.038 - type: recall_at_3 value: 31.785999999999998 - type: recall_at_5 value: 36.858999999999995 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: BeIR/cqadupstack config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 29.098000000000003 - type: map_at_10 value: 38.97 - type: map_at_100 value: 40.293 - type: map_at_1000 value: 40.397 - type: map_at_20 value: 39.778999999999996 - type: map_at_3 value: 35.723 - type: map_at_5 value: 37.519999999999996 - type: mrr_at_1 value: 35.515 - type: mrr_at_10 value: 44.55 - type: mrr_at_100 value: 45.37 - type: mrr_at_1000 value: 45.412 - type: mrr_at_20 value: 45.054 - type: mrr_at_3 value: 41.835 - type: mrr_at_5 value: 43.356 - type: ndcg_at_1 value: 35.515 - type: ndcg_at_10 value: 44.91 - type: ndcg_at_100 value: 50.27700000000001 - type: ndcg_at_1000 value: 52.215 - type: ndcg_at_20 value: 47.235 - type: ndcg_at_3 value: 39.505 - type: ndcg_at_5 value: 42.016 - type: precision_at_1 value: 35.515 - type: precision_at_10 value: 8.152 - type: precision_at_100 value: 1.262 - type: precision_at_1000 value: 0.16 - type: precision_at_20 value: 4.851 - type: precision_at_3 value: 18.447 - type: precision_at_5 value: 13.321 - type: recall_at_1 value: 29.098000000000003 - type: recall_at_10 value: 57.115 - type: recall_at_100 value: 79.467 - type: recall_at_1000 value: 92.162 - type: recall_at_20 value: 65.161 - type: recall_at_3 value: 42.254000000000005 - type: recall_at_5 value: 48.415 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: BeIR/cqadupstack config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 27.372000000000003 - type: map_at_10 value: 37.781 - type: map_at_100 value: 39.128 - type: map_at_1000 value: 39.238 - type: map_at_20 value: 38.592 - type: map_at_3 value: 34.782999999999994 - type: map_at_5 value: 36.466 - type: mrr_at_1 value: 33.904 - type: mrr_at_10 value: 43.15 - type: mrr_at_100 value: 44.049 - type: mrr_at_1000 value: 44.107 - type: mrr_at_20 value: 43.721 - type: mrr_at_3 value: 40.677 - type: mrr_at_5 value: 42.19 - type: ndcg_at_1 value: 33.904 - type: ndcg_at_10 value: 43.527 - type: ndcg_at_100 value: 49.004999999999995 - type: ndcg_at_1000 value: 51.276999999999994 - type: ndcg_at_20 value: 45.988 - type: ndcg_at_3 value: 38.824999999999996 - type: ndcg_at_5 value: 41.04 - type: precision_at_1 value: 33.904 - type: precision_at_10 value: 7.854 - type: precision_at_100 value: 1.2309999999999999 - type: precision_at_1000 value: 0.16 - type: precision_at_20 value: 4.692 - type: precision_at_3 value: 18.531 - type: precision_at_5 value: 13.150999999999998 - type: recall_at_1 value: 27.372000000000003 - type: recall_at_10 value: 55.245999999999995 - type: recall_at_100 value: 78.278 - type: recall_at_1000 value: 93.718 - type: recall_at_20 value: 64.095 - type: recall_at_3 value: 41.665 - type: recall_at_5 value: 47.632000000000005 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: BeIR/cqadupstack config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 25.653 - type: map_at_10 value: 32.151 - type: map_at_100 value: 33.152 - type: map_at_1000 value: 33.243 - type: map_at_20 value: 32.717 - type: map_at_3 value: 30.287 - type: map_at_5 value: 31.25 - type: mrr_at_1 value: 28.988000000000003 - type: mrr_at_10 value: 35.131 - type: mrr_at_100 value: 36.002 - type: mrr_at_1000 value: 36.069 - type: mrr_at_20 value: 35.61 - type: mrr_at_3 value: 33.308 - type: mrr_at_5 value: 34.259 - type: ndcg_at_1 value: 28.988000000000003 - type: ndcg_at_10 value: 35.988 - type: ndcg_at_100 value: 40.764 - type: ndcg_at_1000 value: 43.112 - type: ndcg_at_20 value: 37.852999999999994 - type: ndcg_at_3 value: 32.562000000000005 - type: ndcg_at_5 value: 33.983000000000004 - type: precision_at_1 value: 28.988000000000003 - type: precision_at_10 value: 5.475 - type: precision_at_100 value: 0.8500000000000001 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_20 value: 3.229 - type: precision_at_3 value: 13.905999999999999 - type: precision_at_5 value: 9.386999999999999 - type: recall_at_1 value: 25.653 - type: recall_at_10 value: 44.962 - type: recall_at_100 value: 66.405 - type: recall_at_1000 value: 83.88799999999999 - type: recall_at_20 value: 51.79899999999999 - type: recall_at_3 value: 35.144999999999996 - type: recall_at_5 value: 38.814 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: BeIR/cqadupstack config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 17.825 - type: map_at_10 value: 25.592 - type: map_at_100 value: 26.613999999999997 - type: map_at_1000 value: 26.734 - type: map_at_20 value: 26.115 - type: map_at_3 value: 23.119 - type: map_at_5 value: 24.54 - type: mrr_at_1 value: 21.335 - type: mrr_at_10 value: 29.165000000000003 - type: mrr_at_100 value: 30.049 - type: mrr_at_1000 value: 30.121 - type: mrr_at_20 value: 29.639 - type: mrr_at_3 value: 26.863999999999997 - type: mrr_at_5 value: 28.185 - type: ndcg_at_1 value: 21.335 - type: ndcg_at_10 value: 30.357 - type: ndcg_at_100 value: 35.410000000000004 - type: ndcg_at_1000 value: 38.24 - type: ndcg_at_20 value: 32.08 - type: ndcg_at_3 value: 25.95 - type: ndcg_at_5 value: 28.081 - type: precision_at_1 value: 21.335 - type: precision_at_10 value: 5.506 - type: precision_at_100 value: 0.928 - type: precision_at_1000 value: 0.135 - type: precision_at_20 value: 3.2550000000000003 - type: precision_at_3 value: 12.239 - type: precision_at_5 value: 8.885 - type: recall_at_1 value: 17.825 - type: recall_at_10 value: 41.105999999999995 - type: recall_at_100 value: 64.17 - type: recall_at_1000 value: 84.19200000000001 - type: recall_at_20 value: 47.497 - type: recall_at_3 value: 28.862 - type: recall_at_5 value: 34.348 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: BeIR/cqadupstack config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 29.435 - type: map_at_10 value: 38.261 - type: map_at_100 value: 39.242 - type: map_at_1000 value: 39.347 - type: map_at_20 value: 38.742 - type: map_at_3 value: 35.457 - type: map_at_5 value: 37.043 - type: mrr_at_1 value: 34.235 - type: mrr_at_10 value: 42.24 - type: mrr_at_100 value: 42.988 - type: mrr_at_1000 value: 43.043 - type: mrr_at_20 value: 42.613 - type: mrr_at_3 value: 39.832 - type: mrr_at_5 value: 41.227000000000004 - type: ndcg_at_1 value: 34.235 - type: ndcg_at_10 value: 43.384 - type: ndcg_at_100 value: 48.14 - type: ndcg_at_1000 value: 50.414 - type: ndcg_at_20 value: 44.913 - type: ndcg_at_3 value: 38.454 - type: ndcg_at_5 value: 40.776 - type: precision_at_1 value: 34.235 - type: precision_at_10 value: 7.164 - type: precision_at_100 value: 1.065 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_20 value: 4.021 - type: precision_at_3 value: 17.226 - type: precision_at_5 value: 12.071 - type: recall_at_1 value: 29.435 - type: recall_at_10 value: 54.93900000000001 - type: recall_at_100 value: 76.176 - type: recall_at_1000 value: 91.989 - type: recall_at_20 value: 60.451 - type: recall_at_3 value: 41.332 - type: recall_at_5 value: 47.316 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: BeIR/cqadupstack config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 25.605 - type: map_at_10 value: 34.162 - type: map_at_100 value: 35.827999999999996 - type: map_at_1000 value: 36.04 - type: map_at_20 value: 35.016000000000005 - type: map_at_3 value: 30.984 - type: map_at_5 value: 32.717 - type: mrr_at_1 value: 30.435000000000002 - type: mrr_at_10 value: 38.681 - type: mrr_at_100 value: 39.656000000000006 - type: mrr_at_1000 value: 39.71 - type: mrr_at_20 value: 39.208999999999996 - type: mrr_at_3 value: 35.903 - type: mrr_at_5 value: 37.454 - type: ndcg_at_1 value: 30.435000000000002 - type: ndcg_at_10 value: 39.916000000000004 - type: ndcg_at_100 value: 45.958 - type: ndcg_at_1000 value: 48.449999999999996 - type: ndcg_at_20 value: 42.085 - type: ndcg_at_3 value: 34.696 - type: ndcg_at_5 value: 37.147000000000006 - type: precision_at_1 value: 30.435000000000002 - type: precision_at_10 value: 7.767 - type: precision_at_100 value: 1.547 - type: precision_at_1000 value: 0.23800000000000002 - type: precision_at_20 value: 4.941 - type: precision_at_3 value: 16.073999999999998 - type: precision_at_5 value: 11.937000000000001 - type: recall_at_1 value: 25.605 - type: recall_at_10 value: 50.654999999999994 - type: recall_at_100 value: 77.609 - type: recall_at_1000 value: 93.518 - type: recall_at_20 value: 58.845000000000006 - type: recall_at_3 value: 36.272 - type: recall_at_5 value: 42.596000000000004 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: BeIR/cqadupstack config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 23.666 - type: map_at_10 value: 30.980999999999998 - type: map_at_100 value: 32.0 - type: map_at_1000 value: 32.098 - type: map_at_20 value: 31.621 - type: map_at_3 value: 28.449999999999996 - type: map_at_5 value: 29.731999999999996 - type: mrr_at_1 value: 25.692999999999998 - type: mrr_at_10 value: 32.788000000000004 - type: mrr_at_100 value: 33.783 - type: mrr_at_1000 value: 33.849000000000004 - type: mrr_at_20 value: 33.408 - type: mrr_at_3 value: 30.561 - type: mrr_at_5 value: 31.716 - type: ndcg_at_1 value: 25.692999999999998 - type: ndcg_at_10 value: 35.428 - type: ndcg_at_100 value: 40.375 - type: ndcg_at_1000 value: 42.802 - type: ndcg_at_20 value: 37.621 - type: ndcg_at_3 value: 30.476999999999997 - type: ndcg_at_5 value: 32.621 - type: precision_at_1 value: 25.692999999999998 - type: precision_at_10 value: 5.508 - type: precision_at_100 value: 0.848 - type: precision_at_1000 value: 0.116 - type: precision_at_20 value: 3.272 - type: precision_at_3 value: 12.631 - type: precision_at_5 value: 8.872 - type: recall_at_1 value: 23.666 - type: recall_at_10 value: 47.532000000000004 - type: recall_at_100 value: 69.73700000000001 - type: recall_at_1000 value: 87.83800000000001 - type: recall_at_20 value: 55.61000000000001 - type: recall_at_3 value: 34.06 - type: recall_at_5 value: 39.254 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 16.337 - type: map_at_10 value: 26.488 - type: map_at_100 value: 28.415000000000003 - type: map_at_1000 value: 28.584 - type: map_at_20 value: 27.557 - type: map_at_3 value: 22.665 - type: map_at_5 value: 24.542 - type: mrr_at_1 value: 36.417 - type: mrr_at_10 value: 48.001 - type: mrr_at_100 value: 48.784 - type: mrr_at_1000 value: 48.809000000000005 - type: mrr_at_20 value: 48.507 - type: mrr_at_3 value: 45.103 - type: mrr_at_5 value: 46.843 - type: ndcg_at_1 value: 36.417 - type: ndcg_at_10 value: 35.67 - type: ndcg_at_100 value: 42.716 - type: ndcg_at_1000 value: 45.639 - type: ndcg_at_20 value: 38.471 - type: ndcg_at_3 value: 30.444 - type: ndcg_at_5 value: 32.004 - type: precision_at_1 value: 36.417 - type: precision_at_10 value: 10.73 - type: precision_at_100 value: 1.833 - type: precision_at_1000 value: 0.23800000000000002 - type: precision_at_20 value: 6.596 - type: precision_at_3 value: 22.302 - type: precision_at_5 value: 16.521 - type: recall_at_1 value: 16.337 - type: recall_at_10 value: 40.671 - type: recall_at_100 value: 64.55300000000001 - type: recall_at_1000 value: 80.934 - type: recall_at_20 value: 48.381 - type: recall_at_3 value: 27.279999999999998 - type: recall_at_5 value: 32.621 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 9.056000000000001 - type: map_at_10 value: 19.419 - type: map_at_100 value: 27.069 - type: map_at_1000 value: 28.666000000000004 - type: map_at_20 value: 22.434 - type: map_at_3 value: 13.895 - type: map_at_5 value: 16.121 - type: mrr_at_1 value: 69.0 - type: mrr_at_10 value: 75.804 - type: mrr_at_100 value: 76.117 - type: mrr_at_1000 value: 76.125 - type: mrr_at_20 value: 76.009 - type: mrr_at_3 value: 74.375 - type: mrr_at_5 value: 75.4 - type: ndcg_at_1 value: 57.49999999999999 - type: ndcg_at_10 value: 41.495 - type: ndcg_at_100 value: 45.208 - type: ndcg_at_1000 value: 52.221 - type: ndcg_at_20 value: 40.617999999999995 - type: ndcg_at_3 value: 46.592 - type: ndcg_at_5 value: 43.559 - type: precision_at_1 value: 69.0 - type: precision_at_10 value: 32.574999999999996 - type: precision_at_100 value: 10.205 - type: precision_at_1000 value: 2.036 - type: precision_at_20 value: 24.687 - type: precision_at_3 value: 49.75 - type: precision_at_5 value: 42.0 - type: recall_at_1 value: 9.056000000000001 - type: recall_at_10 value: 24.866 - type: recall_at_100 value: 50.097 - type: recall_at_1000 value: 72.038 - type: recall_at_20 value: 31.858999999999998 - type: recall_at_3 value: 15.096000000000002 - type: recall_at_5 value: 18.548000000000002 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 48.259999999999984 - type: f1 value: 43.1498589523159 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 74.798 - type: map_at_10 value: 83.454 - type: map_at_100 value: 83.623 - type: map_at_1000 value: 83.635 - type: map_at_20 value: 83.55 - type: map_at_3 value: 82.392 - type: map_at_5 value: 83.167 - type: mrr_at_1 value: 80.708 - type: mrr_at_10 value: 88.377 - type: mrr_at_100 value: 88.411 - type: mrr_at_1000 value: 88.411 - type: mrr_at_20 value: 88.402 - type: mrr_at_3 value: 87.646 - type: mrr_at_5 value: 88.232 - type: ndcg_at_1 value: 80.708 - type: ndcg_at_10 value: 87.35199999999999 - type: ndcg_at_100 value: 87.91600000000001 - type: ndcg_at_1000 value: 88.12299999999999 - type: ndcg_at_20 value: 87.593 - type: ndcg_at_3 value: 85.738 - type: ndcg_at_5 value: 86.845 - type: precision_at_1 value: 80.708 - type: precision_at_10 value: 10.432 - type: precision_at_100 value: 1.091 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_20 value: 5.296 - type: precision_at_3 value: 32.778 - type: precision_at_5 value: 20.399 - type: recall_at_1 value: 74.798 - type: recall_at_10 value: 94.459 - type: recall_at_100 value: 96.614 - type: recall_at_1000 value: 97.868 - type: recall_at_20 value: 95.254 - type: recall_at_3 value: 90.144 - type: recall_at_5 value: 92.965 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 20.008 - type: map_at_10 value: 32.731 - type: map_at_100 value: 34.467999999999996 - type: map_at_1000 value: 34.643 - type: map_at_20 value: 33.717000000000006 - type: map_at_3 value: 28.427999999999997 - type: map_at_5 value: 30.788 - type: mrr_at_1 value: 40.586 - type: mrr_at_10 value: 49.056 - type: mrr_at_100 value: 49.887 - type: mrr_at_1000 value: 49.929 - type: mrr_at_20 value: 49.552 - type: mrr_at_3 value: 46.785 - type: mrr_at_5 value: 48.004000000000005 - type: ndcg_at_1 value: 40.586 - type: ndcg_at_10 value: 40.589999999999996 - type: ndcg_at_100 value: 47.03 - type: ndcg_at_1000 value: 49.994 - type: ndcg_at_20 value: 43.229 - type: ndcg_at_3 value: 37.061 - type: ndcg_at_5 value: 37.992 - type: precision_at_1 value: 40.586 - type: precision_at_10 value: 11.219 - type: precision_at_100 value: 1.781 - type: precision_at_1000 value: 0.232 - type: precision_at_20 value: 6.705 - type: precision_at_3 value: 24.743000000000002 - type: precision_at_5 value: 18.086 - type: recall_at_1 value: 20.008 - type: recall_at_10 value: 47.412 - type: recall_at_100 value: 71.274 - type: recall_at_1000 value: 88.898 - 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0.5129628835129124 - 0.514824404624281 - 0.48829262296803855 - 0.49853262011854643 - 0.48457750518082765 - 0.5020774116970983 - 0.5001897357021557 - 0.4702417082210781 - 0.4763216048226018 - 0.49932879417585735 - 0.5129628835129124 - 0.514824404624281 - 0.48829262296803855 - 0.49853262011854643 - 0.48457750518082765 - 0.5020774116970983 - 0.5001897357021557 - 0.4702417082210781 - 0.4763216048226018 - 0.49932879417585735 - 0.5129628835129124 - 0.514824404624281 - 0.48829262296803855 - 0.49853262011854643 - 0.48457750518082765 - 0.5020774116970983 - 0.5001897357021557 - 0.4702417082210781 - 0.4763216048226018 - 0.49932879417585735 - 0.5129628835129124 - 0.514824404624281 - 0.48829262296803855 - 0.49853262011854643 - 0.48457750518082765 - 0.5020774116970983 - 0.5001897357021557 - 0.4702417082210781 - 0.4763216048226018 - 0.49932879417585735 - 0.5129628835129124 - 0.514824404624281 - 0.48829262296803855 - 0.49853262011854643 - 0.48457750518082765 - 0.5020774116970983 - 0.5001897357021557 - 0.4702417082210781 - 0.4763216048226018 - 0.49932879417585735 - 0.5129628835129124 - 0.514824404624281 - 0.48829262296803855 - 0.49853262011854643 - 0.48457750518082765 - 0.5020774116970983 - 0.5001897357021557 - 0.4702417082210781 - 0.4763216048226018 - 0.49932879417585735 - 0.5129628835129124 - 0.514824404624281 - 0.48829262296803855 - 0.49853262011854643 - 0.48457750518082765 - 0.5020774116970983 - 0.5001897357021557 - 0.4702417082210781 - 0.4763216048226018 - 0.49932879417585735 - 0.5129628835129124 - 0.514824404624281 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: None config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 85.75430649102938 - type: cos_sim_ap value: 73.62842656477649 - type: cos_sim_f1 value: 67.76023680315738 - type: cos_sim_precision value: 63.61741547012506 - type: cos_sim_recall value: 72.4802110817942 - type: dot_accuracy value: 85.7423854085951 - type: dot_ap value: 73.59147637253723 - type: dot_f1 value: 67.69498693867396 - type: dot_precision value: 64.03859731701577 - type: dot_recall value: 71.79419525065963 - type: euclidean_accuracy value: 85.7423854085951 - type: euclidean_ap value: 73.6288990409654 - type: euclidean_f1 value: 67.80415430267064 - type: euclidean_precision value: 63.79711493718009 - type: euclidean_recall value: 72.34828496042216 - type: manhattan_accuracy value: 85.69470107885796 - type: manhattan_ap value: 73.49219614602531 - type: manhattan_f1 value: 67.60809797550613 - type: manhattan_precision value: 64.22127255460589 - type: manhattan_recall value: 71.37203166226914 - type: max_accuracy value: 85.75430649102938 - type: max_ap value: 73.6288990409654 - type: max_f1 value: 67.80415430267064 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: None config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.08293553770326 - type: cos_sim_ap value: 86.21246419992926 - type: cos_sim_f1 value: 78.49922526377924 - type: cos_sim_precision value: 75.35769939084857 - type: cos_sim_recall value: 81.9140745303357 - type: dot_accuracy value: 89.08681647067955 - type: dot_ap value: 86.19733517196862 - type: dot_f1 value: 78.51132446157838 - type: dot_precision value: 75.70233755093287 - type: dot_recall value: 81.53680320295658 - type: euclidean_accuracy value: 89.07517367175069 - type: euclidean_ap value: 86.21198725320203 - type: euclidean_f1 value: 78.49867139061116 - type: euclidean_precision value: 75.38276155372839 - type: euclidean_recall value: 81.88327687095781 - type: manhattan_accuracy value: 89.0538285403811 - type: manhattan_ap value: 86.17785515765131 - type: manhattan_f1 value: 78.48184098593084 - type: manhattan_precision value: 74.34396308285694 - type: manhattan_recall value: 83.10748383122882 - type: max_accuracy value: 89.08681647067955 - type: max_ap value: 86.21246419992926 - type: max_f1 value: 78.51132446157838 --- # mlx-community/nomicai-modernbert-embed-base-4bit The Model [mlx-community/nomicai-modernbert-embed-base-4bit](https://huggingface.co/mlx-community/nomicai-modernbert-embed-base-4bit) was converted to MLX format from [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) using mlx-lm version **0.0.3**. ## Use with mlx ```bash pip install mlx-embeddings ``` ```python from mlx_embeddings import load, generate import mlx.core as mx model, tokenizer = load("mlx-community/nomicai-modernbert-embed-base-4bit") # For text embeddings output = generate(model, processor, texts=["I like grapes", "I like fruits"]) embeddings = output.text_embeds # Normalized embeddings # Compute dot product between normalized embeddings similarity_matrix = mx.matmul(embeddings, embeddings.T) print("Similarity matrix between texts:") print(similarity_matrix) ```

withpi/pi_scorer_bert_v3_init_t_24000

withpi

2025-04-02T15:57:57Z

0

transformers

[ "transformers", "safetensors", "modernbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2025-04-02T15:57:22Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

grnr9730/wan2.1_i2v_480p_14B_fp8_scaled

grnr9730

2025-04-02T15:55:46Z

0

null

[ "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-04-02T14:03:56Z

--- license: apache-2.0 ---

efraimdahl/notagen_lieder_labled_fullft

efraimdahl

2025-04-02T15:55:33Z

0

transformers

[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-04-02T15:20:02Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Leyey/alexandre_delmar_LoRA

Leyey

2025-04-02T15:54:33Z

0

diffusers

[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]

text-to-image

2025-04-02T15:54:24Z

--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: a photo in alexandre delmar style widget: [] tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers ---  # SDXL LoRA DreamBooth - Leyey/alexandre_delmar_LoRA <Gallery /> ## Model description These are Leyey/alexandre_delmar_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo in alexandre delmar style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](Leyey/alexandre_delmar_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]

MindX-Deepu/ChatBot

MindX-Deepu

2025-04-02T15:54:28Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-04-02T15:54:27Z

--- license: apache-2.0 ---

coffiee/rs23

coffiee

2025-04-02T15:54:04Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-04-02T15:49:48Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

diamandislabii/4-tumor-compact-h5

diamandislabii

2025-04-02T15:53:21Z

0

keras

[ "keras", "region:us" ]

null

2025-04-02T15:22:54Z

--- library_name: keras --- This model has been uploaded using the Keras library and can be used with JAX, TensorFlow, and PyTorch backends. This model card has been generated automatically and should be completed by the model author. See [Model Cards documentation](https://huggingface.co/docs/hub/model-cards) for more information. For more details about the model architecture, check out [config.json](./config.json).

missbrittanyb/sonia-lora

missbrittanyb

2025-04-02T15:51:38Z

0

null

[ "license:other", "region:us" ]

null

2025-04-02T15:12:23Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---

mlx-community/answerdotai-ModernBERT-base-6bit

mlx-community

2025-04-02T15:50:49Z

0

transformers

[ "transformers", "safetensors", "modernbert", "fill-mask", "masked-lm", "long-context", "mlx", "en", "license:apache-2.0", "autotrain_compatible", "region:us" ]

fill-mask

2025-04-02T15:47:52Z

--- library_name: transformers license: apache-2.0 language: - en tags: - fill-mask - masked-lm - long-context - modernbert - mlx pipeline_tag: fill-mask inference: false --- # mlx-community/answerdotai-ModernBERT-base-6bit The Model [mlx-community/answerdotai-ModernBERT-base-6bit](https://huggingface.co/mlx-community/answerdotai-ModernBERT-base-6bit) was converted to MLX format from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) using mlx-lm version **0.0.3**. ## Use with mlx ```bash pip install mlx-embeddings ``` ```python from mlx_embeddings import load, generate import mlx.core as mx model, tokenizer = load("mlx-community/answerdotai-ModernBERT-base-6bit") # For text embeddings output = generate(model, processor, texts=["I like grapes", "I like fruits"]) embeddings = output.text_embeds # Normalized embeddings # Compute dot product between normalized embeddings similarity_matrix = mx.matmul(embeddings, embeddings.T) print("Similarity matrix between texts:") print(similarity_matrix) ```

kontol666/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hoarse_alert_woodpecker

kontol666

2025-04-02T15:50:43Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am hoarse alert woodpecker", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-04-02T15:50:18Z

--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hoarse_alert_woodpecker tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am hoarse alert woodpecker - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hoarse_alert_woodpecker This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="kontol666/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hoarse_alert_woodpecker", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.50.3 - Pytorch: 2.5.1 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

nik311/therapy-lora-hermes

nik311

2025-04-02T15:49:36Z

0

1

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-04-02T15:49:29Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

PotatoOff/mxbai-rerank-xsmall-v1-safetensors

PotatoOff

2025-04-02T15:48:13Z

5

0

transformers

[ "transformers", "safetensors", "deberta-v2", "text-classification", "reranker", "transformers.js", "sentence-transformers", "text-ranking", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-ranking

2024-09-03T06:00:20Z

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3.159 5.121 6.077 7.884 8.923 6.564 6.764 10.148 14.927 11.723 24.093l-20.594 4.067z"></path><path fill="#eb5e5b" d="M1117 536.549c-6.113-4.702-9.965-11.44-11.917-18.955-2.292-8.819-4.066-17.74-9.467-25.337-4.327-6.085-3.122-13.382-4.6-20.088l-4.55-21.241c-1.59-8.054-3.172-16.118-4.422-24.23l-5.037-36.129c6.382-1.43 12.777-2.462 19.582-3.443 1.906 11.646 3.426 23.24 4.878 34.842.307 2.453.717 4.973.477 7.402-1.86 18.84 2.834 36.934 5.347 55.352 1.474 10.806 4.885 20.848 7.101 31.302 1.394 6.579 1.774 13.374 2.609 20.523z"></path><path fill="#ec644b" d="M1263.638 290.071c4.697 2.713 9.183 5.458 13.45 8.509 17.199 12.295 33.158 25.836 43.873 44.907-8.026 4.725-16.095 9.106-24.83 13.372-11.633-15.937-25.648-28.515-41.888-38.689-1.609-1.008-3.555-1.48-5.344-2.2 2.329-3.852 4.766-7.645 6.959-11.573l7.78-14.326z"></path><path fill="#eb5f2d" d="M1372.453 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fill="#eb5a57" d="M1216.359 827.958c-4.331-3.733-8.603-7.379-12.326-11.518l-26.664-30.44c-.866-.989-1.89-1.839-3.152-2.902 6.483-6.054 13.276-11.959 20.371-18.005l39.315 44.704c-5.648 6.216-11.441 12.12-17.544 18.161z"></path><path fill="#ec6168" d="M1231.598 334.101l38.999 36.066c.931.876 1.456 2.183 2.303 3.608-4.283 4.279-8.7 8.24-13.769 12.091-4.2-3.051-7.512-6.349-11.338-8.867-12.36-8.136-22.893-18.27-32.841-29.093l16.646-13.805z"></path><path fill="#ed656e" d="M1214.597 347.955c10.303 10.775 20.836 20.908 33.196 29.044 3.825 2.518 7.137 5.816 10.992 8.903-3.171 4.397-6.65 8.648-10.432 13.046-6.785-5.184-13.998-9.858-19.529-16.038-4.946-5.527-9.687-8.644-17.309-8.215-2.616.147-5.734-2.788-8.067-4.923-3.026-2.769-5.497-6.144-8.35-9.568 6.286-4.273 12.715-8.237 19.499-12.25z"></path></svg> </p> <p align="center"> <b>The crispy rerank family from <a href="https://mixedbread.ai"><b>Mixedbread</b></a>.</b> </p> # mxbai-rerank-xsmall-v1 This is the smallest model in our family of powerful reranker models. You can learn more about the models in our [blog post](https://www.mixedbread.ai/blog/mxbai-rerank-v1). We have three models: - [mxbai-rerank-xsmall-v1](https://huggingface.co/mixedbread-ai/mxbai-rerank-xsmall-v1) (🍞) - [mxbai-rerank-base-v1](https://huggingface.co/mixedbread-ai/mxbai-rerank-base-v1) - [mxbai-rerank-large-v1](https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v1) ## Quickstart Currently, the best way to use our models is with the most recent version of sentence-transformers. `pip install -U sentence-transformers` Let's say you have a query, and you want to rerank a set of documents. You can do that with only one line of code: ```python from sentence_transformers import CrossEncoder # Load the model, here we use our base sized model model = CrossEncoder("mixedbread-ai/mxbai-rerank-xsmall-v1") # Example query and documents query = "Who wrote 'To Kill a Mockingbird'?" documents = [ "'To Kill a Mockingbird' is a novel by Harper Lee published in 1960. It was immediately successful, winning the Pulitzer Prize, and has become a classic of modern American literature.", "The novel 'Moby-Dick' was written by Herman Melville and first published in 1851. It is considered a masterpiece of American literature and deals with complex themes of obsession, revenge, and the conflict between good and evil.", "Harper Lee, an American novelist widely known for her novel 'To Kill a Mockingbird', was born in 1926 in Monroeville, Alabama. She received the Pulitzer Prize for Fiction in 1961.", "Jane Austen was an English novelist known primarily for her six major novels, which interpret, critique and comment upon the British landed gentry at the end of the 18th century.", "The 'Harry Potter' series, which consists of seven fantasy novels written by British author J.K. Rowling, is among the most popular and critically acclaimed books of the modern era.", "'The Great Gatsby', a novel written by American author F. Scott Fitzgerald, was published in 1925. The story is set in the Jazz Age and follows the life of millionaire Jay Gatsby and his pursuit of Daisy Buchanan." ] # Lets get the scores results = model.rank(query, documents, return_documents=True, top_k=3) ``` <details> <summary>JavaScript Example</summary> Install [transformers.js](https://github.com/xenova/transformers.js) `npm i @xenova/transformers` Let's say you have a query, and you want to rerank a set of documents. In JavaScript, you need to add a function: ```javascript import { AutoTokenizer, AutoModelForSequenceClassification } from '@xenova/transformers'; const model_id = 'mixedbread-ai/mxbai-rerank-xsmall-v1'; const model = await AutoModelForSequenceClassification.from_pretrained(model_id); const tokenizer = await AutoTokenizer.from_pretrained(model_id); /** * Performs ranking with the CrossEncoder on the given query and documents. Returns a sorted list with the document indices and scores. * @param {string} query A single query * @param {string[]} documents A list of documents * @param {Object} options Options for ranking * @param {number} [options.top_k=undefined] Return the top-k documents. If undefined, all documents are returned. * @param {number} [options.return_documents=false] If true, also returns the documents. If false, only returns the indices and scores. */ async function rank(query, documents, { top_k = undefined, return_documents = false, } = {}) { const inputs = tokenizer( new Array(documents.length).fill(query), { text_pair: documents, padding: true, truncation: true, } ) const { logits } = await model(inputs); return logits .sigmoid() .tolist() .map(([score], i) => ({ corpus_id: i, score, ...(return_documents ? { text: documents[i] } : {}) })) .sort((a, b) => b.score - a.score) .slice(0, top_k); } // Example usage: const query = "Who wrote 'To Kill a Mockingbird'?" const documents = [ "'To Kill a Mockingbird' is a novel by Harper Lee published in 1960. It was immediately successful, winning the Pulitzer Prize, and has become a classic of modern American literature.", "The novel 'Moby-Dick' was written by Herman Melville and first published in 1851. It is considered a masterpiece of American literature and deals with complex themes of obsession, revenge, and the conflict between good and evil.", "Harper Lee, an American novelist widely known for her novel 'To Kill a Mockingbird', was born in 1926 in Monroeville, Alabama. She received the Pulitzer Prize for Fiction in 1961.", "Jane Austen was an English novelist known primarily for her six major novels, which interpret, critique and comment upon the British landed gentry at the end of the 18th century.", "The 'Harry Potter' series, which consists of seven fantasy novels written by British author J.K. Rowling, is among the most popular and critically acclaimed books of the modern era.", "'The Great Gatsby', a novel written by American author F. Scott Fitzgerald, was published in 1925. The story is set in the Jazz Age and follows the life of millionaire Jay Gatsby and his pursuit of Daisy Buchanan." ] const results = await rank(query, documents, { return_documents: true, top_k: 3 }); console.log(results); ``` </details> ## Using API You can use the large model via our API as follows: ```python from mixedbread_ai.client import MixedbreadAI mxbai = MixedbreadAI(api_key="{MIXEDBREAD_API_KEY}") res = mxbai.reranking( model="mixedbread-ai/mxbai-rerank-large-v1", query="Who is the author of To Kill a Mockingbird?", input=[ "To Kill a Mockingbird is a novel by Harper Lee published in 1960. It was immediately successful, winning the Pulitzer Prize, and has become a classic of modern American literature.", "The novel Moby-Dick was written by Herman Melville and first published in 1851. It is considered a masterpiece of American literature and deals with complex themes of obsession, revenge, and the conflict between good and evil.", "Harper Lee, an American novelist widely known for her novel To Kill a Mockingbird, was born in 1926 in Monroeville, Alabama. She received the Pulitzer Prize for Fiction in 1961.", "Jane Austen was an English novelist known primarily for her six major novels, which interpret, critique and comment upon the British landed gentry at the end of the 18th century.", "The Harry Potter series, which consists of seven fantasy novels written by British author J.K. Rowling, is among the most popular and critically acclaimed books of the modern era.", "The Great Gatsby, a novel written by American author F. Scott Fitzgerald, was published in 1925. The story is set in the Jazz Age and follows the life of millionaire Jay Gatsby and his pursuit of Daisy Buchanan." ], top_k=3, return_input=false ) print(res.data) ``` The API comes with additional features, such as a continous trained reranker! Check out the [docs](https://www.mixedbread.ai/docs) for more information. ## Evaluation Our reranker models are designed to elevate your search. They work extremely well in combination with keyword search and can even outperform semantic search systems in many cases. | Model | NDCG@10 | Accuracy@3 | | ------------------------------------------------------------------------------------- | -------- | ---------- | | Lexical Search (Lucene) | 38.0 | 66.4 | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 41.6 | 66.9 | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 45.2 | 70.6 | | cohere-embed-v3 (semantic search) | 47.5 | 70.9 | | [mxbai-rerank-xsmall-v1](https://huggingface.co/mixedbread-ai/mxbai-rerank-xsmall-v1) | **43.9** | **70.0** | | [mxbai-rerank-base-v1](https://huggingface.co/mixedbread-ai/mxbai-rerank-base-v1) | **46.9** | **72.3** | | [mxbai-rerank-large-v1](https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v1) | **48.8** | **74.9** | The reported results are aggregated from 11 datasets of BEIR. We used [Pyserini](https://github.com/castorini/pyserini/) to evaluate the models. Find more in our [blog-post](https://www.mixedbread.ai/blog/mxbai-rerank-v1) and on this [spreadsheet](https://docs.google.com/spreadsheets/d/15ELkSMFv-oHa5TRiIjDvhIstH9dlc3pnZeO-iGz4Ld4/edit?usp=sharing). ## Community Please join our [Discord Community](https://discord.gg/jDfMHzAVfU) and share your feedback and thoughts! We are here to help and also always happy to chat. ## License Apache 2.0

jon-fernandes/whisper-small-100

jon-fernandes

2025-04-02T15:47:15Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-04-02T15:47:13Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Savoxism/ppo-LunarLander-v2

Savoxism

2025-04-02T15:46:18Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2025-04-02T15:45:39Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -23.84 +/- 16.96 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

KingEmpire/sn9_pre_c04_18

KingEmpire

2025-04-02T15:45:31Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-04-02T14:53:48Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Membersuger/miners_cp2_w32

Membersuger

2025-04-02T15:43:02Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-04-02T15:15:26Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

blackjack007/Qwen2-0.5B-GRPO-test

blackjack007

2025-04-02T15:43:00Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:AI-MO/NuminaMath-TIR", "arxiv:2402.03300", "base_model:Qwen/Qwen2-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2-0.5B-Instruct", "endpoints_compatible", "region:us" ]

null

2025-03-19T20:34:29Z

--- base_model: Qwen/Qwen2-0.5B-Instruct datasets: AI-MO/NuminaMath-TIR library_name: transformers model_name: Qwen2-0.5B-GRPO-test tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen2-0.5B-GRPO-test This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on the [AI-MO/NuminaMath-TIR](https://huggingface.co/datasets/AI-MO/NuminaMath-TIR) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="blackjack007/Qwen2-0.5B-GRPO-test", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.16.0 - Transformers: 4.50.2 - Pytorch: 2.6.0+cu124 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

Publishing/gemma-3

Publishing

2025-04-02T15:41:52Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3", "trl", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-04-02T15:41:38Z

--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** Publishing - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Membersuger/miners_cp2_w30

Membersuger

2025-04-02T15:41:34Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-04-02T15:15:11Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

pictgensupport/filmnoir

pictgensupport

2025-04-02T15:37:27Z

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-04-02T15:37:24Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: filmnoir --- # Filmnoir <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `filmnoir` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('pictgensupport/filmnoir', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)

Azimjoon/llama3-finetuned

Azimjoon

2025-04-02T15:34:17Z

0

peft

[ "peft", "safetensors", "generated_from_trainer", "base_model:openlm-research/open_llama_3b_v2", "base_model:adapter:openlm-research/open_llama_3b_v2", "license:apache-2.0", "region:us" ]

null

2025-04-02T15:34:14Z

--- library_name: peft license: apache-2.0 base_model: openlm-research/open_llama_3b_v2 tags: - generated_from_trainer model-index: - name: llama3-finetuned results: [] ---  # llama3-finetuned This model is a fine-tuned version of [openlm-research/open_llama_3b_v2](https://huggingface.co/openlm-research/open_llama_3b_v2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.14.0 - Transformers 4.50.2 - Pytorch 2.6.0+cu124 - Tokenizers 0.21.1

newtts2017/f56p2amz

newtts2017

2025-04-02T15:33:02Z

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-04-02T15:20:38Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: f56p2amz --- # F56P2Amz <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `f56p2amz` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "f56p2amz", "lora_weights": "https://huggingface.co/newtts2017/f56p2amz/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('newtts2017/f56p2amz', weight_name='lora.safetensors') image = pipeline('f56p2amz').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/newtts2017/f56p2amz/discussions) to add images that show off what you’ve made with this LoRA.

kostiantynk-outlook/c2eef299-8164-46d8-8b99-443a9c277f10

kostiantynk-outlook

2025-04-02T15:32:48Z

0

peft

[ "peft", "generated_from_trainer", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:adapter:Qwen/Qwen2.5-7B-Instruct", "region:us" ]

null

2025-04-02T15:30:49Z

--- library_name: peft tags: - generated_from_trainer base_model: Qwen/Qwen2.5-7B-Instruct model-index: - name: kostiantynk-outlook/c2eef299-8164-46d8-8b99-443a9c277f10 results: [] ---  # kostiantynk-outlook/c2eef299-8164-46d8-8b99-443a9c277f10 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5683 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

fpadovani/fr_clm_childes_30

fpadovani

2025-04-02T15:32:25Z

0

1

transformers

[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-03-25T21:50:38Z

--- library_name: transformers tags: - generated_from_trainer model-index: - name: child_30 results: [] ---  # child_30 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.3875 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 30 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 40000 - training_steps: 100000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:--------:|:------:|:---------------:| | No log | 2.1459 | 2000 | 6.2006 | | 6.0988 | 4.2918 | 4000 | 4.4864 | | 6.0988 | 6.4378 | 6000 | 4.0450 | | 3.809 | 8.5837 | 8000 | 3.7977 | | 3.809 | 10.7296 | 10000 | 3.6229 | | 3.368 | 12.8755 | 12000 | 3.4840 | | 3.368 | 15.0215 | 14000 | 3.3730 | | 3.1113 | 17.1674 | 16000 | 3.2718 | | 3.1113 | 19.3133 | 18000 | 3.1845 | | 2.916 | 21.4592 | 20000 | 3.1091 | | 2.916 | 23.6052 | 22000 | 3.0571 | | 2.7615 | 25.7511 | 24000 | 3.0031 | | 2.7615 | 27.8970 | 26000 | 2.9622 | | 2.6375 | 30.0429 | 28000 | 2.9277 | | 2.6375 | 32.1888 | 30000 | 2.9047 | | 2.5336 | 34.3348 | 32000 | 2.8888 | | 2.5336 | 36.4807 | 34000 | 2.8873 | | 2.4456 | 38.6266 | 36000 | 2.8729 | | 2.4456 | 40.7725 | 38000 | 2.8654 | | 2.3643 | 42.9185 | 40000 | 2.8761 | | 2.3643 | 45.0644 | 42000 | 2.8874 | | 2.2761 | 47.2103 | 44000 | 2.9046 | | 2.2761 | 49.3562 | 46000 | 2.9111 | | 2.1878 | 51.5021 | 48000 | 2.9330 | | 2.1878 | 53.6481 | 50000 | 2.9497 | | 2.1083 | 55.7940 | 52000 | 2.9701 | | 2.1083 | 57.9399 | 54000 | 2.9880 | | 2.0358 | 60.0858 | 56000 | 3.0221 | | 2.0358 | 62.2318 | 58000 | 3.0519 | | 1.9684 | 64.3777 | 60000 | 3.0712 | | 1.9684 | 66.5236 | 62000 | 3.0901 | | 1.9112 | 68.6695 | 64000 | 3.1114 | | 1.9112 | 70.8155 | 66000 | 3.1317 | | 1.8591 | 72.9614 | 68000 | 3.1540 | | 1.8591 | 75.1073 | 70000 | 3.1873 | | 1.8075 | 77.2532 | 72000 | 3.2064 | | 1.8075 | 79.3991 | 74000 | 3.2267 | | 1.7658 | 81.5451 | 76000 | 3.2442 | | 1.7658 | 83.6910 | 78000 | 3.2605 | | 1.7261 | 85.8369 | 80000 | 3.2768 | | 1.7261 | 87.9828 | 82000 | 3.2917 | | 1.6897 | 90.1288 | 84000 | 3.3144 | | 1.6897 | 92.2747 | 86000 | 3.3288 | | 1.6562 | 94.4206 | 88000 | 3.3447 | | 1.6562 | 96.5665 | 90000 | 3.3496 | | 1.6284 | 98.7124 | 92000 | 3.3609 | | 1.6284 | 100.8584 | 94000 | 3.3714 | | 1.6032 | 103.0043 | 96000 | 3.3779 | | 1.6032 | 105.1502 | 98000 | 3.3861 | | 1.5821 | 107.2961 | 100000 | 3.3875 | ### Framework versions - Transformers 4.45.2 - Pytorch 2.5.1+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

MarfinF/marfin_emotion

MarfinF

2025-04-02T15:31:47Z

351

0

transformers

[ "transformers", "safetensors", "deberta-v2", "text-classification", "mood", "emotion", "indonesian", "nli", "zero-shot-classification", "id", "en", "base_model:MoritzLaurer/mDeBERTa-v3-base-mnli-xnli", "base_model:finetune:MoritzLaurer/mDeBERTa-v3-base-mnli-xnli", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

zero-shot-classification

2025-03-25T01:21:22Z

--- license: apache-2.0 language: - id - en base_model: MoritzLaurer/mDeBERTa-v3-base-mnli-xnli pipeline_tag: zero-shot-classification tags: - mood - emotion - indonesian - nli metrics: - accuracy library_name: transformers --- # Marfin Emotion Detection Model 🎵 This model is fine-tuned from `MoritzLaurer/mDeBERTa-v3-base-mnli-xnli` for **emotion detection** tasks based on chat context, specifically optimized for **Indonesian and English**. ## 📝 Use Case The model predicts the relationship between user chat input and emotional hypotheses. It helps detect emotions like: - `senang` (happy) - `sedih` (sad) - `marah` (angry) - `takut` (fear) - `cinta` (love) This is useful for: - Emotion-based music recommendation - Sentiment analysis in real-time chat apps - AI-driven mood detection systems ## 📊 Training Details - Base model: mDeBERTa-v3-base-mnli-xnli - Fine-tuned with custom NLI-style dataset - Metrics: **Accuracy** ## 🏷 Tags `Zero-Shot Classification`, `Emotion`, `Mood`, `Indonesian`, `English` ## 📥 Example Usage ```python from transformers import pipeline classifier = pipeline("zero-shot-classification", model="MarfinF/marfin_emotion") text = "Aku lagi sedih banget hari ini" labels = ["senang", "sedih", "marah", "takut", "cinta"] result = classifier(text, candidate_labels=labels) print(result)

cafeburger/lora_model

cafeburger

2025-04-02T15:31:23Z

0

transformers

[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-04-02T15:28:46Z

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Membersuger/miners_cp2_w29

Membersuger

2025-04-02T15:31:14Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-04-02T15:14:51Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Membersuger/miners_cp2_w27

Membersuger

2025-04-02T15:30:33Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-04-02T15:14:36Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Felladrin/gguf-Q8_0-bge-reranker-v2-m3

Felladrin

2025-04-02T15:29:57Z

83

2

sentence-transformers

[ "sentence-transformers", "gguf", "transformers", "text-embeddings-inference", "llama-cpp", "gguf-my-repo", "text-ranking", "multilingual", "base_model:BAAI/bge-reranker-v2-m3", "base_model:quantized:BAAI/bge-reranker-v2-m3", "license:apache-2.0", "endpoints_compatible", "region:us", "feature-extraction" ]

text-ranking

2024-10-08T07:20:56Z

--- base_model: BAAI/bge-reranker-v2-m3 language: - multilingual license: apache-2.0 pipeline_tag: text-ranking tags: - transformers - sentence-transformers - text-embeddings-inference - llama-cpp - gguf-my-repo library_name: sentence-transformers --- # Felladrin/bge-reranker-v2-m3-Q8_0-GGUF This model was converted to GGUF format from [`BAAI/bge-reranker-v2-m3`](https://huggingface.co/BAAI/bge-reranker-v2-m3) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/BAAI/bge-reranker-v2-m3) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Felladrin/bge-reranker-v2-m3-Q8_0-GGUF --hf-file bge-reranker-v2-m3-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Felladrin/bge-reranker-v2-m3-Q8_0-GGUF --hf-file bge-reranker-v2-m3-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Felladrin/bge-reranker-v2-m3-Q8_0-GGUF --hf-file bge-reranker-v2-m3-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Felladrin/bge-reranker-v2-m3-Q8_0-GGUF --hf-file bge-reranker-v2-m3-q8_0.gguf -c 2048 ```

bowilleatyou/be487d5c-c04f-4129-800b-55f45acdab86

bowilleatyou

2025-04-02T15:29:37Z

0

transformers

[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-04-02T15:06:50Z

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

pictgensupport/folkart

pictgensupport

2025-04-02T15:29:07Z

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-04-02T15:29:05Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: folkart --- # Folkart <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `folkart` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('pictgensupport/folkart', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)

JennyGan/70b-MP20-7000

JennyGan

2025-04-02T15:27:57Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.1-70B-Instruct", "base_model:adapter:meta-llama/Llama-3.1-70B-Instruct", "region:us" ]

null

2025-04-02T15:26:29Z

--- base_model: meta-llama/Llama-3.1-70B-Instruct library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: QuantizationMethod.BITS_AND_BYTES - _load_in_8bit: False - _load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 - bnb_4bit_quant_storage: uint8 - load_in_4bit: True - load_in_8bit: False ### Framework versions - PEFT 0.6.0

DreadPoor/Irix_1.1-12B-Model_Stock-Q6_K-GGUF

DreadPoor

2025-04-02T15:27:56Z

0

transformers

[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:DreadPoor/Irix_1.1-12B-Model_Stock", "base_model:quantized:DreadPoor/Irix_1.1-12B-Model_Stock", "endpoints_compatible", "region:us", "conversational" ]

null

2025-04-02T15:27:13Z

--- base_model: DreadPoor/Irix_1.1-12B-Model_Stock library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # DreadPoor/Irix_1.1-12B-Model_Stock-Q6_K-GGUF This model was converted to GGUF format from [`DreadPoor/Irix_1.1-12B-Model_Stock`](https://huggingface.co/DreadPoor/Irix_1.1-12B-Model_Stock) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/DreadPoor/Irix_1.1-12B-Model_Stock) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo DreadPoor/Irix_1.1-12B-Model_Stock-Q6_K-GGUF --hf-file irix_1.1-12b-model_stock-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo DreadPoor/Irix_1.1-12B-Model_Stock-Q6_K-GGUF --hf-file irix_1.1-12b-model_stock-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo DreadPoor/Irix_1.1-12B-Model_Stock-Q6_K-GGUF --hf-file irix_1.1-12b-model_stock-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo DreadPoor/Irix_1.1-12B-Model_Stock-Q6_K-GGUF --hf-file irix_1.1-12b-model_stock-q6_k.gguf -c 2048 ```

albertus-sussex/veriscrape-sbert-camera-wo-ref-deepseek-chat

albertus-sussex

2025-04-02T15:26:53Z

0

sentence-transformers

[ "sentence-transformers", "safetensors", "new", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:6964", "loss:TripletLoss", "custom_code", "arxiv:1908.10084", "arxiv:1703.07737", "base_model:Alibaba-NLP/gte-base-en-v1.5", "base_model:finetune:Alibaba-NLP/gte-base-en-v1.5", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2025-04-02T15:26:35Z

--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:6964 - loss:TripletLoss base_model: Alibaba-NLP/gte-base-en-v1.5 widget: - source_sentence: $109.99 sentences: - model - $117.99 - Kodak EasyShare C195 14 Megapixel Compact Camera - Silver - price - source_sentence: Canon sentences: - price - $60.99 - manufacturer - Panasonic - source_sentence: $127.00 sentences: - Canon PowerShot D10 Point & Shoot Digital Camera - Blue - 16:9 - 3x Optical Zoom - 4x Digital Zoom - 2.5" Active Matrix TFT Color LCD - $311.00 - model - price - source_sentence: Canon Powershot A495 Point & Shoot Digital Camera - 10 Megapixel - 2.50"... sentences: - Canon, Inc - manufacturer - model - Panasonic Lumix Dmc-zs7 Point & Shoot Digital Camera - 12.1 Megapixel - 3"... - source_sentence: ': Bell + Howell' sentences: - ': Olympus' - Bell & Howell Z10T ZoomTouch 10MP Touchscreen Digital Camera with Movie Mode, 3x Optical Zoom Lens, 3.0" LCD Screen, USB 2.0 - Silver - manufacturer - model pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy - silhouette_cosine - silhouette_euclidean model-index: - name: SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5 results: - task: type: triplet name: Triplet dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy value: 0.9741601943969727 name: Cosine Accuracy - type: cosine_accuracy value: 0.9732558131217957 name: Cosine Accuracy - task: type: silhouette name: Silhouette dataset: name: Unknown type: unknown metrics: - type: silhouette_cosine value: 0.8823210597038269 name: Silhouette Cosine - type: silhouette_euclidean value: 0.7564524412155151 name: Silhouette Euclidean - type: silhouette_cosine value: 0.8747285008430481 name: Silhouette Cosine - type: silhouette_euclidean value: 0.7522649168968201 name: Silhouette Euclidean --- # SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5)  - **Maximum Sequence Length:** 32 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity    ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 32, 'do_lower_case': False}) with Transformer model: NewModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("albertus-sussex/veriscrape-sbert-camera-wo-ref-deepseek-chat") # Run inference sentences = [ ': Bell + Howell', ': Olympus', 'Bell & Howell Z10T ZoomTouch 10MP Touchscreen Digital Camera with Movie Mode, 3x Optical Zoom Lens, 3.0" LCD Screen, USB 2.0 - Silver', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ```    ## Evaluation ### Metrics #### Triplet * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:--------------------|:-----------| | **cosine_accuracy** | **0.9742** | #### Silhouette * Evaluated with <code>veriscrape.training.SilhouetteEvaluator</code> | Metric | Value | |:----------------------|:-----------| | **silhouette_cosine** | **0.8823** | | silhouette_euclidean | 0.7565 | #### Triplet * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:--------------------|:-----------| | **cosine_accuracy** | **0.9733** | #### Silhouette * Evaluated with <code>veriscrape.training.SilhouetteEvaluator</code> | Metric | Value | |:----------------------|:-----------| | **silhouette_cosine** | **0.8747** | | silhouette_euclidean | 0.7523 |   ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 6,964 training samples * Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, <code>pos_attr_name</code>, <code>neg_attr_name</code>, and <code>website_id</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | pos_attr_name | neg_attr_name | website_id | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | type | string | string | string | string | string | int | | details | <ul><li>min: 3 tokens</li><li>mean: 12.74 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 12.66 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 12.28 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.0 tokens</li><li>max: 3 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.0 tokens</li><li>max: 3 tokens</li></ul> | <ul><li>0: ~9.80%</li><li>1: ~9.00%</li><li>2: ~6.80%</li><li>3: ~11.00%</li><li>4: ~10.20%</li><li>5: ~9.60%</li><li>6: ~10.80%</li><li>7: ~10.70%</li><li>8: ~10.30%</li><li>9: ~11.80%</li></ul> | * Samples: | anchor | positive | negative | pos_attr_name | neg_attr_name | website_id | |:-----------------------------------------------------------|:---------------------------------------------------------------------------------------------------|:-----------------------|:--------------------------|:--------------------------|:---------------| | <code>Casio Computer Co., Ltd</code> | <code>Eastman Kodak Company</code> | <code>$324.99</code> | <code>manufacturer</code> | <code>price</code> | <code>9</code> | | <code>$188.99</code> | <code>$96.99</code> | <code>Panasonic</code> | <code>price</code> | <code>manufacturer</code> | <code>9</code> | | <code>GE J1250 Point & Shoot Digital Camera - Black</code> | <code>Pentax K-r 12.4 Megapixel Digital SLR Camera (Body with Lens Kit) - 18 mm-55 mm - Red</code> | <code>$299.00</code> | <code>model</code> | <code>price</code> | <code>2</code> | * Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 774 evaluation samples * Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, <code>pos_attr_name</code>, <code>neg_attr_name</code>, and <code>website_id</code> * Approximate statistics based on the first 774 samples: | | anchor | positive | negative | pos_attr_name | neg_attr_name | website_id | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | type | string | string | string | string | string | int | | details | <ul><li>min: 3 tokens</li><li>mean: 13.09 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 13.14 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 12.73 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.0 tokens</li><li>max: 3 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.0 tokens</li><li>max: 3 tokens</li></ul> | <ul><li>0: ~10.98%</li><li>1: ~11.11%</li><li>2: ~5.30%</li><li>3: ~10.59%</li><li>4: ~10.34%</li><li>5: ~9.04%</li><li>6: ~10.59%</li><li>7: ~10.85%</li><li>8: ~12.02%</li><li>9: ~9.17%</li></ul> | * Samples: | anchor | positive | negative | pos_attr_name | neg_attr_name | website_id | |:------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------|:--------------------------|:-------------------|:---------------| | <code>Eastman Kodak Company</code> | <code>Panasonic</code> | <code>Kodak EasyShare C143 12 Megapixel Compact Camera - Silver</code> | <code>manufacturer</code> | <code>model</code> | <code>9</code> | | <code>SL605 - Digital camera - compact - 12.2 Mpix - optical zoom: 5 x - supported memory: SD, SDHC - black (EC-SL605ZBPBUS)</code> | <code>EASYSHARE C195 - Digital camera - compact - 14.0 Mpix - optical zoom: 5 x - supported memory: SD, SDHC - silver (8770414)</code> | <code>$184.99</code> | <code>model</code> | <code>price</code> | <code>7</code> | | <code>$186.99</code> | <code>$96.99</code> | <code>10.2MP Cyber-shot TX7 Digital Camera - Red (DSCTX7/R)</code> | <code>price</code> | <code>model</code> | <code>7</code> | * Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `num_train_epochs`: 5 - `warmup_ratio`: 0.1 #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 5 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs | Epoch | Step | Training Loss | Validation Loss | cosine_accuracy | silhouette_cosine | |:-----:|:----:|:-------------:|:---------------:|:---------------:|:-----------------:| | -1 | -1 | - | - | 0.9057 | 0.3675 | | 1.0 | 55 | 0.3791 | 0.2219 | 0.9703 | 0.8847 | | 2.0 | 110 | 0.1751 | 0.2184 | 0.9703 | 0.8844 | | 3.0 | 165 | 0.1592 | 0.2365 | 0.9703 | 0.8832 | | 4.0 | 220 | 0.1416 | 0.2636 | 0.9742 | 0.8812 | | 5.0 | 275 | 0.1219 | 0.2560 | 0.9742 | 0.8823 | | -1 | -1 | - | - | 0.9733 | 0.8747 | ### Framework Versions - Python: 3.10.16 - Sentence Transformers: 4.0.1 - Transformers: 4.45.2 - PyTorch: 2.5.1+cu124 - Accelerate: 1.6.0 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### TripletLoss ```bibtex @misc{hermans2017defense, title={In Defense of the Triplet Loss for Person Re-Identification}, author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, year={2017}, eprint={1703.07737}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

oznuriscoding/rte-best-model

oznuriscoding

2025-04-02T15:25:19Z

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2025-04-02T15:25:02Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

albertus-sussex/veriscrape-sbert-camera-wo-ref-gpt-4o-mini

albertus-sussex

2025-04-02T15:24:41Z

0

sentence-transformers

[ "sentence-transformers", "safetensors", "new", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:7250", "loss:TripletLoss", "custom_code", "arxiv:1908.10084", "arxiv:1703.07737", "base_model:Alibaba-NLP/gte-base-en-v1.5", "base_model:finetune:Alibaba-NLP/gte-base-en-v1.5", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2025-04-02T15:24:19Z

--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:7250 - loss:TripletLoss base_model: Alibaba-NLP/gte-base-en-v1.5 widget: - source_sentence: $319.88 sentences: - Panasonic - price - manufacturer - $155.56 - source_sentence: Panasonic sentences: - model - International Innovations, Inc - manufacturer - Panasonic Lumix DMC-ZR1 Point & Shoot Digital Camera - Red - source_sentence: $89.00 sentences: - PW550 Compact Camera - model - $163.00 - price - source_sentence: Coolpix S4000 Compact Camera sentences: - model - Canon, Inc - manufacturer - Exilim EX-FS10 Compact Camera - source_sentence: Olympus sentences: - model - Olympus - manufacturer - Polycom pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy - silhouette_cosine - silhouette_euclidean model-index: - name: SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5 results: - task: type: triplet name: Triplet dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy value: 0.9714640378952026 name: Cosine Accuracy - type: cosine_accuracy value: 0.9642857313156128 name: Cosine Accuracy - task: type: silhouette name: Silhouette dataset: name: Unknown type: unknown metrics: - type: silhouette_cosine value: 0.8579733371734619 name: Silhouette Cosine - type: silhouette_euclidean value: 0.7253183722496033 name: Silhouette Euclidean - type: silhouette_cosine value: 0.8245989680290222 name: Silhouette Cosine - type: silhouette_euclidean value: 0.6966991424560547 name: Silhouette Euclidean --- # SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5)  - **Maximum Sequence Length:** 32 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity    ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 32, 'do_lower_case': False}) with Transformer model: NewModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("albertus-sussex/veriscrape-sbert-camera-wo-ref-gpt-4o-mini") # Run inference sentences = [ 'Olympus', 'Polycom', 'Olympus', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ```    ## Evaluation ### Metrics #### Triplet * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:--------------------|:-----------| | **cosine_accuracy** | **0.9715** | #### Silhouette * Evaluated with <code>veriscrape.training.SilhouetteEvaluator</code> | Metric | Value | |:----------------------|:----------| | **silhouette_cosine** | **0.858** | | silhouette_euclidean | 0.7253 | #### Triplet * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:--------------------|:-----------| | **cosine_accuracy** | **0.9643** | #### Silhouette * Evaluated with <code>veriscrape.training.SilhouetteEvaluator</code> | Metric | Value | |:----------------------|:-----------| | **silhouette_cosine** | **0.8246** | | silhouette_euclidean | 0.6967 |   ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 7,250 training samples * Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, <code>pos_attr_name</code>, <code>neg_attr_name</code>, and <code>website_id</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | pos_attr_name | neg_attr_name | website_id | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | type | string | string | string | string | string | int | | details | <ul><li>min: 3 tokens</li><li>mean: 10.67 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.73 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.78 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.0 tokens</li><li>max: 3 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.0 tokens</li><li>max: 3 tokens</li></ul> | <ul><li>0: ~8.70%</li><li>1: ~9.80%</li><li>2: ~9.30%</li><li>3: ~8.60%</li><li>4: ~11.60%</li><li>5: ~9.00%</li><li>6: ~10.70%</li><li>7: ~11.60%</li><li>8: ~10.50%</li><li>9: ~10.20%</li></ul> | * Samples: | anchor | positive | negative | pos_attr_name | neg_attr_name | website_id | |:---------------------|:---------------------|:------------------------------------------------|:--------------------------|:--------------------------|:---------------| | <code>$266.76</code> | <code>$166.96</code> | <code>Sony Corporation</code> | <code>price</code> | <code>manufacturer</code> | <code>1</code> | | <code>SAMSUNG</code> | <code>Kodak</code> | <code>SAMSUNG SL605 Black Digital Camera</code> | <code>manufacturer</code> | <code>model</code> | <code>6</code> | | <code>Kodak</code> | <code>Nikon</code> | <code>$67.99</code> | <code>manufacturer</code> | <code>price</code> | <code>4</code> | * Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 806 evaluation samples * Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, <code>pos_attr_name</code>, <code>neg_attr_name</code>, and <code>website_id</code> * Approximate statistics based on the first 806 samples: | | anchor | positive | negative | pos_attr_name | neg_attr_name | website_id | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | type | string | string | string | string | string | int | | details | <ul><li>min: 3 tokens</li><li>mean: 10.67 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.61 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.16 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.0 tokens</li><li>max: 3 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.0 tokens</li><li>max: 3 tokens</li></ul> | <ul><li>0: ~9.80%</li><li>1: ~9.68%</li><li>2: ~10.30%</li><li>3: ~9.80%</li><li>4: ~10.67%</li><li>5: ~9.93%</li><li>6: ~11.41%</li><li>7: ~7.94%</li><li>8: ~10.05%</li><li>9: ~10.42%</li></ul> | * Samples: | anchor | positive | negative | pos_attr_name | neg_attr_name | website_id | |:-------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------|:--------------------------|:--------------------------|:---------------| | <code>Canon PowerShot SD1400 IS 14.1 Megapixel Compact Camera - 5 mm-20 mm - Silver</code> | <code>International Innovations SS1000 5 Megapixel Compact Camera-6.50 mm</code> | <code>Canon, Inc</code> | <code>model</code> | <code>manufacturer</code> | <code>8</code> | | <code>FinePix A150 Compact Camera</code> | <code>Cyber-shot DSC-W350 Compact Camera</code> | <code>Nikon Corporation</code> | <code>model</code> | <code>manufacturer</code> | <code>9</code> | | <code>Sony</code> | <code>Nikon</code> | <code>New! SONY Cyber-shot DSC-H55 14.1MP Digital Camera Silver</code> | <code>manufacturer</code> | <code>model</code> | <code>0</code> | * Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `num_train_epochs`: 5 - `warmup_ratio`: 0.1 #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 5 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs | Epoch | Step | Training Loss | Validation Loss | cosine_accuracy | silhouette_cosine | |:-----:|:----:|:-------------:|:---------------:|:---------------:|:-----------------:| | -1 | -1 | - | - | 0.9057 | 0.3489 | | 1.0 | 57 | 0.3625 | 0.2596 | 0.9727 | 0.8412 | | 2.0 | 114 | 0.172 | 0.2100 | 0.9727 | 0.8578 | | 3.0 | 171 | 0.1677 | 0.2118 | 0.9715 | 0.8345 | | 4.0 | 228 | 0.1716 | 0.2267 | 0.9715 | 0.8541 | | 5.0 | 285 | 0.1578 | 0.2303 | 0.9715 | 0.8580 | | -1 | -1 | - | - | 0.9643 | 0.8246 | ### Framework Versions - Python: 3.10.16 - Sentence Transformers: 4.0.1 - Transformers: 4.45.2 - PyTorch: 2.5.1+cu124 - Accelerate: 1.6.0 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### TripletLoss ```bibtex @misc{hermans2017defense, title={In Defense of the Triplet Loss for Person Re-Identification}, author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, year={2017}, eprint={1703.07737}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

Jadhesh/jadhesh-long-hair

Jadhesh

2025-04-02T15:24:35Z

0

diffusers

[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "fluxgym", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-04-02T15:24:26Z

--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym base_model: black-forest-labs/FLUX.1-dev instance_prompt: JadLH2025 license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # Jadhesh Long Hair A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `JadLH2025` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc. Weights for this model are available in Safetensors format.

DreadPoor/Irixium-12B-Model_Stock-Q4_K_M-GGUF

DreadPoor

2025-04-02T15:24:00Z

0

transformers

[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:DreadPoor/Irixium-12B-Model_Stock", "base_model:quantized:DreadPoor/Irixium-12B-Model_Stock", "endpoints_compatible", "region:us", "conversational" ]

null

2025-04-02T15:23:26Z

--- base_model: DreadPoor/Irixium-12B-Model_Stock library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # DreadPoor/Irixium-12B-Model_Stock-Q4_K_M-GGUF This model was converted to GGUF format from [`DreadPoor/Irixium-12B-Model_Stock`](https://huggingface.co/DreadPoor/Irixium-12B-Model_Stock) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/DreadPoor/Irixium-12B-Model_Stock) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo DreadPoor/Irixium-12B-Model_Stock-Q4_K_M-GGUF --hf-file irixium-12b-model_stock-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo DreadPoor/Irixium-12B-Model_Stock-Q4_K_M-GGUF --hf-file irixium-12b-model_stock-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo DreadPoor/Irixium-12B-Model_Stock-Q4_K_M-GGUF --hf-file irixium-12b-model_stock-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo DreadPoor/Irixium-12B-Model_Stock-Q4_K_M-GGUF --hf-file irixium-12b-model_stock-q4_k_m.gguf -c 2048 ```

tomvoelker/gpt22gpt2-gpt2-medium-cnn-dailymail-seed42

tomvoelker

2025-04-02T15:22:39Z

141

0

transformers

[ "transformers", "tensorboard", "safetensors", "encoder-decoder", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2025-03-13T12:55:03Z

--- library_name: transformers tags: - generated_from_trainer metrics: - rouge model-index: - name: gpt22gpt2-gpt2-medium-cnn-dailymail-seed42 results: [] ---  # gpt22gpt2-gpt2-medium-cnn-dailymail-seed42 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7212 - Rouge1: 0.2011 - Rouge2: 0.0413 - Rougel: 0.1250 - Rougelsum: 0.1887 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:------:|:-----:|:---------------:|:------:|:------:|:------:|:---------:| | 2.8287 | 0.2229 | 2000 | 2.6199 | 0.1926 | 0.0340 | 0.1248 | 0.1800 | | 2.4721 | 0.4458 | 4000 | 2.2632 | 0.1971 | 0.0382 | 0.1284 | 0.1816 | | 2.2324 | 0.6687 | 6000 | 2.0400 | 0.2164 | 0.0487 | 0.1345 | 0.2025 | | 2.1025 | 0.8916 | 8000 | 1.9285 | 0.1827 | 0.0330 | 0.1175 | 0.1691 | | 1.8926 | 1.1145 | 10000 | 1.8595 | 0.1712 | 0.0288 | 0.1124 | 0.1598 | | 1.8605 | 1.3374 | 12000 | 1.8191 | 0.1919 | 0.0371 | 0.1230 | 0.1790 | | 1.8327 | 1.5603 | 14000 | 1.7931 | 0.1838 | 0.0346 | 0.1189 | 0.1724 | | 1.8209 | 1.7832 | 16000 | 1.7606 | 0.1847 | 0.0342 | 0.1169 | 0.1733 | | 1.7879 | 2.0061 | 18000 | 1.7484 | 0.1949 | 0.0390 | 0.1227 | 0.1830 | | 1.6658 | 2.2290 | 20000 | 1.7460 | 0.1910 | 0.0358 | 0.1201 | 0.1798 | | 1.6547 | 2.4519 | 22000 | 1.7386 | 0.1906 | 0.0360 | 0.1196 | 0.1791 | | 1.6426 | 2.6748 | 24000 | 1.7256 | 0.1953 | 0.0387 | 0.1226 | 0.1833 | | 1.6495 | 2.8977 | 26000 | 1.7212 | 0.2011 | 0.0413 | 0.1250 | 0.1887 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0 - Datasets 2.21.0 - Tokenizers 0.19.1

Membersuger/miners_cp2_w24

Membersuger

2025-04-02T15:21:03Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-04-02T15:14:13Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

sigridjineth/ko-reranker-v1.1

sigridjineth

2025-04-02T15:20:51Z

918

6

null

[ "safetensors", "new", "reranker", "korean", "text-ranking", "custom_code", "ko", "dataset:sigridjineth/korean_nli_dataset_reranker_v0", "arxiv:2407.19669", "arxiv:2312.15503", "arxiv:2402.03216", "base_model:Alibaba-NLP/gte-multilingual-reranker-base", "base_model:finetune:Alibaba-NLP/gte-multilingual-reranker-base", "license:apache-2.0", "region:us" ]

text-ranking

2024-12-11T06:26:50Z

--- license: apache-2.0 language: - ko base_model: - Alibaba-NLP/gte-multilingual-reranker-base datasets: - sigridjineth/korean_nli_dataset_reranker_v0 tags: - reranker - korean pipeline_tag: text-ranking --- # Model Card: sigridjineth/ko-reranker-v1.1 - The model, which is finetuned from [Alibaba-NLP/gte-multilingual-reranker-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-reranker-base) is currently under development and may undergo further changes as we refine and improve its performance. Underwent A100 x 8 with 12 hours for training. ## Training Data This model is trained on 328K Korean Triplets, which aggregates several publicly available datasets, ensuring rich linguistic diversity: - **kor_nli (train)**: [https://huggingface.co/datasets/kor_nli](https://huggingface.co/datasets/kor_nli) - **mnli_ko (train)**: [https://huggingface.co/datasets/kozistr/mnli_ko](https://huggingface.co/datasets/kozistr/mnli_ko) - **ko-wiki-reranking (train)**: [https://huggingface.co/datasets/upskyy/ko-wiki-reranking](https://huggingface.co/datasets/upskyy/ko-wiki-reranking) - **mr_tydi_korean (train)**: [https://huggingface.co/datasets/castorini/mr-tydi](https://huggingface.co/datasets/castorini/mr-tydi) - **klue_nli (train)**: [https://huggingface.co/datasets/klue/klue](https://huggingface.co/datasets/klue/klue) These combined resources ensure coverage across a wide range of topics, styles, and complexities in Korean language data, enabling the model to capture nuanced semantic differences. ## Key Features - **Hard Negative Mining**: Integrated [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) to mine challenging negatives. This approach sharpens the model’s ability to distinguish subtle contrasts, boosting robustness and improving ranking quality. - **Teacher-Student Distillation**: Leveraged [BAAI/bge-reranker-v2.5-gemma2-lightweight](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight) as a teacher model. The student reranker learned from teacher-provided positive/negative scores, accelerating convergence and achieving better final performance. ## Intended Use - **Search & Information Retrieval**: Improve document ranking for Korean-language search queries. - **Question Answering (QA)**: Enhance QA pipelines by reordering candidate answers for improved relevance. - **Content Recommendation**: Refine recommendation engines that rely on textual signals to deliver more accurate suggestions. ## Limitations & Future Work - **Preview Release**: The model is still in the refinement phase. Expect future updates to improve stability, generalization, and performance. - **Need for Evaluation**: Developing and standardizing benchmarks for generalized Korean retrieval tasks (especially for rerankers) will be an ongoing effort. ## Evaluation The [AutoRAG Benchmark](https://github.com/Marker-Inc-Korea/AutoRAG-example-korean-embedding-benchmark) serves as both the evaluation dataset and the toolkit for reporting these metrics. ### Model: `sigridjineth/ko-reranker-v1.1-preview` | top_k | Execution Time | F1 | Recall | Precision | MAP | MRR | NDCG | Is Best | |-------|----------------|--------|--------|-----------|--------|--------|--------|---------| | 1 | 0.0438 | 0.6754 | 0.6754 | 0.6754 | 0.6754 | 0.6754 | 0.6754 | True | | 3 | 0.0486 | 0.3684 | 0.7368 | 0.2456 | 0.7032 | 0.7032 | 0.7119 | False | | 5 | 0.0446 | 0.3684 | 0.7368 | 0.2456 | 0.7032 | 0.7032 | 0.7119 | False | --- ### Model: `Alibaba-NLP/gte-multilingual-reranker-base` | top_k | Execution Time | F1 | Recall | Precision | MAP | MRR | NDCG | Is Best | |-------|----------------|--------|--------|-----------|--------|--------|--------|---------| | 1 | 0.0481 | 0.6316 | 0.6316 | 0.6316 | 0.6316 | 0.6316 | 0.6316 | True | | 3 | 0.0427 | 0.3596 | 0.7193 | 0.2398 | 0.6725 | 0.6725 | 0.6846 | False | | 5 | 0.0442 | 0.3596 | 0.7193 | 0.2398 | 0.6725 | 0.6725 | 0.6846 | False | --- ### Model: `dragonkue/bge-reranker-v2-m3-ko` | top_k | Execution Time | F1 | Recall | Precision | MAP | MRR | NDCG | Is Best | |-------|----------------|--------|--------|-----------|--------|--------|--------|---------| | 1 | 0.0814 | 0.6930 | 0.6930 | 0.6930 | 0.6930 | 0.6930 | 0.6930 | True | | 3 | 0.0813 | 0.3596 | 0.7193 | 0.2398 | 0.7061 | 0.7061 | 0.7096 | False | | 5 | 0.0824 | 0.3596 | 0.7193 | 0.2398 | 0.7061 | 0.7061 | 0.7096 | False | ``` Evaluation Results (k=1,3,5,10): Accuracy@1: 0.8070 F1@1: 0.8070 Recall@1: 0.8070 Precision@1: 0.8070 Accuracy@3: 0.9211 F1@3: 0.4605 Recall@3: 0.9211 Precision@3: 0.3070 Accuracy@5: 0.9474 F1@5: 0.3158 Recall@5: 0.9474 Precision@5: 0.1895 Accuracy@10: 0.9737 F1@10: 0.1770 Recall@10: 0.9737 Precision@10: 0.0974 Total inference time (all queries): 142.64 sec Average inference time (per query): 1.2512 sec ``` ## Usage (transformers>=4.36.0) ```python import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer model_name_or_path = "sigridjineth/ko-reranker-v1.1-preview" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) model = AutoModelForSequenceClassification.from_pretrained( model_name_or_path, trust_remote_code=True, torch_dtype=torch.float16 ) model.eval() pairs = [ ["중국의 수도는","베이징"], ["2024년 대한민국 대통령은?", "대한민국 대통령은 윤석열이다"], ["파이썬에서 퀵 소트를 구현하기","quick sort로 코테 1등 먹어보자"] ] with torch.no_grad(): inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) scores = model(**inputs, return_dict=True).logits.view(-1, ).float() print(scores) # Example output: # tensor([1.2315, 0.5923, 0.3041]) ``` ## Usage with Infinity [Infinity](https://github.com/michaelfeil/infinity) is an MIT-licensed inference REST API server that can easily host and serve models. For instance: ```bash docker run --gpus all -v $PWD/data:/app/.cache -p "7997":"7997" \ michaelf34/infinity:0.0.68 \ v2 --model-id Alibaba-NLP/gte-multilingual-reranker-base --revision "main" \ --dtype bfloat16 --batch-size 32 --device cuda --engine torch --port 7997 ``` ## References ``` @misc{zhang2024mgtegeneralizedlongcontexttext, title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval}, author={Xin Zhang and Yanzhao Zhang and Dingkun Long and Wen Xie and Ziqi Dai and Jialong Tang and Huan Lin and Baosong Yang and Pengjun Xie and Fei Huang and Meishan Zhang and Wenjie Li and Min Zhang}, year={2024}, eprint={2407.19669}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2407.19669}, } @misc{li2023making, title={Making Large Language Models A Better Foundation For Dense Retrieval}, author={Chaofan Li and Zheng Liu and Shitao Xiao and Yingxia Shao}, year={2023}, eprint={2312.15503}, archivePrefix={arXiv}, primaryClass={cs.CL} } @misc{chen2024bge, title={BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation}, author={Jianlv Chen and Shitao Xiao and Peitian Zhang and Kun Luo and Defu Lian and Zheng Liu}, year={2024}, eprint={2402.03216}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

pictgensupport/goth

pictgensupport

2025-04-02T15:20:34Z

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-04-02T15:20:32Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: goth --- # Goth <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `goth` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('pictgensupport/goth', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)

optimum/legal-bert-base-uncased-neuronx

optimum

2025-04-02T15:20:06Z

6

0

transformers

[ "transformers", "bert", "pretraining", "license:cc-by-sa-4.0", "endpoints_compatible", "region:us" ]

null

2023-06-06T12:45:07Z

--- license: cc-by-sa-4.0 --- This repo contains artifacts from `nlpaueb/legal-bert-base-uncased` but in neuronx format compatible with INF2 and TRN1 devices. ```py from transformers import AutoTokenizer from optimum.neuron import NeuronBertForMaskedLM input_shapes = {"batch_size": 1, "sequence_length": 128} compiler_args = {"auto_cast": "matmul", "auto_cast_type": "bf16"} neuron_model = NeuronBertForMaskedLM.from_pretrained( "nlpaueb/legal-bert-base-uncased", export=True, **input_shapes, **compiler_args, ) # Save locally neuron_model.save_pretrained("legal_bert_masked_lm_neuronx") neuron_model.push_to_hub( "legal_bert_masked_lm_neuronx", repository_id="optimum/legal-bert-base-uncased-neuronx", # Replace with your HF Hub repo id ) ```

MaestrAI/character-lora-1743606802

MaestrAI

2025-04-02T15:19:13Z

0

null

[ "region:us" ]

null

2025-04-02T15:13:21Z

# character LORA Model This is a LORA model for character character Created at 2025-04-02 17:13:23

duncanlou/ddpm-celebahq-finetuned-butterflies-2epochs

duncanlou

2025-04-02T15:18:26Z

0

diffusers

[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2025-04-02T15:18:06Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) Describe your model here ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('duncanlou/ddpm-celebahq-finetuned-butterflies-2epochs') image = pipeline().images[0] image ```

DreadPoor/Irixium-12B-Model_Stock-Q6_K-GGUF

DreadPoor

2025-04-02T15:18:14Z

0

transformers

[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:DreadPoor/Irixium-12B-Model_Stock", "base_model:quantized:DreadPoor/Irixium-12B-Model_Stock", "endpoints_compatible", "region:us", "conversational" ]

null

2025-04-02T15:17:30Z

--- base_model: DreadPoor/Irixium-12B-Model_Stock library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # DreadPoor/Irixium-12B-Model_Stock-Q6_K-GGUF This model was converted to GGUF format from [`DreadPoor/Irixium-12B-Model_Stock`](https://huggingface.co/DreadPoor/Irixium-12B-Model_Stock) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/DreadPoor/Irixium-12B-Model_Stock) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo DreadPoor/Irixium-12B-Model_Stock-Q6_K-GGUF --hf-file irixium-12b-model_stock-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo DreadPoor/Irixium-12B-Model_Stock-Q6_K-GGUF --hf-file irixium-12b-model_stock-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo DreadPoor/Irixium-12B-Model_Stock-Q6_K-GGUF --hf-file irixium-12b-model_stock-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo DreadPoor/Irixium-12B-Model_Stock-Q6_K-GGUF --hf-file irixium-12b-model_stock-q6_k.gguf -c 2048 ```

RichardErkhov/thusinh1969_-_gemma2-2b-rerank-epoch2.0-awq

RichardErkhov

2025-04-02T15:18:07Z

4

0

null

[ "safetensors", "gemma2", "text-ranking", "arxiv:1910.09700", "4-bit", "awq", "region:us" ]

text-ranking

2025-01-06T18:16:56Z

--- pipeline_tag: text-ranking --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) gemma2-2b-rerank-epoch2.0 - AWQ - Model creator: https://huggingface.co/thusinh1969/ - Original model: https://huggingface.co/thusinh1969/gemma2-2b-rerank-epoch2.0/ Original model description: --- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

RichardErkhov/mixedbread-ai_-_mxbai-rerank-large-v2-8bits

RichardErkhov

2025-04-02T15:18:03Z

3

0

null

[ "safetensors", "qwen2", "text-ranking", "8-bit", "bitsandbytes", "region:us" ]

text-ranking

2025-03-22T21:35:52Z

--- pipeline_tag: text-ranking --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mxbai-rerank-large-v2 - bnb 8bits - Model creator: https://huggingface.co/mixedbread-ai/ - Original model: https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v2/ Original model description: --- library_name: transformers license: apache-2.0 language: - en - zh - de - ja - ko - es - fr - ar - bn - ru - id - sw - te - th --- <br><br> <p align="center"> <svg xmlns="http://www.w3.org/2000/svg" xml:space="preserve" viewBox="0 0 2020 1130" width="150" height="150" aria-hidden="true"><path fill="#e95a0f" d="M398.167 621.992c-1.387-20.362-4.092-40.739-3.851-61.081.355-30.085 6.873-59.139 21.253-85.976 10.487-19.573 24.09-36.822 40.662-51.515 16.394-14.535 34.338-27.046 54.336-36.182 15.224-6.955 31.006-12.609 47.829-14.168 11.809-1.094 23.753-2.514 35.524-1.836 23.033 1.327 45.131 7.255 66.255 16.75 16.24 7.3 31.497 16.165 45.651 26.969 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fill="#eb5a57" d="M1216.359 827.958c-4.331-3.733-8.603-7.379-12.326-11.518l-26.664-30.44c-.866-.989-1.89-1.839-3.152-2.902 6.483-6.054 13.276-11.959 20.371-18.005l39.315 44.704c-5.648 6.216-11.441 12.12-17.544 18.161z"></path><path fill="#ec6168" d="M1231.598 334.101l38.999 36.066c.931.876 1.456 2.183 2.303 3.608-4.283 4.279-8.7 8.24-13.769 12.091-4.2-3.051-7.512-6.349-11.338-8.867-12.36-8.136-22.893-18.27-32.841-29.093l16.646-13.805z"></path><path fill="#ed656e" d="M1214.597 347.955c10.303 10.775 20.836 20.908 33.196 29.044 3.825 2.518 7.137 5.816 10.992 8.903-3.171 4.397-6.65 8.648-10.432 13.046-6.785-5.184-13.998-9.858-19.529-16.038-4.946-5.527-9.687-8.644-17.309-8.215-2.616.147-5.734-2.788-8.067-4.923-3.026-2.769-5.497-6.144-8.35-9.568 6.286-4.273 12.715-8.237 19.499-12.25z"></path></svg> </p> <p align="center"> <b>The crispy rerank family from <a href="https://mixedbread.com"><b>Mixedbread</b></a>.</b> </p> <p align="center"> <sup> 🍞 Looking for a simple end-to-end retrieval solution? Meet Omni, our multimodal and multilingual model. <a href="https://mixedbread.com"><b>Get in touch for access.</a> </sup> </p> # 🍞 mxbai-rerank-large-v2 This is the large model in our family of powerful reranker models. You can learn more about the models in our [blog post](https://www.mixedbread.ai/blog/mxbai-rerank-v2). We have two models: - [mxbai-rerank-base-v2](https://huggingface.co/mixedbread-ai/mxbai-rerank-base-v2) - [mxbai-rerank-large-v2](https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v2) (🍞) **The technical report is coming soon!** ## 🌟 Features - state-of-the-art performance and strong efficiency - multilingual support (100+ languages, outstanding English and Chinese performance) - code support - long-context support ## ⚙️ Usage 1. Install mxbai-rerank ```bash pip install mxbai-rerank ``` 2. Inference ```python from mxbai_rerank import MxbaiRerankV2 model = MxbaiRerankV2("mixedbread-ai/mxbai-rerank-large-v2") query = "Who wrote 'To Kill a Mockingbird'?" documents = [ "'To Kill a Mockingbird' is a novel by Harper Lee published in 1960. It was immediately successful, winning the Pulitzer Prize, and has become a classic of modern American literature.", "The novel 'Moby-Dick' was written by Herman Melville and first published in 1851. It is considered a masterpiece of American literature and deals with complex themes of obsession, revenge, and the conflict between good and evil.", "Harper Lee, an American novelist widely known for her novel 'To Kill a Mockingbird', was born in 1926 in Monroeville, Alabama. She received the Pulitzer Prize for Fiction in 1961.", "Jane Austen was an English novelist known primarily for her six major novels, which interpret, critique and comment upon the British landed gentry at the end of the 18th century.", "The 'Harry Potter' series, which consists of seven fantasy novels written by British author J.K. Rowling, is among the most popular and critically acclaimed books of the modern era.", "'The Great Gatsby', a novel written by American author F. Scott Fitzgerald, was published in 1925. The story is set in the Jazz Age and follows the life of millionaire Jay Gatsby and his pursuit of Daisy Buchanan." ] # Lets get the scores results = model.rank(query, documents, return_documents=True, top_k=3) print(results) ``` ## Performance ### Benchmark Results | Model | BEIR Avg | Multilingual | Chinese | Code Search | Latency (s) | |-------|----------|----------|----------|--------------|-------------| | mxbai-rerank-large-v2 | 57.49 | 29.79 | 84.16 | 32.05 | 0.89 | | mxbai-rerank-base-v2 | 55.57 | 28.56 | 83.70 | 31.73 | 0.67 | | mxbai-rerank-large-v1 | 49.32 | 21.88 | 72.53 | 30.72 | 2.24 | *Latency measured on A100 GPU ## Training Details The models were trained using a three-step process: 1. **GRPO (Guided Reinforcement Prompt Optimization)** 2. **Contrastive Learning** 3. **Preference Learning** For more details, check our [technical blog post](https://mixedbread.com/blog/mxbai-rerank-v2). Paper following soon. ## 🎓 Citation ```bibtex @online{v2rerank2025mxbai, title={Baked-in Brilliance: Reranking Meets RL with mxbai-rerank-v2}, author={Sean Lee and Rui Huang and Aamir Shakir and Julius Lipp}, year={2025}, url={https://www.mixedbread.com/blog/mxbai-rerank-v2}, } ```

RichardErkhov/mixedbread-ai_-_mxbai-rerank-base-v2-4bits

RichardErkhov

2025-04-02T15:17:58Z

3

0

null

[ "safetensors", "qwen2", "text-ranking", "4-bit", "bitsandbytes", "region:us" ]

text-ranking

2025-03-21T15:59:32Z

--- pipeline_tag: text-ranking --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mxbai-rerank-base-v2 - bnb 4bits - Model creator: https://huggingface.co/mixedbread-ai/ - Original model: https://huggingface.co/mixedbread-ai/mxbai-rerank-base-v2/ Original model description: --- library_name: transformers language: - en - zh - de - ja - ko - es - fr - ar - bn - ru - id - sw - te - th license: apache-2.0 --- <br><br> <p align="center"> <svg xmlns="http://www.w3.org/2000/svg" xml:space="preserve" viewBox="0 0 2020 1130" width="150" height="150" aria-hidden="true"><path fill="#e95a0f" d="M398.167 621.992c-1.387-20.362-4.092-40.739-3.851-61.081.355-30.085 6.873-59.139 21.253-85.976 10.487-19.573 24.09-36.822 40.662-51.515 16.394-14.535 34.338-27.046 54.336-36.182 15.224-6.955 31.006-12.609 47.829-14.168 11.809-1.094 23.753-2.514 35.524-1.836 23.033 1.327 45.131 7.255 66.255 16.75 16.24 7.3 31.497 16.165 45.651 26.969 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fill="#eb5a57" d="M1216.359 827.958c-4.331-3.733-8.603-7.379-12.326-11.518l-26.664-30.44c-.866-.989-1.89-1.839-3.152-2.902 6.483-6.054 13.276-11.959 20.371-18.005l39.315 44.704c-5.648 6.216-11.441 12.12-17.544 18.161z"></path><path fill="#ec6168" d="M1231.598 334.101l38.999 36.066c.931.876 1.456 2.183 2.303 3.608-4.283 4.279-8.7 8.24-13.769 12.091-4.2-3.051-7.512-6.349-11.338-8.867-12.36-8.136-22.893-18.27-32.841-29.093l16.646-13.805z"></path><path fill="#ed656e" d="M1214.597 347.955c10.303 10.775 20.836 20.908 33.196 29.044 3.825 2.518 7.137 5.816 10.992 8.903-3.171 4.397-6.65 8.648-10.432 13.046-6.785-5.184-13.998-9.858-19.529-16.038-4.946-5.527-9.687-8.644-17.309-8.215-2.616.147-5.734-2.788-8.067-4.923-3.026-2.769-5.497-6.144-8.35-9.568 6.286-4.273 12.715-8.237 19.499-12.25z"></path></svg> </p> <p align="center"> <b>The crispy rerank family from <a href="https://mixedbread.ai"><b>Mixedbread</b></a>.</b> </p> <p align="center"> <sup> 🍞 Looking for a simple end-to-end retrieval solution? Meet Omni, our multimodal and multilingual model. <a href="https://mixedbread.com"><b>Get in touch for access.</a> </sup> </p> # 🍞 mxbai-rerank-base-v2 This is the base model in our family of powerful reranker models. You can learn more about the models in our [blog post](https://www.mixedbread.ai/blog/mxbai-rerank-v2). We have two models: - [mxbai-rerank-base-v2](https://huggingface.co/mixedbread-ai/mxbai-rerank-base-v2) (🍞) - [mxbai-rerank-large-v2](https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v2) **The technical report is coming soon!** ## 🌟 Features - state-of-the-art performance and strong efficiency - multilingual support (100+ languages, outstanding English and Chinese performance) - code support - long-context support ## ⚙️ Usage 1. Install mxbai-rerank ```bash pip install mxbai-rerank ``` 2. Inference ```python from mxbai_rerank import MxbaiRerankV2 model = MxbaiRerankV2("mixedbread-ai/mxbai-rerank-base-v2") query = "Who wrote 'To Kill a Mockingbird'?" documents = [ "'To Kill a Mockingbird' is a novel by Harper Lee published in 1960. It was immediately successful, winning the Pulitzer Prize, and has become a classic of modern American literature.", "The novel 'Moby-Dick' was written by Herman Melville and first published in 1851. It is considered a masterpiece of American literature and deals with complex themes of obsession, revenge, and the conflict between good and evil.", "Harper Lee, an American novelist widely known for her novel 'To Kill a Mockingbird', was born in 1926 in Monroeville, Alabama. She received the Pulitzer Prize for Fiction in 1961.", "Jane Austen was an English novelist known primarily for her six major novels, which interpret, critique and comment upon the British landed gentry at the end of the 18th century.", "The 'Harry Potter' series, which consists of seven fantasy novels written by British author J.K. Rowling, is among the most popular and critically acclaimed books of the modern era.", "'The Great Gatsby', a novel written by American author F. Scott Fitzgerald, was published in 1925. The story is set in the Jazz Age and follows the life of millionaire Jay Gatsby and his pursuit of Daisy Buchanan." ] # Lets get the scores results = model.rank(query, documents, return_documents=True, top_k=3) print(results) ``` ## Performance ### Benchmark Results | Model | BEIR Avg | Multilingual | Chinese | Code Search | Latency (s) | |-------|----------|----------|----------|--------------|-------------| | mxbai-rerank-large-v2 | 57.49 | 29.79 | 84.16 | 32.05 | 0.89 | | mxbai-rerank-base-v2 | 55.57 | 28.56 | 83.70 | 31.73 | 0.67 | | mxbai-rerank-large-v1 | 49.32 | 21.88 | 72.53 | 30.72 | 2.24 | *Latency measured on A100 GPU ## Training Details The models were trained using a three-step process: 1. **GRPO (Guided Reinforcement Prompt Optimization)** 2. **Contrastive Learning** 3. **Preference Learning** For more details, check our [technical blog post](https://mixedbread.com/blog/mxbai-rerank-v2). Paper following soon. ## 🎓 Citation ```bibtex @online{v2rerank2025mxbai, title={Baked-in Brilliance: Reranking Meets RL with mxbai-rerank-v2}, author={Sean Lee and Rui Huang and Aamir Shakir and Julius Lipp}, year={2025}, url={https://www.mixedbread.com/blog/mxbai-rerank-v2}, } ```

RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf

RichardErkhov

2025-04-02T15:17:57Z

366

0

null

[ "gguf", "text-ranking", "endpoints_compatible", "region:us", "conversational" ]

text-ranking

2025-03-14T03:32:42Z

--- pipeline_tag: text-ranking --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) reranker_0.5_cont_filt_7max - GGUF - Model creator: https://huggingface.co/lightblue/ - Original model: https://huggingface.co/lightblue/reranker_0.5_cont_filt_7max/ | Name | Quant method | Size | | ---- | ---- | ---- | | [reranker_0.5_cont_filt_7max.Q2_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q2_K.gguf) | Q2_K | 0.39GB | | [reranker_0.5_cont_filt_7max.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.IQ3_XS.gguf) | IQ3_XS | 0.39GB | | [reranker_0.5_cont_filt_7max.IQ3_S.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.IQ3_S.gguf) | IQ3_S | 0.39GB | | [reranker_0.5_cont_filt_7max.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q3_K_S.gguf) | Q3_K_S | 0.39GB | | [reranker_0.5_cont_filt_7max.IQ3_M.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.IQ3_M.gguf) | IQ3_M | 0.39GB | | [reranker_0.5_cont_filt_7max.Q3_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q3_K.gguf) | Q3_K | 0.4GB | | [reranker_0.5_cont_filt_7max.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q3_K_M.gguf) | Q3_K_M | 0.4GB | | [reranker_0.5_cont_filt_7max.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q3_K_L.gguf) | Q3_K_L | 0.42GB | | [reranker_0.5_cont_filt_7max.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.IQ4_XS.gguf) | IQ4_XS | 0.4GB | | [reranker_0.5_cont_filt_7max.Q4_0.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q4_0.gguf) | Q4_0 | 0.4GB | | [reranker_0.5_cont_filt_7max.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.IQ4_NL.gguf) | IQ4_NL | 0.4GB | | [reranker_0.5_cont_filt_7max.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q4_K_S.gguf) | Q4_K_S | 0.45GB | | [reranker_0.5_cont_filt_7max.Q4_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q4_K.gguf) | Q4_K | 0.46GB | | [reranker_0.5_cont_filt_7max.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q4_K_M.gguf) | Q4_K_M | 0.46GB | | [reranker_0.5_cont_filt_7max.Q4_1.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q4_1.gguf) | Q4_1 | 0.43GB | | [reranker_0.5_cont_filt_7max.Q5_0.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q5_0.gguf) | Q5_0 | 0.46GB | | [reranker_0.5_cont_filt_7max.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q5_K_S.gguf) | Q5_K_S | 0.48GB | | [reranker_0.5_cont_filt_7max.Q5_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q5_K.gguf) | Q5_K | 0.49GB | | [reranker_0.5_cont_filt_7max.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q5_K_M.gguf) | Q5_K_M | 0.49GB | | [reranker_0.5_cont_filt_7max.Q5_1.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q5_1.gguf) | Q5_1 | 0.49GB | | [reranker_0.5_cont_filt_7max.Q6_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q6_K.gguf) | Q6_K | 0.61GB | | [reranker_0.5_cont_filt_7max.Q8_0.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont_filt_7max-gguf/blob/main/reranker_0.5_cont_filt_7max.Q8_0.gguf) | Q8_0 | 0.63GB | Original model description: --- library_name: transformers license: other base_model: Qwen/Qwen2.5-0.5B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: reranker_continuous_filt_max7_train results: [] ---  # reranker_continuous_filt_max7_train This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the reranker_continuous_filt_max7_train dataset. It achieves the following results on the evaluation set: - Loss: 0.3869 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 8 - total_eval_batch_size: 8 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 1.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:-----:|:---------------:| | 0.403 | 0.1000 | 1977 | 0.4783 | | 0.5192 | 0.2000 | 3954 | 0.4524 | | 0.3639 | 0.3000 | 5931 | 0.4370 | | 0.4343 | 0.4000 | 7908 | 0.4286 | | 0.3929 | 0.5000 | 9885 | 0.4163 | | 0.4455 | 0.6000 | 11862 | 0.4040 | | 0.3775 | 0.7000 | 13839 | 0.3947 | | 0.3629 | 0.8000 | 15816 | 0.3898 | | 0.5186 | 0.9000 | 17793 | 0.3872 | ### Framework versions - Transformers 4.46.1 - Pytorch 2.4.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

RichardErkhov/lightblue_-_reranker_0.5_cont-gguf

RichardErkhov

2025-04-02T15:17:55Z

372

0

null

[ "gguf", "text-ranking", "endpoints_compatible", "region:us", "conversational" ]

text-ranking

2025-03-14T03:34:59Z

--- pipeline_tag: text-ranking --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) reranker_0.5_cont - GGUF - Model creator: https://huggingface.co/lightblue/ - Original model: https://huggingface.co/lightblue/reranker_0.5_cont/ | Name | Quant method | Size | | ---- | ---- | ---- | | [reranker_0.5_cont.Q2_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q2_K.gguf) | Q2_K | 0.39GB | | [reranker_0.5_cont.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.IQ3_XS.gguf) | IQ3_XS | 0.39GB | | [reranker_0.5_cont.IQ3_S.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.IQ3_S.gguf) | IQ3_S | 0.39GB | | [reranker_0.5_cont.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q3_K_S.gguf) | Q3_K_S | 0.39GB | | [reranker_0.5_cont.IQ3_M.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.IQ3_M.gguf) | IQ3_M | 0.39GB | | [reranker_0.5_cont.Q3_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q3_K.gguf) | Q3_K | 0.4GB | | [reranker_0.5_cont.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q3_K_M.gguf) | Q3_K_M | 0.4GB | | [reranker_0.5_cont.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q3_K_L.gguf) | Q3_K_L | 0.42GB | | [reranker_0.5_cont.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.IQ4_XS.gguf) | IQ4_XS | 0.4GB | | [reranker_0.5_cont.Q4_0.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q4_0.gguf) | Q4_0 | 0.4GB | | [reranker_0.5_cont.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.IQ4_NL.gguf) | IQ4_NL | 0.4GB | | [reranker_0.5_cont.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q4_K_S.gguf) | Q4_K_S | 0.45GB | | [reranker_0.5_cont.Q4_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q4_K.gguf) | Q4_K | 0.46GB | | [reranker_0.5_cont.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q4_K_M.gguf) | Q4_K_M | 0.46GB | | [reranker_0.5_cont.Q4_1.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q4_1.gguf) | Q4_1 | 0.43GB | | [reranker_0.5_cont.Q5_0.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q5_0.gguf) | Q5_0 | 0.46GB | | [reranker_0.5_cont.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q5_K_S.gguf) | Q5_K_S | 0.48GB | | [reranker_0.5_cont.Q5_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q5_K.gguf) | Q5_K | 0.49GB | | [reranker_0.5_cont.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q5_K_M.gguf) | Q5_K_M | 0.49GB | | [reranker_0.5_cont.Q5_1.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q5_1.gguf) | Q5_1 | 0.49GB | | [reranker_0.5_cont.Q6_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q6_K.gguf) | Q6_K | 0.61GB | | [reranker_0.5_cont.Q8_0.gguf](https://huggingface.co/RichardErkhov/lightblue_-_reranker_0.5_cont-gguf/blob/main/reranker_0.5_cont.Q8_0.gguf) | Q8_0 | 0.63GB | Original model description: --- library_name: transformers license: other base_model: Qwen/Qwen2.5-0.5B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: reranker_continuous_train results: [] ---  # reranker_continuous_train This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the reranker_continuous_train dataset. It achieves the following results on the evaluation set: - Loss: 0.3195 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 8 - total_eval_batch_size: 8 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 1.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.4242 | 0.1 | 2156 | 0.3844 | | 0.236 | 0.2 | 4312 | 0.3643 | | 0.6602 | 0.3 | 6468 | 0.3521 | | 0.3464 | 0.4 | 8624 | 0.3472 | | 0.3598 | 0.5 | 10780 | 0.3412 | | 0.3377 | 0.6 | 12936 | 0.3341 | | 0.4547 | 0.7 | 15092 | 0.3258 | | 0.2282 | 0.8 | 17248 | 0.3228 | | 0.2692 | 0.9 | 19404 | 0.3195 | | 0.2059 | 1.0 | 21560 | 0.3195 | ### Framework versions - Transformers 4.46.1 - Pytorch 2.4.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf

RichardErkhov

2025-04-02T15:17:51Z

307

0

null

[ "gguf", "text-ranking", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

text-ranking

2025-03-02T03:46:01Z

--- pipeline_tag: text-ranking --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) SmolLM2-135M-Bebop-Reranker - GGUF - Model creator: https://huggingface.co/jbaron34/ - Original model: https://huggingface.co/jbaron34/SmolLM2-135M-Bebop-Reranker/ | Name | Quant method | Size | | ---- | ---- | ---- | | [SmolLM2-135M-Bebop-Reranker.Q2_K.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q2_K.gguf) | Q2_K | 0.08GB | | [SmolLM2-135M-Bebop-Reranker.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.IQ3_XS.gguf) | IQ3_XS | 0.08GB | | [SmolLM2-135M-Bebop-Reranker.IQ3_S.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.IQ3_S.gguf) | IQ3_S | 0.08GB | | [SmolLM2-135M-Bebop-Reranker.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q3_K_S.gguf) | Q3_K_S | 0.08GB | | [SmolLM2-135M-Bebop-Reranker.IQ3_M.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.IQ3_M.gguf) | IQ3_M | 0.08GB | | [SmolLM2-135M-Bebop-Reranker.Q3_K.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q3_K.gguf) | Q3_K | 0.09GB | | [SmolLM2-135M-Bebop-Reranker.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q3_K_M.gguf) | Q3_K_M | 0.09GB | | [SmolLM2-135M-Bebop-Reranker.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q3_K_L.gguf) | Q3_K_L | 0.09GB | | [SmolLM2-135M-Bebop-Reranker.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.IQ4_XS.gguf) | IQ4_XS | 0.09GB | | [SmolLM2-135M-Bebop-Reranker.Q4_0.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q4_0.gguf) | Q4_0 | 0.09GB | | [SmolLM2-135M-Bebop-Reranker.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.IQ4_NL.gguf) | IQ4_NL | 0.09GB | | [SmolLM2-135M-Bebop-Reranker.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q4_K_S.gguf) | Q4_K_S | 0.1GB | | [SmolLM2-135M-Bebop-Reranker.Q4_K.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q4_K.gguf) | Q4_K | 0.1GB | | [SmolLM2-135M-Bebop-Reranker.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q4_K_M.gguf) | Q4_K_M | 0.1GB | | [SmolLM2-135M-Bebop-Reranker.Q4_1.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q4_1.gguf) | Q4_1 | 0.09GB | | [SmolLM2-135M-Bebop-Reranker.Q5_0.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q5_0.gguf) | Q5_0 | 0.1GB | | [SmolLM2-135M-Bebop-Reranker.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q5_K_S.gguf) | Q5_K_S | 0.1GB | | [SmolLM2-135M-Bebop-Reranker.Q5_K.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q5_K.gguf) | Q5_K | 0.1GB | | [SmolLM2-135M-Bebop-Reranker.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q5_K_M.gguf) | Q5_K_M | 0.1GB | | [SmolLM2-135M-Bebop-Reranker.Q5_1.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q5_1.gguf) | Q5_1 | 0.1GB | | [SmolLM2-135M-Bebop-Reranker.Q6_K.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q6_K.gguf) | Q6_K | 0.13GB | | [SmolLM2-135M-Bebop-Reranker.Q8_0.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q8_0.gguf) | Q8_0 | 0.13GB | Original model description: --- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf

RichardErkhov

2025-04-02T15:17:50Z

352

0

null

[ "gguf", "text-ranking", "endpoints_compatible", "region:us" ]

text-ranking

2025-03-13T14:32:23Z

--- pipeline_tag: text-ranking --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Qwen2.5-0.5b-bebop-reranker-newer-small - GGUF - Model creator: https://huggingface.co/jbaron34/ - Original model: https://huggingface.co/jbaron34/Qwen2.5-0.5b-bebop-reranker-newer-small/ | Name | Quant method | Size | | ---- | ---- | ---- | | [Qwen2.5-0.5b-bebop-reranker-newer-small.Q2_K.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q2_K.gguf) | Q2_K | 0.32GB | | [Qwen2.5-0.5b-bebop-reranker-newer-small.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.IQ3_XS.gguf) | IQ3_XS | 0.32GB | | [Qwen2.5-0.5b-bebop-reranker-newer-small.IQ3_S.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.IQ3_S.gguf) | IQ3_S | 0.32GB | | [Qwen2.5-0.5b-bebop-reranker-newer-small.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q3_K_S.gguf) | Q3_K_S | 0.32GB | | [Qwen2.5-0.5b-bebop-reranker-newer-small.IQ3_M.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.IQ3_M.gguf) | IQ3_M | 0.32GB | | [Qwen2.5-0.5b-bebop-reranker-newer-small.Q3_K.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q3_K.gguf) | Q3_K | 0.33GB | | [Qwen2.5-0.5b-bebop-reranker-newer-small.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q3_K_M.gguf) | Q3_K_M | 0.33GB | | [Qwen2.5-0.5b-bebop-reranker-newer-small.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q3_K_L.gguf) | Q3_K_L | 0.34GB | | [Qwen2.5-0.5b-bebop-reranker-newer-small.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.IQ4_XS.gguf) | IQ4_XS | 0.33GB | | [Qwen2.5-0.5b-bebop-reranker-newer-small.Q4_0.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q4_0.gguf) | Q4_0 | 0.33GB | | [Qwen2.5-0.5b-bebop-reranker-newer-small.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.IQ4_NL.gguf) | IQ4_NL | 0.33GB | | [Qwen2.5-0.5b-bebop-reranker-newer-small.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q4_K_S.gguf) | Q4_K_S | 0.36GB | | [Qwen2.5-0.5b-bebop-reranker-newer-small.Q4_K.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q4_K.gguf) | Q4_K | 0.37GB | | [Qwen2.5-0.5b-bebop-reranker-newer-small.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q4_K_M.gguf) | Q4_K_M | 0.37GB | | [Qwen2.5-0.5b-bebop-reranker-newer-small.Q4_1.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q4_1.gguf) | Q4_1 | 0.35GB | | [Qwen2.5-0.5b-bebop-reranker-newer-small.Q5_0.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q5_0.gguf) | Q5_0 | 0.37GB | | [Qwen2.5-0.5b-bebop-reranker-newer-small.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q5_K_S.gguf) | Q5_K_S | 0.38GB | | [Qwen2.5-0.5b-bebop-reranker-newer-small.Q5_K.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q5_K.gguf) | Q5_K | 0.39GB | | [Qwen2.5-0.5b-bebop-reranker-newer-small.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q5_K_M.gguf) | Q5_K_M | 0.39GB | | [Qwen2.5-0.5b-bebop-reranker-newer-small.Q5_1.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q5_1.gguf) | Q5_1 | 0.39GB | | [Qwen2.5-0.5b-bebop-reranker-newer-small.Q6_K.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q6_K.gguf) | Q6_K | 0.47GB | | [Qwen2.5-0.5b-bebop-reranker-newer-small.Q8_0.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf/blob/main/Qwen2.5-0.5b-bebop-reranker-newer-small.Q8_0.gguf) | Q8_0 | 0.49GB | Original model description: --- base_model: unsloth/qwen2.5-0.5b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** jbaron34 - **License:** apache-2.0 - **Finetuned from model :** unsloth/qwen2.5-0.5b-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

RichardErkhov/cnmoro_-_TinyLlama-ContextQuestionPair-Classifier-Reranker-4bits

RichardErkhov

2025-04-02T15:17:38Z

3

0

null

[ "safetensors", "llama", "text-ranking", "4-bit", "bitsandbytes", "region:us" ]

text-ranking

2025-03-22T17:10:48Z

--- pipeline_tag: text-ranking --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) TinyLlama-ContextQuestionPair-Classifier-Reranker - bnb 4bits - Model creator: https://huggingface.co/cnmoro/ - Original model: https://huggingface.co/cnmoro/TinyLlama-ContextQuestionPair-Classifier-Reranker/ Original model description: --- license: cc-by-nc-2.0 language: - en - pt tags: - classification - llama - tinyllama - rag - rerank --- ```python template = """<s><|system|> You are a chatbot who always responds in JSON format indicating if the context contains relevant information to answer the question</s> <|user|> Context: {Text} Question: {Prompt}</s> <|assistant|> """ # Output should be: {"relevant": true} # or {"relevant": false} ``` Example: ```text <s><|system|> You are a chatbot who always responds in JSON format indicating if the context contains relevant information to answer the question</s> <|user|> Context: old. NFT were observed in almost all patients over 60 years of age, but the incidence was low. Many ubiquitin-positive small-sized granules were observed in the second and third layer of the parahippocampal gyrus of aged patients, and the incidence rose with increasing age. On the other hand, few of these granules were in patients with Alzheimer\'s type dementia. Granulovacuolar degeneration was examined. Many centrally-located granules were positive for ubiquitin. Based on electron microscopic observation of these granules at several stages, the granules were thought to be a type of autophagosome. During the first stage of granulovacuolar degeneration, electron-dense materials appeared in the cytoplasm, following which they were surrounded by smooth cytoplasm, following which they were surrounded by smooth endoplasmic reticulum. Analytical electron microscopy disclosed that the granules contained some aluminium. Several senile changes in the central nervous system in cadavers were examined. The pattern of extension of Alzheimer\'s neurofibrillary tangles (NFT) and senile plaques (SP) in the olfactory bulbs of 100 specimens was examined during routine autopsy by immunohistochemical staining. NFT were first observed in the anterior olfactory nucleus after the age of 60, and incidence rose with increasing age. Senile plaques were found in the nucleus when there were many SP in the cerebral cortex. Of 25 non-demented amyotrophic lateral sclerosis patients, SP were found in the cerebral cortices of 10, and 9 of 10 were over 60 years old. NFT were observed in almost all patients over Question: What is granulovacuolar degeneration and what was its observation on electron microscopy?</s> <|assistant|> {"relevant": true}</s> ``` vLLM recommended request parameters: ```python prompt = "<s><|system|>\nYou are a chatbot who always responds in JSON format indicating if the context contains relevant information to answer the question</s>\n<|user|>\nContext:\nConhecida como missão de imagem de raios-x e espectroscopia (da sigla em inglês XRISM), a estratégia é utilizar o telescópio para ampliar os estudos da humanidade a níveis celestiais com uma fração dos pixels da tela de um Gameboy original, lançado em 1989. Isso é possível por meio de uma ferramenta chamada “Resolve”. Apesar de utilizar a medição em pixels, a tecnologia é bastante diferente de uma câmera. Com um conjunto de microcalorímetros de seis pixels quadrados que mede 0,5 cm², ela detecta a temperatura de cada raio-x que o atinge. Como funciona o Resolve do telescópio XRISM? Cientista do projeto XRISM da NASA, Brian Williams explicou em um comunicado o funcionamento do telescópio. “Chamamos o Resolve de espectrômetro de microcalorímetros porque cada um de seus 36 pixels está medindo pequenas quantidades de calor entregues por cada raio-x recebido, nos permitindo ver as impressões digitais químicas dos elementos que compõem as fontes com detalhes sem precedentes”.\n\nQuestion:\nQual é a sigla em alemão mencionada?</s>\n<|assistant|>\n{\"relevant\":" headers = { "Accept": "text/event-stream", "Authorization": "Bearer EMPTY" } body = { "model": model, "prompt": [prompt], "best_of": 5, "max_tokens": 1, "temperature": 0, "top_p": 1, "use_beam_search": True, "top_k": -1, "min_p": 0, "repetition_penalty": 1, "length_penalty": 1, "min_tokens": 1, "logprobs": 1 } result = requests.post(base_uri, headers=headers, json=body) result = result.json() boolean_response = bool(eval(json_result['choices'][0]['text'].strip().title())) print(boolean_response) ```

RichardErkhov/castorini_-_rank_zephyr_7b_v1_full-4bits

RichardErkhov

2025-04-02T15:17:34Z

3

0

null

[ "safetensors", "mistral", "text-ranking", "arxiv:2312.02724", "4-bit", "bitsandbytes", "region:us" ]

text-ranking

2025-03-19T04:37:56Z

--- pipeline_tag: text-ranking --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) rank_zephyr_7b_v1_full - bnb 4bits - Model creator: https://huggingface.co/castorini/ - Original model: https://huggingface.co/castorini/rank_zephyr_7b_v1_full/ Original model description: --- tags: - generated_from_trainer license: mit language: - en base_model: mistralai/Mistral-7B-v0.1 ---  <img src="https://huggingface.co/castorini/rank_zephyr_7b_v1_full/resolve/main/thumbnail.jpeg" alt="RankZephyr Logo" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/>  # Model Card for RankZephyr 7B V1 - Full RankZephyr is a series of language models trained to act as helpful reranking assistants built on the Zephyr-7B-β model. RankZephyr Base is the model that follows single-stage fine-tuning on the RankGPT-3.5 model, while RankZephyr Full is the model that is further fine-tuned on RankGPT-4 reorderings of OpenAI's Ada2 orderings for 5K queries. ## Model description - **Model type:** A 7B parameter GPT-like model initially fine-tuned on a mix of publicly available, synthetic datasets, followed by task-specific listwise reranking data. - **Language(s) (NLP):** Primarily English - **License:** MIT - **Fine-tuned from model:** [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) ### Model Sources  - **Repository:** https://github.com/castorini/rank_llm - **Paper:** https://arxiv.org/abs/2312.02724 ## Effectiveness At the time of release, RankZephyr-7B-Full is the state-of-the-art open-source reranking model on various datasets like DL19/20/21/22 and TREC-COVID and TREC-News. With the MS MARCO v1 collection: | Model | Size | First Stage | DL19 | DL20| |-------------|-----|----|---------------|--------------| | **RankZephyr-7b-v1-full-rho** 🪁 | **7B** | **SPLADE++ ED** | **0.7855** | **0.8255** | | **RankZephyr-7b-v1-full** 🪁 | **7B** | **SPLADE++ ED** | **0.7803** | **0.8211** | | RankGPT-4 (PSC) | -| SPLADE++ ED | 0.7601 | 0.7514 | | RankGPT-4 | -| SPLADE++ ED | 0.7464 | 0.7076 | | **RankZephyr-7b-v1-base** 🪁 | **7B** | **SPLADE++ ED** | **0.7341** | **0.7213** | | RankGPT-3.5 | -| SPLADE++ ED | 0.7504 | 0.7120| More details can be found in the paper. ## Intended uses & limitations The model is to be used in conjunction with the [RankLLM repository](https://github.com/castorini/rank_llm). While `rank-llm` exists as a PyPI package, we are currently in the early stages of development and encourage users to directly check install from source. The original Zephyr model is trained for chat. In our case, RankZephyr is fine-tuned to act as a listwise reranking agent. You provide it with a query and documents and get back a reordered list of document identifiers. ## Bias, Risks, and Limitations The following is an excerpt from the [Zephyr-7B-β model card](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta/blob/main/README.md#bias-risks--limitations):  > Zephyr-7B-β has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base model (`mistralai/Mistral-7B-v0.1`), however it is likely to have included a mix of Web data and technical sources like books and code. See the [Falcon 180B model card](https://huggingface.co/tiiuae/falcon-180B#training-data) for an example of this. Our model is trained specifically on monolingual English data, effectiveness on multilingual sets is not guaranteed. ## Citation If you find RankZephyr is useful in your work, please cite the following paper: ``` @ARTICLE{pradeep2023rankzephyr, title = {{RankZephyr}: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!}, author = {Ronak Pradeep and Sahel Sharifymoghaddam and Jimmy Lin}, year = {2023}, journal = {arXiv:2312.02724} } ```

RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf

RichardErkhov

2025-04-02T15:17:14Z

1,713

1

sentence-transformers

[ "sentence-transformers", "gguf", "text-ranking", "arxiv:2312.15503", "arxiv:2402.03216", "endpoints_compatible", "region:us" ]

text-ranking

2024-10-07T13:46:52Z

--- library_name: sentence-transformers pipeline_tag: text-ranking --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) bge-reranker-v2-gemma - GGUF - Model creator: https://huggingface.co/BAAI/ - Original model: https://huggingface.co/BAAI/bge-reranker-v2-gemma/ | Name | Quant method | Size | | ---- | ---- | ---- | | [bge-reranker-v2-gemma.Q2_K.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q2_K.gguf) | Q2_K | 1.08GB | | [bge-reranker-v2-gemma.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.IQ3_XS.gguf) | IQ3_XS | 1.16GB | | [bge-reranker-v2-gemma.IQ3_S.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.IQ3_S.gguf) | IQ3_S | 1.2GB | | [bge-reranker-v2-gemma.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q3_K_S.gguf) | Q3_K_S | 1.2GB | | [bge-reranker-v2-gemma.IQ3_M.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.IQ3_M.gguf) | IQ3_M | 1.22GB | | [bge-reranker-v2-gemma.Q3_K.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q3_K.gguf) | Q3_K | 1.29GB | | [bge-reranker-v2-gemma.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q3_K_M.gguf) | Q3_K_M | 1.29GB | | [bge-reranker-v2-gemma.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q3_K_L.gguf) | Q3_K_L | 1.36GB | | [bge-reranker-v2-gemma.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.IQ4_XS.gguf) | IQ4_XS | 1.4GB | | [bge-reranker-v2-gemma.Q4_0.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q4_0.gguf) | Q4_0 | 1.44GB | | [bge-reranker-v2-gemma.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.IQ4_NL.gguf) | IQ4_NL | 1.45GB | | [bge-reranker-v2-gemma.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q4_K_S.gguf) | Q4_K_S | 1.45GB | | [bge-reranker-v2-gemma.Q4_K.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q4_K.gguf) | Q4_K | 1.52GB | | [bge-reranker-v2-gemma.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q4_K_M.gguf) | Q4_K_M | 1.52GB | | [bge-reranker-v2-gemma.Q4_1.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q4_1.gguf) | Q4_1 | 1.56GB | | [bge-reranker-v2-gemma.Q5_0.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q5_0.gguf) | Q5_0 | 1.68GB | | [bge-reranker-v2-gemma.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q5_K_S.gguf) | Q5_K_S | 1.68GB | | [bge-reranker-v2-gemma.Q5_K.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q5_K.gguf) | Q5_K | 1.71GB | | [bge-reranker-v2-gemma.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q5_K_M.gguf) | Q5_K_M | 1.71GB | | [bge-reranker-v2-gemma.Q5_1.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q5_1.gguf) | Q5_1 | 1.79GB | | [bge-reranker-v2-gemma.Q6_K.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q6_K.gguf) | Q6_K | 1.92GB | | [bge-reranker-v2-gemma.Q8_0.gguf](https://huggingface.co/RichardErkhov/BAAI_-_bge-reranker-v2-gemma-gguf/blob/main/bge-reranker-v2-gemma.Q8_0.gguf) | Q8_0 | 2.49GB | Original model description: --- license: apache-2.0 pipeline_tag: text-classification tags: - transformers - sentence-transformers language: - multilingual --- # Reranker **More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/tree/master).** - [Model List](#model-list) - [Usage](#usage) - [Fine-tuning](#fine-tune) - [Evaluation](#evaluation) - [Citation](#citation) Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. And the score can be mapped to a float value in [0,1] by sigmoid function. ## Model List | Model | Base model | Language | layerwise | feature | |:--------------------------------------------------------------------------|:--------:|:-----------------------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:| | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) | Chinese and English | - | Lightweight reranker model, easy to deploy, with fast inference. | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | [xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) | Chinese and English | - | Lightweight reranker model, easy to deploy, with fast inference. | | [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) | [bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | - | Lightweight reranker model, possesses strong multilingual capabilities, easy to deploy, with fast inference. | | [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma) | [gemma-2b](https://huggingface.co/google/gemma-2b) | Multilingual | - | Suitable for multilingual contexts, performs well in both English proficiency and multilingual capabilities. | | [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise) | [MiniCPM-2B-dpo-bf16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16) | Multilingual | 8-40 | Suitable for multilingual contexts, performs well in both English and Chinese proficiency, allows freedom to select layers for output, facilitating accelerated inference. | You can select the model according your senario and resource. - For **multilingual**, utilize [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) and [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma) - For **Chinese or English**, utilize [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) and [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise). - For **efficiency**, utilize [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) and the low layer of [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise). - For better performance, recommand [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise) and [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma) ## Usage ### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` #### For normal reranker (bge-reranker-base / bge-reranker-large / bge-reranker-v2-m3 ) Get relevance scores (higher scores indicate more relevance): ```python from FlagEmbedding import FlagReranker reranker = FlagReranker('BAAI/bge-reranker-v2-m3', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation score = reranker.compute_score(['query', 'passage']) print(score) # -5.65234375 # You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score score = reranker.compute_score(['query', 'passage'], normalize=True) print(score) # 0.003497010252573502 scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]) print(scores) # [-8.1875, 5.26171875] # You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']], normalize=True) print(scores) # [0.00027803096387751553, 0.9948403768236574] ``` #### For LLM-based reranker ```python from FlagEmbedding import FlagLLMReranker reranker = FlagLLMReranker('BAAI/bge-reranker-v2-gemma', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation # reranker = FlagLLMReranker('BAAI/bge-reranker-v2-gemma', use_bf16=True) # You can also set use_bf16=True to speed up computation with a slight performance degradation score = reranker.compute_score(['query', 'passage']) print(score) scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]) print(scores) ``` #### For LLM-based layerwise reranker ```python from FlagEmbedding import LayerWiseFlagLLMReranker reranker = LayerWiseFlagLLMReranker('BAAI/bge-reranker-v2-minicpm-layerwise', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation # reranker = LayerWiseFlagLLMReranker('BAAI/bge-reranker-v2-minicpm-layerwise', use_bf16=True) # You can also set use_bf16=True to speed up computation with a slight performance degradation score = reranker.compute_score(['query', 'passage'], cutoff_layers=[28]) # Adjusting 'cutoff_layers' to pick which layers are used for computing the score. print(score) scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']], cutoff_layers=[28]) print(scores) ``` ### Using Huggingface transformers #### For normal reranker (bge-reranker-base / bge-reranker-large / bge-reranker-v2-m3 ) Get relevance scores (higher scores indicate more relevance): ```python import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-m3') model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-v2-m3') model.eval() pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] with torch.no_grad(): inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) scores = model(**inputs, return_dict=True).logits.view(-1, ).float() print(scores) ``` #### For LLM-based reranker ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer def get_inputs(pairs, tokenizer, prompt=None, max_length=1024): if prompt is None: prompt = "Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'." sep = "\n" prompt_inputs = tokenizer(prompt, return_tensors=None, add_special_tokens=False)['input_ids'] sep_inputs = tokenizer(sep, return_tensors=None, add_special_tokens=False)['input_ids'] inputs = [] for query, passage in pairs: query_inputs = tokenizer(f'A: {query}', return_tensors=None, add_special_tokens=False, max_length=max_length * 3 // 4, truncation=True) passage_inputs = tokenizer(f'B: {passage}', return_tensors=None, add_special_tokens=False, max_length=max_length, truncation=True) item = tokenizer.prepare_for_model( [tokenizer.bos_token_id] + query_inputs['input_ids'], sep_inputs + passage_inputs['input_ids'], truncation='only_second', max_length=max_length, padding=False, return_attention_mask=False, return_token_type_ids=False, add_special_tokens=False ) item['input_ids'] = item['input_ids'] + sep_inputs + prompt_inputs item['attention_mask'] = [1] * len(item['input_ids']) inputs.append(item) return tokenizer.pad( inputs, padding=True, max_length=max_length + len(sep_inputs) + len(prompt_inputs), pad_to_multiple_of=8, return_tensors='pt', ) tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-gemma') model = AutoModelForCausalLM.from_pretrained('BAAI/bge-reranker-v2-gemma') yes_loc = tokenizer('Yes', add_special_tokens=False)['input_ids'][0] model.eval() pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] with torch.no_grad(): inputs = get_inputs(pairs, tokenizer) scores = model(**inputs, return_dict=True).logits[:, -1, yes_loc].view(-1, ).float() print(scores) ``` #### For LLM-based layerwise reranker ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer def get_inputs(pairs, tokenizer, prompt=None, max_length=1024): if prompt is None: prompt = "Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'." sep = "\n" prompt_inputs = tokenizer(prompt, return_tensors=None, add_special_tokens=False)['input_ids'] sep_inputs = tokenizer(sep, return_tensors=None, add_special_tokens=False)['input_ids'] inputs = [] for query, passage in pairs: query_inputs = tokenizer(f'A: {query}', return_tensors=None, add_special_tokens=False, max_length=max_length * 3 // 4, truncation=True) passage_inputs = tokenizer(f'B: {passage}', return_tensors=None, add_special_tokens=False, max_length=max_length, truncation=True) item = tokenizer.prepare_for_model( [tokenizer.bos_token_id] + query_inputs['input_ids'], sep_inputs + passage_inputs['input_ids'], truncation='only_second', max_length=max_length, padding=False, return_attention_mask=False, return_token_type_ids=False, add_special_tokens=False ) item['input_ids'] = item['input_ids'] + sep_inputs + prompt_inputs item['attention_mask'] = [1] * len(item['input_ids']) inputs.append(item) return tokenizer.pad( inputs, padding=True, max_length=max_length + len(sep_inputs) + len(prompt_inputs), pad_to_multiple_of=8, return_tensors='pt', ) tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-minicpm-layerwise', trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained('BAAI/bge-reranker-v2-minicpm-layerwise', trust_remote_code=True, torch_dtype=torch.bfloat16) model = model.to('cuda') model.eval() pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] with torch.no_grad(): inputs = get_inputs(pairs, tokenizer).to(model.device) all_scores = model(**inputs, return_dict=True, cutoff_layers=[28]) all_scores = [scores[:, -1].view(-1, ).float() for scores in all_scores[0]] print(all_scores) ``` ## Fine-tune ### Data Format Train data should be a json file, where each line is a dict like this: ``` {"query": str, "pos": List[str], "neg":List[str], "prompt": str} ``` `query` is the query, and `pos` is a list of positive texts, `neg` is a list of negative texts, `prompt` indicates the relationship between query and texts. If you have no negative texts for a query, you can random sample some from the entire corpus as the negatives. See [toy_finetune_data.jsonl](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_reranker/toy_finetune_data.jsonl) for a toy data file. ### Train You can fine-tune the reranker with the following code: **For llm-based reranker** ```shell torchrun --nproc_per_node {number of gpus} \ -m FlagEmbedding.llm_reranker.finetune_for_instruction.run \ --output_dir {path to save model} \ --model_name_or_path google/gemma-2b \ --train_data ./toy_finetune_data.jsonl \ --learning_rate 2e-4 \ --num_train_epochs 1 \ --per_device_train_batch_size 1 \ --gradient_accumulation_steps 16 \ --dataloader_drop_last True \ --query_max_len 512 \ --passage_max_len 512 \ --train_group_size 16 \ --logging_steps 1 \ --save_steps 2000 \ --save_total_limit 50 \ --ddp_find_unused_parameters False \ --gradient_checkpointing \ --deepspeed stage1.json \ --warmup_ratio 0.1 \ --bf16 \ --use_lora True \ --lora_rank 32 \ --lora_alpha 64 \ --use_flash_attn True \ --target_modules q_proj k_proj v_proj o_proj ``` **For llm-based layerwise reranker** ```shell torchrun --nproc_per_node {number of gpus} \ -m FlagEmbedding.llm_reranker.finetune_for_layerwise.run \ --output_dir {path to save model} \ --model_name_or_path openbmb/MiniCPM-2B-dpo-bf16 \ --train_data ./toy_finetune_data.jsonl \ --learning_rate 2e-4 \ --num_train_epochs 1 \ --per_device_train_batch_size 1 \ --gradient_accumulation_steps 16 \ --dataloader_drop_last True \ --query_max_len 512 \ --passage_max_len 512 \ --train_group_size 16 \ --logging_steps 1 \ --save_steps 2000 \ --save_total_limit 50 \ --ddp_find_unused_parameters False \ --gradient_checkpointing \ --deepspeed stage1.json \ --warmup_ratio 0.1 \ --bf16 \ --use_lora True \ --lora_rank 32 \ --lora_alpha 64 \ --use_flash_attn True \ --target_modules q_proj k_proj v_proj o_proj \ --start_layer 8 \ --head_multi True \ --head_type simple \ --lora_extra_parameters linear_head ``` Our rerankers are initialized from [google/gemma-2b](https://huggingface.co/google/gemma-2b) (for llm-based reranker) and [openbmb/MiniCPM-2B-dpo-bf16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16) (for llm-based layerwise reranker), and we train it on a mixture of multilingual datasets: - [bge-m3-data](https://huggingface.co/datasets/Shitao/bge-m3-data) - [quora train data](https://huggingface.co/datasets/quora) - [fever train data](https://fever.ai/dataset/fever.html) ## Evaluation - llama-index. ![image-20240317193909373](./assets/llama-index.png) - BEIR. rereank the top 100 results from bge-en-v1.5 large. ![image-20240317174633333](./assets/BEIR-bge-en-v1.5.png) rereank the top 100 results from e5 mistral 7b instruct. ![image-20240317172949713](./assets/BEIR-e5-mistral.png) - CMTEB-retrieval. It rereank the top 100 results from bge-zh-v1.5 large. ![image-20240317173026235](./assets/CMTEB-retrieval-bge-zh-v1.5.png) - miracl (multi-language). It rereank the top 100 results from bge-m3. ![image-20240317173117639](./assets/miracl-bge-m3.png) ## Citation If you find this repository useful, please consider giving a star and citation ```bibtex @misc{li2023making, title={Making Large Language Models A Better Foundation For Dense Retrieval}, author={Chaofan Li and Zheng Liu and Shitao Xiao and Yingxia Shao}, year={2023}, eprint={2312.15503}, archivePrefix={arXiv}, primaryClass={cs.CL} } @misc{chen2024bge, title={BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation}, author={Jianlv Chen and Shitao Xiao and Peitian Zhang and Kun Luo and Defu Lian and Zheng Liu}, year={2024}, eprint={2402.03216}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

rudimon/mrruti

rudimon

2025-04-02T15:15:23Z

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-04-02T14:50:01Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: MrRuti --- # Mrruti <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `MrRuti` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "MrRuti", "lora_weights": "https://huggingface.co/rudimon/mrruti/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('rudimon/mrruti', weight_name='lora.safetensors') image = pipeline('MrRuti').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/rudimon/mrruti/discussions) to add images that show off what you’ve made with this LoRA.

mlfoundations-dev/3k_globalbatchsize128_lr4e5_epochs7

mlfoundations-dev

2025-04-02T15:12:43Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-04-02T09:17:44Z

--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: 3k_globalbatchsize128_lr4e5_epochs7 results: [] ---  # 3k_globalbatchsize128_lr4e5_epochs7 This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/openthoughts_3000 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 7.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.3.0 - Datasets 3.1.0 - Tokenizers 0.20.3

challenger8/gemma-2-2B-it-thinking-function_calling-V0

challenger8

2025-04-02T15:12:35Z

0

transformers

[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:HuggingFaceTB/SmolLM2-1.7B-Instruct", "base_model:finetune:HuggingFaceTB/SmolLM2-1.7B-Instruct", "endpoints_compatible", "region:us" ]

null

2025-04-02T15:03:34Z

--- base_model: HuggingFaceTB/SmolLM2-1.7B-Instruct library_name: transformers model_name: gemma-2-2B-it-thinking-function_calling-V0 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-2-2B-it-thinking-function_calling-V0 This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="challenger8/gemma-2-2B-it-thinking-function_calling-V0", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.4.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

suman-off/gemma-product-description

suman-off

2025-04-02T15:12:27Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-4b-pt", "base_model:finetune:google/gemma-3-4b-pt", "endpoints_compatible", "region:us" ]

null

2025-04-02T13:19:28Z

--- base_model: google/gemma-3-4b-pt library_name: transformers model_name: gemma-product-description tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-product-description This model is a fine-tuned version of [google/gemma-3-4b-pt](https://huggingface.co/google/gemma-3-4b-pt). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="suman-off/gemma-product-description", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.50.0.dev0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

sdadas/polish-reranker-base-mse

sdadas

2025-04-02T15:09:39Z

32

0

sentence-transformers

[ "sentence-transformers", "pytorch", "safetensors", "roberta", "text-classification", "transformers", "information-retrieval", "text-ranking", "pl", "arxiv:2402.14318", "license:apache-2.0", "region:us" ]

text-ranking

2024-02-03T11:29:52Z

--- pipeline_tag: text-ranking tags: - transformers - information-retrieval language: pl license: apache-2.0 library_name: sentence-transformers --- <h1 align="center">polish-reranker-base-mse</h1> This is a Polish text ranking model trained using the mean squared error (MSE) distillation method on a large dataset of text pairs consisting of 1.4 million queries and 10 million documents. The training data included the following parts: 1) The Polish MS MARCO training split (800k queries); 2) The ELI5 dataset translated to Polish (over 500k queries); 3) A collection of Polish medical questions and answers (approximately 100k queries). As a teacher model, we employed [unicamp-dl/mt5-13b-mmarco-100k](https://huggingface.co/unicamp-dl/mt5-13b-mmarco-100k), a large multilingual reranker based on the MT5-XXL architecture. As a student model, we choose [Polish RoBERTa](https://huggingface.co/sdadas/polish-roberta-base-v2). In the MSE method, the student is trained to directly replicate the outputs returned by the teacher. ## Usage (Sentence-Transformers) You can use the model like this with [sentence-transformers](https://www.SBERT.net): ```python from sentence_transformers import CrossEncoder import torch.nn query = "Jak dożyć 100 lat?" answers = [ "Trzeba zdrowo się odżywiać i uprawiać sport.", "Trzeba pić alkohol, imprezować i jeździć szybkimi autami.", "Gdy trwała kampania politycy zapewniali, że rozprawią się z zakazem niedzielnego handlu." ] model = CrossEncoder( "sdadas/polish-reranker-base-mse", default_activation_function=torch.nn.Identity(), max_length=512, device="cuda" if torch.cuda.is_available() else "cpu" ) pairs = [[query, answer] for answer in answers] results = model.predict(pairs) print(results.tolist()) ``` ## Usage (Huggingface Transformers) The model can also be used with Huggingface Transformers in the following way: ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import numpy as np query = "Jak dożyć 100 lat?" answers = [ "Trzeba zdrowo się odżywiać i uprawiać sport.", "Trzeba pić alkohol, imprezować i jeździć szybkimi autami.", "Gdy trwała kampania politycy zapewniali, że rozprawią się z zakazem niedzielnego handlu." ] model_name = "sdadas/polish-reranker-base-mse" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) texts = [f"{query}</s></s>{answer}" for answer in answers] tokens = tokenizer(texts, padding="longest", max_length=512, truncation=True, return_tensors="pt") output = model(**tokens) results = output.logits.detach().numpy() results = np.squeeze(results) print(results.tolist()) ``` ## Evaluation Results The model achieves **NDCG@10** of **57.50** in the Rerankers category of the Polish Information Retrieval Benchmark. See [PIRB Leaderboard](https://huggingface.co/spaces/sdadas/pirb) for detailed results. ## Citation ```bibtex @article{dadas2024assessing, title={Assessing generalization capability of text ranking models in Polish}, author={Sławomir Dadas and Małgorzata Grębowiec}, year={2024}, eprint={2402.14318}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

sdadas/polish-reranker-bge-v2

sdadas

2025-04-02T15:09:29Z

132

1

sentence-transformers

[ "sentence-transformers", "safetensors", "xlm-roberta", "text-classification", "transformers", "information-retrieval", "text-ranking", "custom_code", "pl", "arxiv:2402.14318", "license:gemma", "region:us" ]

text-ranking

2024-09-25T11:52:55Z

--- pipeline_tag: text-ranking tags: - transformers - information-retrieval language: pl license: gemma library_name: sentence-transformers --- <h1 align="center">polish-reranker-bge-v2</h1> This is a reranker for Polish based on [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) and further fine-tuned on large dataset of text pairs: - We utilised [RankNet loss](https://icml.cc/Conferences/2015/wp-content/uploads/2015/06/icml_ranking.pdf) and trained the model on the same data as [sdadas/polish-reranker-roberta-v2](https://huggingface.co/sdadas/polish-reranker-roberta-v2) - [BAAI/bge-reranker-v2.5-gemma2-lightweight](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight) was used as the teacher model for distillation - After the training, we merged the original and fine-tuned weights to create the final checkpoint - We used a custom implementation of XLM-RoBERTa with support for Flash Attention 2. If you want to use these features, load the model with the arguments `trust_remote_code=True` and `attn_implementation="flash_attention_2"`. This is especially important for this model, since [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) supports long contexts of 8192 tokens. For such input length, the inference can be up to 400% faster with Flash Attention in comparison to the original model. In most cases, the use of [sdadas/polish-reranker-roberta-v2](https://huggingface.co/sdadas/polish-reranker-roberta-v2) is preferred to this model as it achieves better results for Polish. The main advantage of this model is its context length, so it may perform better on some datasets with long documents. ## Usage (Huggingface Transformers) The model can be used with Huggingface Transformers in the following way: ```python import torch from transformers import AutoTokenizer, AutoModelForSequenceClassification import numpy as np query = "Jak dożyć 100 lat?" answers = [ "Trzeba zdrowo się odżywiać i uprawiać sport.", "Trzeba pić alkohol, imprezować i jeździć szybkimi autami.", "Gdy trwała kampania politycy zapewniali, że rozprawią się z zakazem niedzielnego handlu." ] model_name = "sdadas/polish-reranker-bge-v2" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained( model_name, trust_remote_code=True, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2", device_map="cuda" ) texts = [f"{query}</s></s>{answer}" for answer in answers] tokens = tokenizer(texts, padding="longest", max_length=8192, truncation=True, return_tensors="pt").to("cuda") output = model(**tokens) results = output.logits.detach().cpu().float().numpy() results = np.squeeze(results) print(results.tolist()) ``` ## Evaluation Results The model achieves **NDCG@10** of **64.21** in the Rerankers category of the Polish Information Retrieval Benchmark. See [PIRB Leaderboard](https://huggingface.co/spaces/sdadas/pirb) for detailed results. ## Citation ```bibtex @article{dadas2024assessing, title={Assessing generalization capability of text ranking models in Polish}, author={Sławomir Dadas and Małgorzata Grębowiec}, year={2024}, eprint={2402.14318}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

sdadas/polish-reranker-large-mse

sdadas

2025-04-02T15:09:22Z

18

0

sentence-transformers

[ "sentence-transformers", "pytorch", "safetensors", "roberta", "text-classification", "transformers", "information-retrieval", "text-ranking", "pl", "arxiv:2402.14318", "license:apache-2.0", "region:us" ]

text-ranking

2024-02-03T11:32:30Z

--- pipeline_tag: text-ranking tags: - transformers - information-retrieval language: pl license: apache-2.0 library_name: sentence-transformers --- <h1 align="center">polish-reranker-large-mse</h1> This is a Polish text ranking model trained using the mean squared error (MSE) distillation method on a large dataset of text pairs consisting of 1.4 million queries and 10 million documents. The training data included the following parts: 1) The Polish MS MARCO training split (800k queries); 2) The ELI5 dataset translated to Polish (over 500k queries); 3) A collection of Polish medical questions and answers (approximately 100k queries). As a teacher model, we employed [unicamp-dl/mt5-13b-mmarco-100k](https://huggingface.co/unicamp-dl/mt5-13b-mmarco-100k), a large multilingual reranker based on the MT5-XXL architecture. As a student model, we choose [Polish RoBERTa](https://huggingface.co/sdadas/polish-roberta-large-v2). In the MSE method, the student is trained to directly replicate the outputs returned by the teacher. ## Usage (Sentence-Transformers) You can use the model like this with [sentence-transformers](https://www.SBERT.net): ```python from sentence_transformers import CrossEncoder import torch.nn query = "Jak dożyć 100 lat?" answers = [ "Trzeba zdrowo się odżywiać i uprawiać sport.", "Trzeba pić alkohol, imprezować i jeździć szybkimi autami.", "Gdy trwała kampania politycy zapewniali, że rozprawią się z zakazem niedzielnego handlu." ] model = CrossEncoder( "sdadas/polish-reranker-large-mse", default_activation_function=torch.nn.Identity(), max_length=512, device="cuda" if torch.cuda.is_available() else "cpu" ) pairs = [[query, answer] for answer in answers] results = model.predict(pairs) print(results.tolist()) ``` ## Usage (Huggingface Transformers) The model can also be used with Huggingface Transformers in the following way: ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import numpy as np query = "Jak dożyć 100 lat?" answers = [ "Trzeba zdrowo się odżywiać i uprawiać sport.", "Trzeba pić alkohol, imprezować i jeździć szybkimi autami.", "Gdy trwała kampania politycy zapewniali, że rozprawią się z zakazem niedzielnego handlu." ] model_name = "sdadas/polish-reranker-large-mse" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) texts = [f"{query}</s></s>{answer}" for answer in answers] tokens = tokenizer(texts, padding="longest", max_length=512, truncation=True, return_tensors="pt") output = model(**tokens) results = output.logits.detach().numpy() results = np.squeeze(results) print(results.tolist()) ``` ## Evaluation Results The model achieves **NDCG@10** of **60.27** in the Rerankers category of the Polish Information Retrieval Benchmark. See [PIRB Leaderboard](https://huggingface.co/spaces/sdadas/pirb) for detailed results. ## Citation ```bibtex @article{dadas2024assessing, title={Assessing generalization capability of text ranking models in Polish}, author={Sławomir Dadas and Małgorzata Grębowiec}, year={2024}, eprint={2402.14318}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

albertus-sussex/veriscrape-sbert-movie-wo-ref-gemini-1.5-flash

albertus-sussex

2025-04-02T15:09:15Z

0

sentence-transformers

[ "sentence-transformers", "safetensors", "new", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:38612", "loss:TripletLoss", "custom_code", "arxiv:1908.10084", "arxiv:1703.07737", "base_model:Alibaba-NLP/gte-base-en-v1.5", "base_model:finetune:Alibaba-NLP/gte-base-en-v1.5", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2025-04-02T15:08:50Z

--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:38612 - loss:TripletLoss base_model: Alibaba-NLP/gte-base-en-v1.5 widget: - source_sentence: John Irvin sentences: - director - mpaa_rating - Martin Ritt - NC17 - source_sentence: Liar Liar (HD-DVD) sentences: - title - director - Michael Bay - Hustle and Flow (HD-DVD) - source_sentence: (R) sentences: - mpaa_rating - director - Robert Redford - (PG-13) - source_sentence: (PG) sentences: - (R) - mpaa_rating - title - P2 (2007) - source_sentence: » sentences: - title - » - mpaa_rating - Cats & Dogs pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy - silhouette_cosine - silhouette_euclidean model-index: - name: SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5 results: - task: type: triplet name: Triplet dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy value: 0.9995338916778564 name: Cosine Accuracy - type: cosine_accuracy value: 0.9993706941604614 name: Cosine Accuracy - task: type: silhouette name: Silhouette dataset: name: Unknown type: unknown metrics: - type: silhouette_cosine value: 0.7176739573478699 name: Silhouette Cosine - type: silhouette_euclidean value: 0.5843296051025391 name: Silhouette Euclidean - type: silhouette_cosine value: 0.7134687304496765 name: Silhouette Cosine - type: silhouette_euclidean value: 0.5822250247001648 name: Silhouette Euclidean --- # SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5)  - **Maximum Sequence Length:** 32 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity    ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 32, 'do_lower_case': False}) with Transformer model: NewModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("albertus-sussex/veriscrape-sbert-movie-wo-ref-gemini-1.5-flash") # Run inference sentences = [ '»', '»', 'Cats & Dogs', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ```    ## Evaluation ### Metrics #### Triplet * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:--------------------|:-----------| | **cosine_accuracy** | **0.9995** | #### Silhouette * Evaluated with <code>veriscrape.training.SilhouetteEvaluator</code> | Metric | Value | |:----------------------|:-----------| | **silhouette_cosine** | **0.7177** | | silhouette_euclidean | 0.5843 | #### Triplet * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:--------------------|:-----------| | **cosine_accuracy** | **0.9994** | #### Silhouette * Evaluated with <code>veriscrape.training.SilhouetteEvaluator</code> | Metric | Value | |:----------------------|:-----------| | **silhouette_cosine** | **0.7135** | | silhouette_euclidean | 0.5822 |   ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 38,612 training samples * Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, <code>pos_attr_name</code>, <code>neg_attr_name</code>, and <code>website_id</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | pos_attr_name | neg_attr_name | website_id | |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | type | string | string | string | string | string | int | | details | <ul><li>min: 3 tokens</li><li>mean: 6.03 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.98 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.14 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 4.68 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.42 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>0: ~1.70%</li><li>1: ~4.00%</li><li>2: ~3.80%</li><li>3: ~26.80%</li><li>4: ~2.70%</li><li>5: ~25.30%</li><li>6: ~23.20%</li><li>7: ~3.90%</li><li>8: ~4.90%</li><li>9: ~3.70%</li></ul> | * Samples: | anchor | positive | negative | pos_attr_name | neg_attr_name | website_id | |:----------------------------------------------------|:----------------------------------------|:-----------------------------|:-------------------|:-------------------------|:---------------| | <code>Horror</code> | <code>Action</code> | <code>Martin Scorsese</code> | <code>genre</code> | <code>director</code> | <code>4</code> | | <code>Megamind</code> | <code>It's Kind of a Funny Story</code> | <code>TV-G</code> | <code>title</code> | <code>mpaa_rating</code> | <code>6</code> | | <code>Something Wicked This Way Comes (1983)</code> | <code>Kandahar (2001)</code> | <code>(PG)</code> | <code>title</code> | <code>mpaa_rating</code> | <code>3</code> | * Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 4,291 evaluation samples * Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, <code>pos_attr_name</code>, <code>neg_attr_name</code>, and <code>website_id</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | pos_attr_name | neg_attr_name | website_id | |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | type | string | string | string | string | string | int | | details | <ul><li>min: 3 tokens</li><li>mean: 5.64 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.79 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.23 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 4.54 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.48 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>0: ~2.20%</li><li>1: ~4.90%</li><li>2: ~5.40%</li><li>3: ~27.00%</li><li>4: ~2.40%</li><li>5: ~19.80%</li><li>6: ~22.80%</li><li>7: ~4.30%</li><li>8: ~7.30%</li><li>9: ~3.90%</li></ul> | * Samples: | anchor | positive | negative | pos_attr_name | neg_attr_name | website_id | |:-----------------------------------|:--------------------------|:-------------------------|:-------------------|:-------------------------|:---------------| | <code>My Fake Fiance (2009)</code> | <code>Dames (1934)</code> | <code>Comedy</code> | <code>title</code> | <code>genre</code> | <code>4</code> | | <code>Music Drama</code> | <code>Unknown</code> | <code>PG-13</code> | <code>genre</code> | <code>mpaa_rating</code> | <code>2</code> | | <code>Due Date</code> | <code>Megamind</code> | <code>Ben Younger</code> | <code>title</code> | <code>director</code> | <code>6</code> | * Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `num_train_epochs`: 5 - `warmup_ratio`: 0.1 #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 5 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs | Epoch | Step | Training Loss | Validation Loss | cosine_accuracy | silhouette_cosine | |:-----:|:----:|:-------------:|:---------------:|:---------------:|:-----------------:| | -1 | -1 | - | - | 0.5540 | 0.0936 | | 1.0 | 302 | 0.4164 | 0.0137 | 0.9991 | 0.6674 | | 2.0 | 604 | 0.0161 | 0.0107 | 0.9991 | 0.7327 | | 3.0 | 906 | 0.0063 | 0.0070 | 0.9995 | 0.7161 | | 4.0 | 1208 | 0.0036 | 0.0057 | 0.9995 | 0.7182 | | 5.0 | 1510 | 0.002 | 0.0064 | 0.9995 | 0.7177 | | -1 | -1 | - | - | 0.9994 | 0.7135 | ### Framework Versions - Python: 3.10.16 - Sentence Transformers: 4.0.1 - Transformers: 4.45.2 - PyTorch: 2.5.1+cu124 - Accelerate: 1.6.0 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### TripletLoss ```bibtex @misc{hermans2017defense, title={In Defense of the Triplet Loss for Person Re-Identification}, author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, year={2017}, eprint={1703.07737}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

sdadas/polish-reranker-roberta-v2

sdadas

2025-04-02T15:09:08Z

1,102

2

sentence-transformers

[ "sentence-transformers", "safetensors", "roberta", "text-classification", "transformers", "information-retrieval", "text-ranking", "custom_code", "pl", "arxiv:2402.14318", "license:gemma", "region:us" ]

text-ranking

2024-09-20T10:41:13Z

--- pipeline_tag: text-ranking tags: - transformers - information-retrieval language: pl license: gemma library_name: sentence-transformers --- <h1 align="center">polish-reranker-roberta-v2</h1> This is an improved version of reranker based on [sdadas/polish-roberta-large-v2](https://huggingface.co/sdadas/polish-roberta-large-v2) trained with [RankNet loss](https://icml.cc/Conferences/2015/wp-content/uploads/2015/06/icml_ranking.pdf) on a large dataset of text pairs. The model was trained in the same way and on the same data as [sdadas/polish-reranker-large-ranknet](https://huggingface.co/sdadas/polish-reranker-large-ranknet), with the following improvements: - We used predictions from [BAAI/bge-reranker-v2.5-gemma2-lightweight](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight) for distillation instead of [unicamp-dl/mt5-13b-mmarco-100k](https://huggingface.co/unicamp-dl/mt5-13b-mmarco-100k). - We used a custom implementation of the RoBERTa model with support for Flash Attention 2. If you want to use these features, load the model with the arguments `trust_remote_code=True` and `attn_implementation="flash_attention_2"`. Our reranker achieves results close to [BAAI/bge-reranker-v2.5-gemma2-lightweight](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight) on the PIRB benchmark, even outperforming it on some datasets. At the same time, it is over 21 times smaller — 435M vs. 9.24B parameters. ## Usage (Huggingface Transformers) The model can be used with Huggingface Transformers in the following way: ```python import torch from transformers import AutoTokenizer, AutoModelForSequenceClassification import numpy as np query = "Jak dożyć 100 lat?" answers = [ "Trzeba zdrowo się odżywiać i uprawiać sport.", "Trzeba pić alkohol, imprezować i jeździć szybkimi autami.", "Gdy trwała kampania politycy zapewniali, że rozprawią się z zakazem niedzielnego handlu." ] model_name = "sdadas/polish-reranker-roberta-v2" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained( model_name, trust_remote_code=True, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2", device_map="cuda" ) texts = [f"{query}</s></s>{answer}" for answer in answers] tokens = tokenizer(texts, padding="longest", max_length=512, truncation=True, return_tensors="pt").to("cuda") output = model(**tokens) results = output.logits.detach().cpu().float().numpy() results = np.squeeze(results) print(results.tolist()) ``` ## Evaluation Results The model achieves **NDCG@10** of **65.30** in the Rerankers category of the Polish Information Retrieval Benchmark. See [PIRB Leaderboard](https://huggingface.co/spaces/sdadas/pirb) for detailed results. ## Citation ```bibtex @article{dadas2024assessing, title={Assessing generalization capability of text ranking models in Polish}, author={Sławomir Dadas and Małgorzata Grębowiec}, year={2024}, eprint={2402.14318}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

HPAI-BSC/Llama3-Aloe-8B-Alpha

HPAI-BSC

2025-04-02T15:08:18Z

13,971

59

transformers

[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "biology", "medical", "question-answering", "en", "dataset:argilla/dpo-mix-7k", "dataset:nvidia/HelpSteer", "dataset:jondurbin/airoboros-3.2", "dataset:hkust-nlp/deita-10k-v0", "dataset:LDJnr/Capybara", "dataset:HPAI-BSC/CareQA", "dataset:GBaker/MedQA-USMLE-4-options", "dataset:lukaemon/mmlu", "dataset:bigbio/pubmed_qa", "dataset:openlifescienceai/medmcqa", "dataset:bigbio/med_qa", "dataset:HPAI-BSC/better-safe-than-sorry", "dataset:HPAI-BSC/pubmedqa-cot", "dataset:HPAI-BSC/medmcqa-cot", "dataset:HPAI-BSC/medqa-cot", "arxiv:2405.01886", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

question-answering

2024-04-26T16:43:36Z

--- license: cc-by-nc-4.0 datasets: - argilla/dpo-mix-7k - nvidia/HelpSteer - jondurbin/airoboros-3.2 - hkust-nlp/deita-10k-v0 - LDJnr/Capybara - HPAI-BSC/CareQA - GBaker/MedQA-USMLE-4-options - lukaemon/mmlu - bigbio/pubmed_qa - openlifescienceai/medmcqa - bigbio/med_qa - HPAI-BSC/better-safe-than-sorry - HPAI-BSC/pubmedqa-cot - HPAI-BSC/medmcqa-cot - HPAI-BSC/medqa-cot language: - en library_name: transformers tags: - biology - medical pipeline_tag: question-answering --- <div align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/6620f941eba5274b5c12f83d/DlPW2CN-qErpC3QJqdNho.png" width="400" alt="HPAI"/> </div> <hr style="margin: 15px"> <div align="center" style="line-height: 1;"> <a href="https://hpai.bsc.es/" target="_blank" style="margin: 1px;"> <img alt="Web" src="https://img.shields.io/badge/Website-HPAI-8A2BE2" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://huggingface.co/HPAI-BSC" target="_blank" style="margin: 1px;"> <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-HPAI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://github.com/HPAI-BSC" target="_blank" style="margin: 1px;"> <img alt="GitHub" src="https://img.shields.io/badge/GitHub-HPAI-%23121011.svg?logo=github&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="https://www.linkedin.com/company/hpai" target="_blank" style="margin: 1px;"> <img alt="Linkedin" src="https://img.shields.io/badge/Linkedin-HPAI-blue" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://bsky.app/profile/hpai.bsky.social" target="_blank" style="margin: 1px;"> <img alt="BlueSky" src="https://img.shields.io/badge/Bluesky-HPAI-0285FF?logo=bluesky&logoColor=fff" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://linktr.ee/hpai_bsc" target="_blank" style="margin: 1px;"> <img alt="LinkTree" src="https://img.shields.io/badge/Linktree-HPAI-43E55E?style=flat&logo=linktree&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="https://arxiv.org/abs/2405.01886" target="_blank" style="margin: 1px;"> <img alt="Arxiv" src="https://img.shields.io/badge/arXiv-2405.01886-b31b1b.svg" style="display: inline-block; vertical-align: middle;"/> </a> <a href="LICENSE" style="margin: 1px;"> <img alt="License" src="https://img.shields.io/badge/license-CC--BY--NC%204.0-green" style="display: inline-block; vertical-align: middle;"/> </a> </div> AVAILABLE NOW THE LATEST ITERATION OF THE ALOE FAMILY! [ALOE BETA 8B](https://huggingface.co/HPAI-BSC/Llama3.1-Aloe-Beta-8B) AND [ALOE BETA 70B](https://huggingface.co/HPAI-BSC/Llama3.1-Aloe-Beta-70B) VERSIONS. These include: * Better overall performance * More thorough alignment and safety * License compatible with more uses # Aloe: A New Family of Healthcare LLMs Aloe is a new family of healthcare LLMs that is highly competitive with all previous open models of its range and reaches state-of-the-art results at its size by using model merging and advanced prompting strategies. Aloe scores high in metrics measuring ethics and factuality, thanks to a combined red teaming and alignment effort. Complete training details, model merging configurations, and all training data (including synthetically generated data) will be shared. Additionally, the prompting repository used in this work to produce state-of-the-art results during inference will also be shared. Aloe comes with a healthcare-specific risk assessment to contribute to the safe use and deployment of such systems. <img src="https://cdn-uploads.huggingface.co/production/uploads/62972c4979f193515da1d38e/xlssx5_3_kLQlJlmE-aya.png" width="95%"> ## Model Details ### [](https://huggingface.co/templates/model-card-example#model-description)Model Description - **Developed by:** [HPAI](https://hpai.bsc.es/) - **Model type:** Causal decoder-only transformer language model - **Language(s) (NLP):** English (mainly) - **License:** This model is based on Meta Llama 3 8B and is governed by the [Meta Llama 3 License](https://llama.meta.com/llama3/license/). All our modifications are available with a [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) license. - **Finetuned from model :** [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) ### [](https://huggingface.co/templates/model-card-example#model-sources-optional)Model Sources [optional] - **Repository:** https://github.com/HPAI-BSC/prompt_engine (more coming soon) - **Paper:** https://arxiv.org/abs/2405.01886 (more coming soon) ## Model Performance Aloe has been tested on the most popular healthcare QA datasets, with and without medprompting inference technique. Results show competitive performance, even against bigger models. <img src="https://cdn-uploads.huggingface.co/production/uploads/62f7a16192950415b637e201/rQ4z-qXzKN44oAcFDbHi2.png" width="95%"> Results using advanced prompting methods (aka Medprompt) are achieved through a [repo](https://github.com/HPAI-BSC/prompt_engine) made public with this work. ## Uses ### Direct Use We encourage the use of Aloe for research purposes, as a stepping stone to build better foundational models for healthcare. ### Out-of-Scope Use These models are not to be used for clinical practice, medical diagnosis, or any other form of direct or indirect healthcare advice. Models are prone to error and can produce toxic content. The use of Aloe models for activities harmful for individuals, such as spam, fraud, or impersonation, is prohibited. ## Bias, Risks, and Limitations We consider three risk cases: - Healthcare professional impersonation, a fraudulent behaviour which currently generates billions of dollars in [profit](https://www.justice.gov/opa/pr/justice-department-charges-dozens-12-billion-health-care-fraud). A model such as Aloe could be used to increase the efficacy of such deceiving activities, making them more widespread. The main preventive actions are public literacy on the unreliability of digitised information and the importance of medical registration, and legislation enforcing AI-generated content disclaimers. - Medical decision-making without professional supervision. While this is already an issue in modern societies (eg self-medication) a model such as Aloe, capable of producing high-quality conversational data, can facilitate self-delusion, particularly in the presence of sycophancy. By producing tailored responses, it can also be used to generate actionable answers. Public literacy on the dangers of self-diagnosis is one of the main defences, together with the introduction of disclaimers and warnings on the models' outputs. - Access to information on dangerous substances or procedures. While the literature on sensitive content can already be found on different sources (eg libraries, internet, dark web), LLMs can centralize such access, making it nearly impossible to control the flow of such information. Model alignment can help in that regard, but so far the effects remain insufficient, as jailbreaking methods still overcome it. Table below shows the performance of Aloe at several AI safety tasks: <img src="https://cdn-uploads.huggingface.co/production/uploads/62972c4979f193515da1d38e/T6Jblpf1kmTkM04K716rM.png" width="95%"> ### Recommendations We avoid the use of all personal data in our training. Model safety cannot be guaranteed. Aloe can produce toxic content under the appropriate prompts. For these reasons, minors should not be left alone to interact with Aloe without supervision. ## How to Get Started with the Model Use the code below to get started with the model. You can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the `generate()` function. Let's see examples of both. #### Transformers pipeline ```python import transformers import torch model_id = "HPAI-BSC/Llama3-Aloe-8B-Alpha" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto", ) messages = [ {"role": "system", "content": "You are an expert medical assistant named Aloe, developed by the High Performance Artificial Intelligence Group at Barcelona Supercomputing Center(BSC). You are to be a helpful, respectful, and honest assistant."}, {"role": "user", "content": "Hello."}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` #### Transformers AutoModelForCausalLM ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch model_id = "HPAI-BSC/Llama3-Aloe-8B-Alpha" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ {"role": "system", "content": "You are an expert medical assistant named Aloe, developed by the High Performance Artificial Intelligence Group at Barcelona Supercomputing Center(BSC). You are to be a helpful, respectful, and honest assistant."}, {"role": "user", "content": "Hello"}, ] input_ids = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt" ).to(model.device) terminators = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>") ] outputs = model.generate( input_ids, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9, ) response = outputs[0][input_ids.shape[-1]:] print(tokenizer.decode(response, skip_special_tokens=True)) ``` ## Training Details Supervised fine-tuning on top of Llama 3 8B using medical and general domain datasets, model merging using DARE-TIES process, two-stage DPO process for human preference alignment. More details coming soon. ### Training Data - Medical domain datasets, including synthetic data generated using Mixtral-8x7B and Genstruct - HPAI-BSC/pubmedqa-cot - HPAI-BSC/medqa-cot - HPAI-BSC/medmcqa-cot - LDJnr/Capybara - hkust-nlp/deita-10k-v0 - jondurbin/airoboros-3.2 - argilla/dpo-mix-7k - nvidia/HelpSteer - Custom preference data with adversarial prompts generated from Anthropic Harmless, Chen et al., and original prompts ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data - [MedQA (USMLE)](https://huggingface.co/datasets/bigbio/med_qa) - [MedMCQA](https://huggingface.co/datasets/medmcqa) - [PubMedQA](https://huggingface.co/datasets/bigbio/pubmed_qa) - [MMLU-Medical](https://huggingface.co/datasets/lukaemon/mmlu) - [MedQA-4-Option](https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options) - [CareQA](https://huggingface.co/datasets/HPAI-BSC/CareQA) #### Metrics - Accuracy: suite the evaluation of multiple-choice question-answering tasks. ### Results <img src="https://cdn-uploads.huggingface.co/production/uploads/62972c4979f193515da1d38e/STlPSggXr9P9JeWAvmAsi.png" width="90%"> #### Summary To compare Aloe with the most competitive open models (both general purpose and healthcare-specific) we use popular healthcare datasets (PubMedQA, MedMCQA, MedQA and MMLU for six medical tasks only), together with the new and highly reliable CareQA. We produce the standard MultiMedQA score for reference, by computing the weighted average accuracy on all scores except CareQA. Additionally, we calculate the arithmetic mean across all datasets. The Medical MMLU is calculated by averaging the six medical subtasks: Anatomy, Clinical knowledge, College Biology, College medicine, Medical genetics, and Professional medicine. Benchmark results indicate the training conducted on Aloe has boosted its performance above Llama3-8B-Instruct. Llama3-Aloe-8B-Alpha outperforms larger models like Meditron 70B, and is close to larger base models, like Yi-34. For the former, this gain is consistent even when using SC-CoT, using their best-reported variant. All these results make Llama3-Aloe-8B-Alpha the best healthcare LLM of its size. With the help of prompting techniques the performance of Llama3-Aloe-8B-Alpha is significantly improved. Medprompting in particular provides a 7% increase in reported accuracy, after which Llama3-Aloe-8B-Alpha only lags behind the ten times bigger Llama-3-70B-Instruct. This improvement is mostly consistent across medical fields. Llama3-Aloe-8B-Alpha with medprompting beats the performance of Meditron 70B with their self reported 20 shot SC-CoT in MMLU med and is slightly worse in the other benchmarks. ## Environmental Impact - **Hardware Type:** 4xH100 - **Hours used:** 7,000 - **Hardware Provider:** Barcelona Supercomputing Center - **Compute Region:** Spain - **Carbon Emitted:** 439.25kg ## Model Card Authors [Ashwin Kumar Gururajan](https://huggingface.co/G-AshwinKumar) ## Model Card Contact mailto:[email protected] ## Citations If you use this repository in a published work, please cite the following papers as source: ``` @misc{gururajan2024aloe, title={Aloe: A Family of Fine-tuned Open Healthcare LLMs}, author={Ashwin Kumar Gururajan and Enrique Lopez-Cuena and Jordi Bayarri-Planas and Adrian Tormos and Daniel Hinjos and Pablo Bernabeu-Perez and Anna Arias-Duart and Pablo Agustin Martin-Torres and Lucia Urcelay-Ganzabal and Marta Gonzalez-Mallo and Sergio Alvarez-Napagao and Eduard Ayguadé-Parra and Ulises Cortés Dario Garcia-Gasulla}, year={2024}, eprint={2405.01886}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

cmarkea/bloomz-3b-reranking

cmarkea

2025-04-02T15:08:16Z

113

1

transformers

[ "transformers", "pytorch", "safetensors", "bloom", "text-classification", "text-ranking", "fr", "en", "dataset:unicamp-dl/mmarco", "dataset:rajpurkar/squad", "base_model:cmarkea/bloomz-3b-dpo-chat", "base_model:finetune:cmarkea/bloomz-3b-dpo-chat", "license:bigscience-bloom-rail-1.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-ranking

2024-03-15T16:13:35Z

--- license: bigscience-bloom-rail-1.0 datasets: - unicamp-dl/mmarco - rajpurkar/squad language: - fr - en pipeline_tag: text-ranking base_model: - cmarkea/bloomz-3b-dpo-chat --- # Bloomz-3b Reranking This reranking model is built from [cmarkea/bloomz-3b-dpo-chat](https://huggingface.co/cmarkea/bloomz-3b-dpo-chat) model and aims to measure the semantic correspondence between a question (query) and a context. With its normalized scoring, it helps to filter the query/context matchings outputted by a retriever in an ODQA (Open-Domain Question Answering) context. Moreover, it allows to reorder the results using a more efficient modeling approach than the retriever one. However, this modeling type is not conducive to direct database searching due to its high computational cost. Developed to be language-agnostic, this model supports both French and English. Consequently, it can effectively score in a cross-language context without being influenced by its behavior in a monolingual context (English or French). ## Dataset The training dataset is composed of the [mMARCO](https://huggingface.co/datasets/unicamp-dl/mmarco) dataset, consisting of query/positive/hard negative triplets. Additionally, we have included [SQuAD](https://huggingface.co/datasets/rajpurkar/squad) data from the "train" split, forming query/positive/hard negative triplets. In order to generate hard negative data for SQuAD, we considered contexts from the same theme as the query but from a different set of queries. Hence, the negative observations belong to the same themes as the queries but presumably do not contain the answer to the question. Finally, the triplets are flattened to obtain pairs of query/context sentences with a label 1 if query/positive and a label 0 if query/negative. In each element of the pair (query and context), the language, French or English, is randomly and uniformly chosen. ## Evaluation To assess the performance of the reranker, we will make use of the "validation" split of the [SQuAD](https://huggingface.co/datasets/rajpurkar/squad) dataset. We will select the first question from each paragraph, along with the paragraph constituting the context that should be ranked Top-1 for an Oracle modeling. What's intriguing is that the number of themes is limited, and each context from a corresponding theme that does not match the query is considered as a hard negative (other contexts outside the theme are simple negatives). Thus, we can construct the following table, with each theme showing the number of contexts and associated query: | Theme name | Context number | Theme name | Context number | |---------------------------------------------:|:---------------|---------------------------------------------:|:---------------| | Normans | 39 | Civil_disobedience | 26 | | Computational_complexity_theory | 48 | Construction | 22 | | Southern_California | 39 | Private_school | 26 | | Sky_(United_Kingdom) | 22 | Harvard_University | 30 | | Victoria_(Australia) | 25 | Jacksonville,_Florida | 21 | | Huguenot | 44 | Economic_inequality | 44 | | Steam_engine | 46 | University_of_Chicago | 37 | | Oxygen | 43 | Yuan_dynasty | 47 | | 1973_oil_crisis | 24 | Immune_system | 49 | | European_Union_law | 40 | Intergovernmental_Panel_on_Climate_Change | 24 | | Amazon_rainforest | 21 | Prime_number | 31 | | Ctenophora | 31 | Rhine | 44 | | Fresno,_California | 28 | Scottish_Parliament | 39 | | Packet_switching | 23 | Islamism | 39 | | Black_Death | 23 | Imperialism | 39 | | Geology | 25 | Warsaw | 49 | | Pharmacy | 26 | French_and_Indian_War | 46 | | Force | 44 | | | The evaluation corpus consists of 1204 pairs of query/context to be ranked. Firstly, the evaluation scores were computed in cases where both the query and the context are in the same language (French/French). | Model (French/French) | Top-mean | Top-std | Top-1 (%) | Top-10 (%) | Top-100 (%) | MRR (x100) | mean score Top | std score Top | |:-----------------------------:|:----------:|:---------:|:---------:|:----------:|:-----------:|:----------:|:----------------:|:---------------:| | BM25 | 14.47 | 92.19 | 69.77 | 92.03 | 98.09 | 77.74 | NA | NA | | [CamemBERT](https://huggingface.co/antoinelouis/crossencoder-camembert-base-mmarcoFR) | 5.72 | 36.88 | 69.35 | 95.51 | 98.92 | 79.51 | 0.83 | 0.37 | | [DistilCamemBERT](https://huggingface.co/antoinelouis/crossencoder-distilcamembert-mmarcoFR) | 5.54 | 25.90 | 66.11 | 92.77 | 99.17 | 76.00 | 0.80 | 0.39 | | [mMiniLMv2-L12](https://huggingface.co/antoinelouis/crossencoder-mMiniLMv2-L12-mmarcoFR) | 4.43 | 30.27 | 71.51 | 95.68 | 99.42 | 80.17 | 0.78 | 0.38 | | [RoBERTa (multilingual)](https://huggingface.co/abbasgolestani/ag-nli-DeTS-sentence-similarity-v2) | 15.13 | 60.39 | 57.23 | 83.87 | 96.18 | 66.21 | 0.53 | 0.11 | | [cmarkea/bloomz-560m-reranking](https://huggingface.co/cmarkea/bloomz-560m-reranking) | 1.49 | 2.58 | 83.55 | 99.17 | 100 | 89.98 | 0.93 | 0.15 | | [cmarkea/bloomz-3b-reranking](https://huggingface.co/cmarkea/bloomz-3b-reranking) | 1.22 | 1.06 | 89.37 | 99.75 | 100 | 93.79 | 0.94 | 0.10 | Then, we evaluated the model in a cross-language context, with queries in French and contexts in English. | Model (French/English) | Top-mean | Top-std | Top-1 (%) | Top-10 (%) | Top-100 (%) | MRR (x100) | mean score Top | std score Top | |:-----------------------------:|:----------:|:---------:|:---------:|:----------:|:-----------:|:----------:|:----------------:|:---------------:| | BM25 | 288.04 | 371.46 | 21.93 | 41.93 | 55.15 | 28.41 | NA | NA | | [CamemBERT](https://huggingface.co/antoinelouis/crossencoder-camembert-base-mmarcoFR) | 12.20 | 61.39 | 59.55 | 89.71 | 97.42 | 70.38 | 0.65 | 0.47 | | [DistilCamemBERT](https://huggingface.co/antoinelouis/crossencoder-distilcamembert-mmarcoFR) | 40.97 | 104.78 | 25.66 | 64.78 | 88.62 | 38.83 | 0.53 | 0.49 | | [mMiniLMv2-L12](https://huggingface.co/antoinelouis/crossencoder-mMiniLMv2-L12-mmarcoFR) | 6.91 | 32.16 | 59.88 | 89.95 | 99.09 | 70.39 | 0.61 | 0.46 | | [RoBERTa (multilingual)](https://huggingface.co/abbasgolestani/ag-nli-DeTS-sentence-similarity-v2) | 79.32 | 153.62 | 27.91 | 49.50 | 78.16 | 35.41 | 0.40 | 0.12 | | [cmarkea/bloomz-560m-reranking](https://huggingface.co/cmarkea/bloomz-560m-reranking) | 1.51 | 1.92 | 81.89 | 99.09 | 100 | 88.64 | 0.92 | 0.15 | | [cmarkea/bloomz-3b-reranking](https://huggingface.co/cmarkea/bloomz-3b-reranking) | 1.22 | 0.98 | 89.20 | 99.84 | 100 | 93.63 | 0.94 | 0.10 | As observed, the cross-language context does not significantly impact the behavior of our models. If the model were used in a context of reranking and filtering the Top-K results from a search, a threshold of 0.8 could be applied to filter the contexts outputted by the retriever, thereby reducing noise issues present in the contexts for RAG-type applications. How to Use Bloomz-3b-reranking ------------------------------ The following example is based on the API Pipeline of the Transformers library. ```python from transformers import pipeline reranker = pipeline( task='text-classification', model='cmarkea/bloomz-3b-reranking', top_k=None ) query: str contexts: List[str] similarities = reranker( [ dict( text=context, # the model was trained with context in `text` text_pair=query # and query in `text_pair` argument. ) for context in contexts ] ) score_label_1 = [ next(item['score'] for item in entry if item['label'] == 'LABEL_1') for entry in similarities ] contexts_reranked = sorted( zip(score_label_1, contexts), key=lambda x: x[0], reverse=True ) score, contexts_cleaned = zip( *filter( lambda x: x[0] >= 0.8, contexts_reranked ) ) ``` Citation -------- ```bibtex @online{DeBloomzReranking, AUTHOR = {Cyrile Delestre}, ORGANIZATION = {Cr{\'e}dit Mutuel Ark{\'e}a}, URL = {https://huggingface.co/cmarkea/bloomz-3b-reranking}, YEAR = {2024}, KEYWORDS = {NLP ; Transformers ; LLM ; Bloomz}, } ```

Jsevisal/CrossEncoder-ModernBERT-base-qnli

Jsevisal

2025-04-02T15:07:18Z

70

0

transformers

[ "transformers", "safetensors", "modernbert", "text-classification", "cross-encoder", "sentence-transformers", "text-ranking", "en", "dataset:nyu-mll/glue", "arxiv:1804.07461", "base_model:answerdotai/ModernBERT-base", "base_model:finetune:answerdotai/ModernBERT-base", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-ranking

2025-01-24T10:40:16Z

--- base_model: - answerdotai/ModernBERT-base datasets: - nyu-mll/glue language: - en library_name: transformers metrics: - accuracy - f1 pipeline_tag: text-ranking tags: - cross-encoder - sentence-transformers --- # Model Card for Model ID ModernBert version of CrossEncoders QNLI models. Used to determine if a passage contains the answer to a question. In this case the model has been train on GLUE. ## Model Details ### Model Description This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on [GLUE QNLI](https://arxiv.org/abs/1804.07461) dataset. It achieves the following results on the evaluation set: - Accuracy Score: 0.9319 - F1 Score: 0.9322 ## Usage Pre-trained models can be used like this: ```python from sentence_transformers import CrossEncoder model = CrossEncoder('Jsevisal/CrossEncoder-ModernBERT-base-qnli') scores = model.predict([('Query1', 'Paragraph1'), ('Query2', 'Paragraph2')]) #e.g. scores = model.predict([('How many people live in Berlin?', 'Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.'), ('What is the size of New York?', 'New York City is famous for the Metropolitan Museum of Art.')]) ``` ## Usage with Transformers AutoModel You can use the model also directly with Transformers library (without SentenceTransformers library): ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch model = AutoModelForSequenceClassification.from_pretrained('Jsevisal/CrossEncoder-ModernBERT-base-qnli') tokenizer = AutoTokenizer.from_pretrained('Jsevisal/CrossEncoder-ModernBERT-base-qnli') features = tokenizer(['How many people live in Berlin?', 'What is the size of New York?'], ['Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt") model.eval() with torch.no_grad(): scores = torch.nn.functional.sigmoid(model(**features).logits) print(scores) ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.98) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.49.0.dev0 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0

sabafallah/bge-reranker-base-Q4_K_M-GGUF

sabafallah

2025-04-02T15:07:02Z

7

0

sentence-transformers

[ "sentence-transformers", "gguf", "mteb", "text-embeddings-inference", "llama-cpp", "gguf-my-repo", "text-ranking", "en", "zh", "base_model:BAAI/bge-reranker-base", "base_model:quantized:BAAI/bge-reranker-base", "license:mit", "model-index", "endpoints_compatible", "region:us", "feature-extraction" ]

text-ranking

2025-04-01T17:20:17Z

--- base_model: BAAI/bge-reranker-base language: - en - zh library_name: sentence-transformers license: mit pipeline_tag: text-ranking tags: - mteb - text-embeddings-inference - llama-cpp - gguf-my-repo model-index: - name: bge-reranker-base results: - task: type: Reranking dataset: name: MTEB CMedQAv1 type: C-MTEB/CMedQAv1-reranking config: default split: test revision: None metrics: - type: map value: 81.27206722525007 - type: mrr value: 84.14238095238095 - task: type: Reranking dataset: name: MTEB CMedQAv2 type: C-MTEB/CMedQAv2-reranking config: default split: test revision: None metrics: - type: map value: 84.10369934291236 - type: mrr value: 86.79376984126984 - task: type: Reranking dataset: name: MTEB MMarcoReranking type: C-MTEB/Mmarco-reranking config: default split: dev revision: None metrics: - type: map value: 35.4600511272538 - type: mrr value: 34.60238095238095 - task: type: Reranking dataset: name: MTEB T2Reranking type: C-MTEB/T2Reranking config: default split: dev revision: None metrics: - type: map value: 67.27728847727172 - type: mrr value: 77.1315192743764 --- # sabafallah/bge-reranker-base-Q4_K_M-GGUF This model was converted to GGUF format from [`BAAI/bge-reranker-base`](https://huggingface.co/BAAI/bge-reranker-base) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/BAAI/bge-reranker-base) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo sabafallah/bge-reranker-base-Q4_K_M-GGUF --hf-file bge-reranker-base-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo sabafallah/bge-reranker-base-Q4_K_M-GGUF --hf-file bge-reranker-base-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo sabafallah/bge-reranker-base-Q4_K_M-GGUF --hf-file bge-reranker-base-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo sabafallah/bge-reranker-base-Q4_K_M-GGUF --hf-file bge-reranker-base-q4_k_m.gguf -c 2048 ```

LHRuig/dannydsx

LHRuig

2025-04-02T12:27:57Z

0

diffusers

[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]

text-to-image

2025-04-02T12:27:39Z

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: suit output: url: images/suit.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: dannydsx --- # dannydsx <Gallery /> ## Model description dannydsx lora ## Trigger words You should use `dannydsx` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/LHRuig/dannydsx/tree/main) them in the Files & versions tab.

LHRuig/danielmontoyasx

LHRuig

2025-04-02T12:26:02Z

0

diffusers

[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]

text-to-image

2025-04-02T12:25:43Z

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: suit output: url: images/suit.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: danielmontoyasx --- # danielmontoyasx <Gallery /> ## Model description danielmontoyasx lora ## Trigger words You should use `danielmontoyasx` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/LHRuig/danielmontoyasx/tree/main) them in the Files & versions tab.

jonatatyska/Qwen2.5-1.5B-Instruct-EmbodiedZero-GRPO-only2

jonatatyska

2025-04-02T12:24:47Z

9

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "grpo", "conversational", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-03-25T17:54:09Z

--- base_model: Qwen/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-EmbodiedZero-GRPO-only2 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-EmbodiedZero-GRPO-only2 This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="jonatatyska/Qwen2.5-1.5B-Instruct-EmbodiedZero-GRPO-only2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/ine-ufsc/huggingface/runs/rb1llhhq) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.5.1 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

bowilleatyou/8fa2fdb1-48db-4522-9ae7-9d23b77bc57f

bowilleatyou

2025-04-02T12:24:36Z

0

transformers

[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-04-02T11:27:14Z

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

LHRuig/connormaguiresx

LHRuig

2025-04-02T12:23:36Z

0

diffusers

[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]

text-to-image

2025-04-02T12:23:16Z

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: suit output: url: images/suit.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: connormaguiresx --- # connormaguiresx <Gallery /> ## Model description connormaguiresx lora ## Trigger words You should use `connormaguiresx` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/LHRuig/connormaguiresx/tree/main) them in the Files & versions tab.

SuryaPr8562/phi-3.5-transcription-summarizer

SuryaPr8562

2025-04-02T12:21:03Z

0

transformers

[ "transformers", "safetensors", "gguf", "llama", "text-generation-inference", "unsloth", "trl", "sft", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2025-04-02T11:55:15Z

--- base_model: unsloth/phi-3.5-mini-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** SuryaPr8562 - **License:** apache-2.0 - **Finetuned from model :** unsloth/phi-3.5-mini-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

LHRuig/caincfsx

LHRuig

2025-04-02T12:20:39Z

0

diffusers

[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]

text-to-image

2025-04-02T12:20:20Z

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: suit output: url: images/suit.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: caincfsx --- # caincfsx <Gallery /> ## Model description caincfsx lora ## Trigger words You should use `caincfsx` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/LHRuig/caincfsx/tree/main) them in the Files & versions tab.

Bobbeu/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-placid_leaping_dolphin

Bobbeu

2025-04-02T12:19:09Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am placid leaping dolphin", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-04-01T19:05:11Z

--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-placid_leaping_dolphin tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am placid leaping dolphin - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-placid_leaping_dolphin This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Bobbeu/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-placid_leaping_dolphin", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.50.3 - Pytorch: 2.5.1 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

mrlijun/SMR-R1

mrlijun

2025-04-02T12:18:06Z

12

2

null

[ "safetensors", "qwen2_5_vl", "medical", "Information Extraction", "Reinforcement Learning", "Visual Question Answering", "license:apache-2.0", "region:us" ]

null

2025-03-26T08:28:59Z

--- license: apache-2.0 tags: - medical - Information Extraction - Reinforcement Learning - Visual Question Answering --- # SMR-R1: Reinforcing Ability to Extract Structured Information From Medical Reports in Vision Language Models [![GitHub](https://img.shields.io/badge/GitHub-开源项目-blue)](https://github.com/yingyukexiansheng/SMR-R1) [![License](https://img.shields.io/badge/License-MIT-green)](LICENSE) ## 🎯Overview Given the remarkable performance of the GRPO algorithm on the DeepSeek-R1 model, we have also applied GRPO to the field of medical report structured extraction. We conducted reinforcement training on the Qwen2.5VL-7B model, and the results showed an improvement of 15 percentage points (pp) on the evaluation set compared to the Qwen2.5VL-7B model, 15 pp higher than the SFT training on the same model with the same data, and 7 pp higher than the Qwen2.5VL-72B model. This project aims to provide a solution for the structured extraction of medical reports. We have open-sourced the following content: - A desensitized medical report structured data evaluation set [🤗 SMR-R1 Dataset](https://huggingface.co/datasets/mrlijun/SMR-R1) - A medical report structured extraction model [🤗 SMR-R1 Model](https://huggingface.co/mrlijun/SMR-R1) - Methods for evaluating medical report structured extraction Through this project, researchers and developers can quickly get started with the task of structured extraction of medical reports and further conduct research and application development using the provided data and models. ## 🍖 Dataset We have open-sourced a structured data evaluation set for medical reports. This dataset features the following characteristics: - **Diversity and Representativeness**: The dataset encompasses various types of medical reports, such as medical records, examination reports, and diagnostic reports. It covers a range of imaging conditions, including fluoroscopy, oblique views, and variations in lighting intensity. Additionally, it includes non-medical report-related images (e.g., medicine boxes) to assess the model's ability to filter out non-medical images. - **High-Quality Annotation**: All data has been annotated and reviewed by professional medical personnel to ensure accuracy and reliability. - **Data Anonymization**: To protect patient privacy, all information in the dataset has been anonymized to ensure that no personally identifiable information is included. ## 🔄 Model We have open-sourced a medical report structured extraction model based on qwen2.5-vl-7b ### 🧪 Evaluation Modify MODEL_PATH, DATA_ROOT, IMAGE_ROOT in evaluate.py to your local directory, and then run the following command to evaluate the model performance: ```bash python evaluate.py ``` | Model | Dataset |Performance |Description | |:-----------|:-----------:|:-----------:|:-----------:| | Qwen2.5-VL-7B-Instruct | \ |54.71% | \ | | Qwen2.5-VL-72B-Instruct | \ | 62.89%| \ | | Qwen2.5-VL-7B-Instruct-sft | 6k | 55.21%| (Supervised Fine-Tuning) training based on Qwen2.5-VL-7B-Instruct. | | Qwen2.5-VL-7B-Instruct-rl(SMR-R1) | 6k | 71.87%| (Reinforcement Learning) training based on Qwen2.5-VL-7B-Instruct. | ### 📈 Training curve ![train.png](https://cdn-uploads.huggingface.co/production/uploads/64636db1c615cbc1244749cf/mX0VcOS5kPke671RRIgnm.png) ### 😊 Extract display ![OCR-65.png](https://cdn-uploads.huggingface.co/production/uploads/64636db1c615cbc1244749cf/3fzhHQ7tVJQyRXmeF7F5H.png) ```md <think> 从图片中可以看到这是一个血液学检查报告，包含了多种免疫球蛋白和补体的水平以及一些特定抗体的检测结果。由于图片中没有提供患者的姓名、性别、年龄、检查时间、科室、检查名称、检查部位等信息，因此这些字段无法填写。此外，报告中也没有提供检查描述、诊断、处理建议、样本采集时间和其他相关信息。因此，我们将这些字段留空。 </think> <answer> { "姓名": "", "性别": "", "年龄": "", "检查时间": "", "科室": "", "检查名称": "", "检查部位": "", "指标": [ {"项目名称": "免疫球蛋白G [IgG]", "结果": "12.10", "单位": "g/L", "参考范围": "5.28--21.9", "异常标记": "", "检测方法": "", "结果状态": "正常"}, {"项目名称": "免疫球蛋白A [IgA]", "结果": "3.48", "单位": "g/L", "参考范围": "0.43--2.53", "异常标记": "↑", "检测方法": "", "结果状态": "偏高"}, {"项目名称": "免疫球蛋白M [IgM]", "结果": "0.66", "单位": "g/L", "参考范围": "0.48--2.26", "异常标记": "", "检测方法": "", "结果状态": "正常"}, {"项目名称": "免疫球蛋白E [IgE]", "结果": "51.8", "单位": "IU/mL", "参考范围": "0--165", "异常标记": "", "检测方法": "", "结果状态": "正常"}, {"项目名称": "补体C3 [C3]", "结果": "1.34", "单位": "g/L", "参考范围": "0.7--2.06", "异常标记": "", "检测方法": "", "结果状态": "正常"}, {"项目名称": "补体C4 [C4]", "结果": "0.26", "单位": "g/L", "参考范围": "0.11--0.61", "异常标记": "", "检测方法": "", "结果状态": "正常"}, {"项目名称": "肺炎支原体抗体IgM [MP]", "结果": "阴性(-)", "单位": "", "参考范围": "", "异常标记": "", "检测方法": "", "结果状态": "正常"}, {"项目名称": "肺炎支原体抗体IgM滴度 [MP滴度]", "结果": "阴性(-)", "单位": "", "参考范围": "", "异常标记": "", "检测方法": "", "结果状态": "正常"}, {"项目名称": "肺炎衣原体抗体 [CPIgM]", "结果": "阴性(-)", "单位": "", "参考范围": "", "异常标记": "", "检测方法": "", "结果状态": "正常"}, {"项目名称": "柯萨奇病毒抗体 [CVB-IgM]", "结果": "阴性(-)", "单位": "", "参考范围": "", "异常标记": "", "检测方法": "", "结果状态": "正常"}, {"项目名称": "EB病毒壳抗原IgG [VCA-IgG]", "结果": "弱阳性(±)", "单位": "", "参考范围": "", "异常标记": "", "检测方法": "", "结果状态": "正常"} ], "检查描述": "", "诊断": "", "处理建议": "", "样本采集时间": "", "其他": "" } </answer> ``` ## 📋️ TODO - [X] Conduct reinforcement learning directly on the base model. - [ ] Perform reinforcement learning after SFT (Supervised Fine-Tuning) on the base model. - [ ] Do not impose restrictions on the extracted fields, allowing for the automatic structuring of medical reports. - [ ] Due to the significant impact on results from incorrect identification of some values or terms in medical reports, it is not advisable to readily use edit distance as the reward. The reward method will be optimized to be determined by a large model in the future. ## Acknowledgements We sincerely thank [DeepSeek](https://github.com/deepseek-ai/DeepSeek-R1), [QwenVL](https://github.com/QwenLM/Qwen2.5-VL), [vllm](https://github.com/vllm-project/vllm) (our initial codebase). ## 📚 Contributors and Citation If you find this work useful, please cite it as follows: ```bib @misc{lijun2025SMR-R1, author = {Lijun Liu, Tao Zhang, Tao Zhang, Chong Li, Mingrui Wang, Chenglin Zhu, Mingan Lin, Zenan Zhou, Weipeng Chen}, title = {SMR-R1: Reinforcing Ability to Extract Structured Information From Medical Reports in Vision Language Models}, howpublished = {\url{https://github.com/yingyukexiansheng/SMR-R1}}, year = {2025} } ```

hadi-ibra/ppo-LunarLander-v2

hadi-ibra

2025-04-02T12:17:38Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2025-04-02T12:17:17Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 239.87 +/- 21.75 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

dmaly/hands-flux

dmaly

2025-04-02T12:17:30Z

0

diffusers

[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]

text-to-image

2025-04-02T12:17:18Z

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: Screenshot output: url: images/Screenshot 2025-04-02 at 15.16.51.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: null --- # Flux Dev Hands&Fingers <Gallery /> ## Download model Weights for this model are available in Safetensors format. [Download](/dmaly/hands-flux/tree/main) them in the Files & versions tab.

MartinB77/product-classifier-model-B2

MartinB77

2025-04-02T12:16:24Z

0

transformers

[ "transformers", "safetensors", "distilbert", "text-classification", "product-classification", "pytorch", "en", "dataset:lokeshparab/amazon-products-dataset", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2025-04-02T12:04:21Z

--- language: en license: apache-2.0 tags: - product-classification - transformers - pytorch - distilbert datasets: - lokeshparab/amazon-products-dataset model-index: - name: Product Classifier B2 results: [] --- # Product Classifier B2 Tento model slouží k predikci kategorií produktů na základě jejich názvu nebo popisu... # 🏍️ Amazon Product Classifier (Balanced B2) This is a fine-tuned DistilBERT model for **multi-class classification** of product titles into Amazon-like product categories. The model is based on `distilbert-base-uncased` and was trained on a **balanced subset** of the Amazon Products dataset. ## 🧠 Model Architecture - Base: `distilbert-base-uncased` (6-layer, 768 hidden size) - Classification Head: 2 dense layers with dropout + ReLU - Output: softmax over 19 product categories ## 📊 Training Data The model was trained on a balanced subset (≈40k samples) of the [Amazon Products Dataset](https://www.kaggle.com/datasets/lokeshparab/amazon-products-dataset), which contains product titles and their corresponding categories. Preprocessing included: - Removing empty/missing titles - Keeping top-level categories only - Balancing the dataset to avoid category bias ## 🍿 Example Categories - beauty & health - home & kitchen - tv, audio & cameras - computers & accessories - clothing & accessories - appliances - sports & fitness - grocery & gourmet foods - ... (total 19) ## 🧪 Example Usage (Python) ```python from transformers import pipeline classifier = pipeline("text-classification", model="your-username/product-classifier-model-B2") result = classifier("Smartwatch with heart rate monitor and GPS tracking") print(result) # [{'label': 'stores', 'score': 0.94}] ``` ## 🚀 Intended Use The model is designed to help developers quickly classify product titles into e-commerce categories, useful for: - Auto-tagging items in online stores - Cleaning and organizing product catalogs - Building recommendation engines (in combination with embeddings) ## 📌 Limitations - English-only (trained on `distilbert-base-uncased`) - May not perform well on very short or ambiguous product names - Not suitable for legal/medical/financial applications ## 📄 License & Source - Model: MIT License - Training Data: [Amazon Products Dataset](https://www.kaggle.com/datasets/lokeshparab/amazon-products-dataset) on Kaggle (check license and attribution requirements on Kaggle page)

devcs0205/OpenR1-Qwen-7B-SFT

devcs0205

2025-04-02T12:16:02Z

6

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "sft", "conversational", "dataset:open-r1/OpenR1-Math-220k", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-03-28T10:56:05Z

--- base_model: Qwen/Qwen2.5-7B-Instruct datasets: open-r1/OpenR1-Math-220k library_name: transformers model_name: OpenR1-Qwen-7B-SFT tags: - generated_from_trainer - open-r1 - trl - sft licence: license --- # Model Card for OpenR1-Qwen-7B-SFT This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the [open-r1/OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="devcs0205/OpenR1-Qwen-7B-SFT", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/cskang-newen-ai/huggingface/runs/gs8lkf9j) This model was trained with SFT. ### Framework versions - TRL: 0.16.0 - Transformers: 4.50.0 - Pytorch: 2.5.1 - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

ArseniyPerchik/dummy-model

ArseniyPerchik

2025-04-02T12:15:32Z

0

transformers

[ "transformers", "safetensors", "camembert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2025-04-02T11:57:01Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Gustavobaby/tomisthemovementalliance

Gustavobaby

2025-04-02T12:15:26Z

0

diffusers

[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]

text-to-image

2025-04-02T12:15:23Z

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: Screenshot output: url: images/Capture d’écran 2025-04-02 à 14.15.20.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: null --- # tomisthemovementalliance <Gallery /> ## Download model Weights for this model are available in Safetensors format. [Download](/Gustavobaby/tomisthemovementalliance/tree/main) them in the Files & versions tab.

infly/INF-ORM-Llama3.1-70B

infly

2025-04-02T12:14:59Z

701

16

transformers

[ "transformers", "safetensors", "llama", "text-classification", "dataset:infly/INF-ORM-Preference-Magnitude-80K", "arxiv:2410.01257", "base_model:meta-llama/Llama-3.1-70B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-70B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-classification

2024-12-04T08:18:11Z

--- library_name: transformers base_model: meta-llama/Llama-3.1-70B-Instruct datasets: - infly/INF-ORM-Preference-Magnitude-80K pipeline_tag: text-classification --- <div align="center"> <img src="INF.jpg" width="300"/> 🤗 <a href="https://huggingface.co/infly" target="_blank">Hugging Face</a> <br> <br> <br> </div> # INF Outcome Reward Model ## Introduction [**INF-ORM-Llama3.1-70B**] is the outcome reward model roughly built on the [Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct) architecture and trained with the dataset [INF-ORM-Preference-Magnitude-80K](https://huggingface.co/datasets/infly/INF-ORM-Preference-Magnitude-80K). We did the following three things to improve the performance of our model. ### Data Pre-processing We trained it on the dataset [INF-ORM-Preference-Magnitude-80K](https://huggingface.co/datasets/infly/INF-ORM-Preference-Magnitude-80K), which is derived from the **decontaminated dataset** [Skywork/Skywork-Reward-Perference-80k-v0.2](https://huggingface.co/datasets/Skywork/Skywork-Reward-Preference-80K-v0.2). We use GPT-4o to evaluate the difference between the chosen answer and the rejected answer in the [Skywork/Skywork-Reward-Perference-80k-v0.2](https://huggingface.co/datasets/Skywork/Skywork-Reward-Preference-80K-v0.2) and then add the 'Magnitude' column in the dataset. The evaluation follows the following rules: 1. If the chosen answer is much better than rejected answer, set 'Magnitude' value $d$ to 3. 2. If the chosen answer is better than the rejected answer, set 'Magnitude' value $d$ to 2. 3. If the chosen answer is slightly better than rejected answer, set 'Magnitude' value $d$ to 1. After that, we train our model with the scaled BT loss. The scaled BT loss is defined as: $$\mathcal{L}_{Scaled-BT} = -\alpha*d*log(\sigma(r_{\theta}(x, y_{c})-r_{\theta}(x, y_{r})))$$ where $\alpha$ is the scaling factor. You can find more details about scaled BT loss here [1](https://arxiv.org/pdf/2410.01257). > Here we look at the performance gains of scaled BT loss from a different perspective than [1](https://arxiv.org/pdf/2410.01257). The scaled BT loss can be thought of as a form of cross-entropy, where the distribution of the difference of the logits produced by the model is sensitive to the distribution of the magnitude. Therefore, we improve the difference of the values in the 'Magnitude' column from 1, 2, 3 to 1, 3, 10 and finally get better performance. ### Modified Score Head We use the modified score head instead of origin score head. ```python # modified score head self.score = nn.Sequential( nn.Linear(config.hidden_size, config.hidden_size), nn.ReLU(), nn.Linear(config.hidden_size, 1) ) # origin score head self.score = nn.linear(config.hidden_size, 1) ``` ### Model Merge We trained two models and merge them with the weight $0.5$. | Model | Score | Chat | Chat Hard | Safety | Reasoning | | ----------------- | :---: | :---: | :-------: | :----: | :-------: | | INF-ORM-v1 | 94.3 | 96.1 | 88.2 | 94.6 | 98.2 | | INF-ORM-v2 | 94.4 | 95.5 | 90.8 | 93 | 99.1 | | INF-ORM-v3(Averaged) | 95.1 | 96.6 | 91.0 | 93.6 | 99.1 | ## RewardBench Leaderboard We evaluate our model on [RewardBench](https://huggingface.co/spaces/allenai/reward-bench) using the [official test script](https://github.com/allenai/reward-bench) locally. As of December 2024, INF-ORM-Llama3.1-70B ranks first on the RewardBench leaderboard. | Rank | Model | Model Type | Score | Chat | Chat Hard | Safety | Reasoning | | :---: | -------------------------------------------- | ----------------- | :---: | :---: | :-------: | :----: | :-------: | | 1 | **infly/INF-ORM-Llama3.1-70B** | Seq. Classifier | 95.1 | 96.6 | 91.0 | 93.6 | 99.1 | | 2 | Skywork/Skywork-Reward-Gemma-2-27B-v0.2 | Seq. Classifier | 94.3 | 96.1 | 89.9 | 93.0 | 98.1 | | 3 | nvidia/Llama-3.1-Nemotron-70B-Reward | Custom Classifier | 94.1 | 97.5 | 85.7 | 95.1 | 98.1 | | 4 | Skywork/Skywork-Reward-Gemma-2-27B | Seq. Classifier | 93.8 | 95.8 | 91.4 | 91.9 | 96.1 | | 5 | SF-Foundation/TextEval-Llama3.1-70B | Generative | 93.5 | 94.1 | 90.1 | 93.2 | 96.4 | | 6 | meta-metrics/MetaMetrics-RM-v1.0 | Custom Classifier | 93.4 | 98.3 | 86.4 | 90.8 | 98.2 | | 7 | Skywork/Skywork-Critic-Llama-3.1-70B | Generative | 93.3 | 96.6 | 87.9 | 93.1 | 95.5 | | 8 | Skywork/Skywork-Reward-Llama-3.1-8B-v0.2 | Seq. Classifier | 93.1 | 94.7 | 88.4 | 92.7 | 96.7 | | 9 | nicolinho/QRM-Llama3.1-8B | Seq. Classifier | 93.1 | 94.4 | 89.7 | 92.3 | 95.8 | | 10 | LxzGordon/URM-LLaMa-3.1-8B | Seq. Classifier | 92.9 | 95.5 | 88.2 | 91.1 | 97.0 | ## Demo Code We provide an example usage of the INF-ORM-Llama3.1-70B below. Below is an example of obtaining the reward scores of two conversations. ```python from typing import List, Optional, Union import torch import torch.nn as nn from transformers import LlamaPreTrainedModel, LlamaModel, PreTrainedTokenizerFast from transformers.modeling_outputs import SequenceClassifierOutputWithPast class INFORMForSequenceClassification(LlamaPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.model = LlamaModel(config) self.score = nn.Sequential( nn.Linear(config.hidden_size, config.hidden_size), nn.ReLU(), nn.Linear(config.hidden_size, self.num_labels) ) # Initialize weights and apply final processing self.post_init() def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): transformer_outputs = self.model( input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, ) hidden_states = transformer_outputs[0] logits = self.score(hidden_states) if input_ids is not None: batch_size = input_ids.shape[0] else: batch_size = inputs_embeds.shape[0] if self.config.pad_token_id is None and batch_size != 1: raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") if self.config.pad_token_id is None: sequence_lengths = -1 else: if input_ids is not None: # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 sequence_lengths = sequence_lengths % input_ids.shape[-1] sequence_lengths = sequence_lengths.to(logits.device) else: sequence_lengths = -1 pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] loss = None return SequenceClassifierOutputWithPast( loss=loss, logits=pooled_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) # Load model model_name = "infly/INF-ORM-Llama3.1-70B" orm = INFORMForSequenceClassification.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map="auto", attn_implementation="flash_attention_2", num_labels=1, ) # Load tokenizer tokenizer = PreTrainedTokenizerFast.from_pretrained(model_name) conv1 = [ { "content": "Tom has 20 oranges. He gave 5 oranges to his friend Lisa, then he bought 3 more oranges. Finally, he divided all the oranges equally among himself and his 4 friends. How many oranges does each person get?", "role": "user" }, { "content": "Tom has 20 oranges. He gave 5 oranges to Lisa. 20 - 5 = 15. Tom now has 15 oranges. Tom bought 3 more oranges. 15 + 3 = 18. Tom now has 18 oranges. Tom divides the 18 oranges equally among himself and his 4 friends (a total of 5 people). 18 ÷ 5 = 3.6 oranges. Each person gets 3.6 oranges.", "role": "assistant" } ] conv2= [ { "content": "Tom has 20 oranges. He gave 5 oranges to his friend Lisa, then he bought 3 more oranges. Finally, he divided all the oranges equally among himself and his 4 friends. How many oranges does each person get?", "role": "user" }, { "content": "Tom has 20 oranges. He gave 5 oranges to his friend Lisa. 20 - 5 = 15. Tom now has 15 oranges. Tom bought 3 more oranges. 15 + 3 = 18. Tom now has 18 oranges. Tom divides the 18 oranges equally among his 4 friends (a total of 4 people). 18 ÷ 4 = 4.5 oranges. Each person gets 4.5 oranges.", "role": "assistant" } ] conv1_tokenized = tokenizer.apply_chat_template(conv1, tokenize=True, return_tensors="pt").to("cuda") conv2_tokenized = tokenizer.apply_chat_template(conv2, tokenize=True, return_tensors="pt").to("cuda") # Inference with torch.no_grad(): score1 = orm(conv1_tokenized).logits[0][0].item() score2 = orm(conv2_tokenized).logits[0][0].item() print(f"Score for response 1: {score1}") print(f"Score for response 2: {score2}") # Output: # Score for response 1: 4.96875 # Score for response 2: 2.890625 ``` ## License Agreement INF-ORM-Llama3.1-70B support commercial applications under a permissive [License](https://huggingface.co/infly/INF-ORM-Llama3.1-70B/blob/main/LICENSE). ## Contributors ### Supervisors Wei Chu • Yuan Qi ### RM Team Minghao Yang • Chao Qu • Xiaoyu Tan ## Contact If you have any questions, please feel free to reach us at Minghao Yang <[email protected]>, Chao Qu <[email protected]> and Xiaoyu Tan <[email protected]>. ## Acknowledgement This work was done during my internship at INF. I would like to thank my mentor (Qu Chao, Tan Xiaoyu) and the INF team for their support. Their insights and expertise greatly contributed to the successful completion of this work. ## How to cite ```bibtex @misc{INF-ORM-Llama3.1-70B, url={[https://huggingface.co/infly/INF-ORM-Llama3.1-70B](https://huggingface.co/infly/INF-ORM-Llama3.1-70B)}, title={INF-ORM-Llama3.1-70B}, year={2024}, author={Minghao Yang, Chao Qu, Xiaoyu Tan} } ```

LHRuig/blakelarsonsx

LHRuig

2025-04-02T12:10:07Z

0

diffusers

[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]

text-to-image

2025-04-02T12:09:44Z

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: suit output: url: images/suit.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: blakelarsonsx --- # blakelarsonsx <Gallery /> ## Model description blakelarsonsx lora ## Trigger words You should use `blakelarsonsx` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/LHRuig/blakelarsonsx/tree/main) them in the Files & versions tab.