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Lekhansh/Llama-3.2-3B-Instruct-Scientific-Text-Cleaner_merged	Lekhansh	2025-04-28T09:33:19Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T09:28:10Z	--- 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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sknow-lab/Gemma3-12B-CIC-SciCite	sknow-lab	2025-04-28T09:32:06Z	0	1	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "scientometrics", "citation_analysis", "citation_intent_classification", "text-generation", "conversational", "en", "dataset:allenai/scicite", "arxiv:2502.14561", "base_model:google/gemma-3-12b-it", "base_model:finetune:google/gemma-3-12b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-25T13:30:58Z	--- license: gemma datasets: - allenai/scicite language: - en metrics: - f1 base_model: - google/gemma-3-12b-it pipeline_tag: text-generation library_name: transformers tags: - scientometrics - citation_analysis - citation_intent_classification --- # Gemma3-12B-CIC-SciCite A fine-tuned model for Citation Intent Classification, based on [Gemma 3 - 12B Instruct](https://huggingface.co/google/gemma-3-12b-it) and trained on the [SciCite](https://huggingface.co/datasets/allenai/scicite) dataset. GGUF Version: https://huggingface.co/sknow-lab/Gemma3-12B-CIC-SciCite-GGUF ## SciCite classes \| Class \| Definition \| \| --- \| --- \| \| Background information \| The citation states, mentions, or points to the background information giving more context about a problem, concept, approach, topic, or importance of the problem in the field. \| \| Method \| Making use of a method, tool, approach or dataset. \| \| Result comparison \| Comparison of the paper’s results/findings with the results/findings of other work. \| ## Quickstart ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "sknow-lab/Gemma3-12B-CIC-SciCite" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) system_prompt = """ # CONTEXT # You are an expert researcher tasked with classifying the intent of a citation in a scientific publication. ######## # OBJECTIVE # You will be given a sentence containing a citation. You must classify the intent of the citation by assigning it to one of three predefined classes. ######## # CLASS DEFINITIONS # The three (3) possible classes are the following: "background information", "method", "results comparison." 1 - background information: The citation states, mentions, or points to the background information giving more context about a problem, concept, approach, topic, or importance of the problem in the field. 2 - method: Making use of a method, tool, approach, or dataset. 3 - results comparison: Comparison of the paper’s results/findings with the results/findings of other work. ######## # RESPONSE RULES # - Analyze only the citation marked with the @@CITATION tag. - Assign exactly one class to each citation. - Respond only with the exact name of one of the following classes: "background information", "method", or "results comparison". - Do not provide any explanation or elaboration. """ test_citing_sentence = "Activated PBMC are the basis of the standard PBMC blast assay for HIV-1 neutralization, whereas the various GHOST and HeLa cell lines have all been used in neutralization assays @@CITATION@@." user_prompt = f""" {test_citing_sentence} ### Question: Which is the most likely intent for this citation? a) background information b) method c) results comparison ### Answer: """ messages = [ {"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] # Response: method ``` Details about the system prompts and query templates can be found in the paper. There might be a need for a cleanup function to extract the predicted label from the output. You can find ours on [GitHub](https://github.com/athenarc/CitationIntentOpenLLM/blob/main/citation_intent_classification_experiments.py). ## Citation ``` @misc{koloveas2025llmspredictcitationintent, title={Can LLMs Predict Citation Intent? An Experimental Analysis of In-context Learning and Fine-tuning on Open LLMs}, author={Paris Koloveas and Serafeim Chatzopoulos and Thanasis Vergoulis and Christos Tryfonopoulos}, year={2025}, eprint={2502.14561}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2502.14561}, } ```
Triangle104/Qwen2.5-0.5B-Instruct-Q6_K-GGUF	Triangle104	2025-04-28T09:19:06Z	7	0	null	[ "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:quantized:Qwen/Qwen2.5-0.5B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-09-22T17:51:40Z	--- base_model: Qwen/Qwen2.5-0.5B-Instruct language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct/blob/main/LICENSE pipeline_tag: text-generation tags: - chat - llama-cpp - gguf-my-repo --- # Triangle104/Qwen2.5-0.5B-Instruct-Q6_K-GGUF This model was converted to GGUF format from [`Qwen/Qwen2.5-0.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) 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/Qwen/Qwen2.5-0.5B-Instruct) 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 Triangle104/Qwen2.5-0.5B-Instruct-Q6_K-GGUF --hf-file qwen2.5-0.5b-instruct-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Qwen2.5-0.5B-Instruct-Q6_K-GGUF --hf-file qwen2.5-0.5b-instruct-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 Triangle104/Qwen2.5-0.5B-Instruct-Q6_K-GGUF --hf-file qwen2.5-0.5b-instruct-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Qwen2.5-0.5B-Instruct-Q6_K-GGUF --hf-file qwen2.5-0.5b-instruct-q6_k.gguf -c 2048 ```
sjahr/ppo-LunarLander-v2	sjahr	2025-04-28T08:23:48Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-04-28T08:23:28Z	--- 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: 241.42 +/- 31.44 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 ... ```
Lingyue1/Light-R1	Lingyue1	2025-04-28T08:16:57Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-27T07:00:26Z	--- library_name: transformers license: other base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B tags: - llama-factory - full - generated_from_trainer model-index: - name: Light-R1-ly 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. --> # Light-R1 This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co//lamda12/zhouz/models/DeepSeek-R1-Distill-Qwen-7B) on the light_r1_3k 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: 5e-06 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.45.2 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
neginr/r1_annotated_finqa	neginr	2025-04-28T08:13:50Z	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	2025-04-28T08:07:14Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: r1_annotated_finqa 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. --> # r1_annotated_finqa 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/r1_annotated_finqa 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: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 32 - gradient_accumulation_steps: 3 - total_train_batch_size: 96 - total_eval_batch_size: 256 - 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 2.5.1 - Datasets 3.0.2 - Tokenizers 0.20.3
mlfoundations-dev/opencodereasoning_1k	mlfoundations-dev	2025-04-28T08:05:14Z	0	0	transformers	[ "transformers", "tensorboard", "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:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T08:02:36Z	--- library_name: transformers license: other base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: opencodereasoning_1k 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. --> # opencodereasoning_1k 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/opencodereasoning_1k 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: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 24 - total_train_batch_size: 96 - total_eval_batch_size: 32 - 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: 0.1 - num_epochs: 7.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.0a0+b465a5843b.nv24.09 - Datasets 3.5.0 - Tokenizers 0.20.3
kkks05/Llama-3.2-1B-instruct-lora_spider	kkks05	2025-04-28T08:04:01Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-28T05:26:10Z	--- base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: kkks05 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-1b-instruct-unsloth-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)
mradermacher/YOYO-O1-32B-V4-preview4-i1-GGUF	mradermacher	2025-04-28T08:00:06Z	10	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:YOYO-AI/YOYO-O1-32B-V4-preview4", "base_model:quantized:YOYO-AI/YOYO-O1-32B-V4-preview4", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-04-28T03:43:16Z	--- base_model: YOYO-AI/YOYO-O1-32B-V4-preview4 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/YOYO-AI/YOYO-O1-32B-V4-preview4 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-i1-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.i1-IQ1_S.gguf) \| i1-IQ1_S \| 7.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-i1-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.i1-IQ1_M.gguf) \| i1-IQ1_M \| 8.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-i1-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-i1-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 10.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-i1-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.i1-IQ2_S.gguf) \| i1-IQ2_S \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-i1-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.i1-IQ2_M.gguf) \| i1-IQ2_M \| 11.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-i1-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 11.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-i1-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.i1-Q2_K.gguf) \| i1-Q2_K \| 12.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-i1-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 12.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-i1-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 13.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-i1-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 14.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-i1-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.i1-IQ3_S.gguf) \| i1-IQ3_S \| 14.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-i1-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.i1-IQ3_M.gguf) \| i1-IQ3_M \| 14.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-i1-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 16.0 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-i1-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 17.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-i1-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 17.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-i1-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.i1-Q4_0.gguf) \| i1-Q4_0 \| 18.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-i1-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 18.9 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-i1-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 19.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-i1-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.i1-Q4_1.gguf) \| i1-Q4_1 \| 20.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-i1-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-i1-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 23.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/YOYO-O1-32B-V4-preview4-i1-GGUF/resolve/main/YOYO-O1-32B-V4-preview4.i1-Q6_K.gguf) \| i1-Q6_K \| 27.0 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mlfoundations-dev/d1_science_shortest_3k	mlfoundations-dev	2025-04-28T07:49:06Z	0	0	transformers	[ "transformers", "tensorboard", "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:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T07:46:42Z	--- library_name: transformers license: other base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: d1_science_shortest_3k 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. --> # d1_science_shortest_3k 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/d1_science_shortest_3k 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: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - gradient_accumulation_steps: 6 - total_train_batch_size: 96 - total_eval_batch_size: 128 - 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 2.6.0a0+ecf3bae40a.nv25.01 - Datasets 3.5.0 - Tokenizers 0.20.3
shibajustfor/eb908b1f-1b3d-4348-b3ea-1aec8aa320b5	shibajustfor	2025-04-28T07:46:44Z	0	0	peft	[ "peft", "generated_from_trainer", "base_model:tokyotech-llm/Llama-3-Swallow-8B-v0.1", "base_model:adapter:tokyotech-llm/Llama-3-Swallow-8B-v0.1", "region:us" ]	null	2025-04-28T07:46:19Z	--- library_name: peft tags: - generated_from_trainer base_model: tokyotech-llm/Llama-3-Swallow-8B-v0.1 model-index: - name: shibajustfor/eb908b1f-1b3d-4348-b3ea-1aec8aa320b5 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. --> # shibajustfor/eb908b1f-1b3d-4348-b3ea-1aec8aa320b5 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5814 ## 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 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
bharathsj/llama-3.2-3b-v1	bharathsj	2025-04-28T07:44:31Z	0	0	null	[ "safetensors", "llama", "license:apache-2.0", "region:us" ]	null	2025-04-28T07:33:00Z	--- license: apache-2.0 ---
Triangle104/Qwen2.5-3B-Instruct-Q5_K_M-GGUF	Triangle104	2025-04-28T07:34:19Z	4	0	null	[ "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:quantized:Qwen/Qwen2.5-3B-Instruct", "license:other", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-09-19T16:38:42Z	--- base_model: Qwen/Qwen2.5-3B-Instruct language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara license: other license_name: qwen-research license_link: https://huggingface.co/Qwen/Qwen2.5-3B-Instruct/blob/main/LICENSE pipeline_tag: text-generation tags: - chat - llama-cpp - gguf-my-repo --- # Triangle104/Qwen2.5-3B-Instruct-Q5_K_M-GGUF This model was converted to GGUF format from [`Qwen/Qwen2.5-3B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) 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/Qwen/Qwen2.5-3B-Instruct) 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 Triangle104/Qwen2.5-3B-Instruct-Q5_K_M-GGUF --hf-file qwen2.5-3b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Qwen2.5-3B-Instruct-Q5_K_M-GGUF --hf-file qwen2.5-3b-instruct-q5_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 Triangle104/Qwen2.5-3B-Instruct-Q5_K_M-GGUF --hf-file qwen2.5-3b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Qwen2.5-3B-Instruct-Q5_K_M-GGUF --hf-file qwen2.5-3b-instruct-q5_k_m.gguf -c 2048 ```
mlfoundations-dev/c1_code_10d_16s_3k	mlfoundations-dev	2025-04-28T07:32:01Z	2	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	2025-04-27T23:40:04Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: c1_code_10d_16s_3k 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. --> # c1_code_10d_16s_3k 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/c1_code_10d_16s_3k 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: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 24 - total_train_batch_size: 96 - total_eval_batch_size: 32 - 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: 0.1 - num_epochs: 7.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
willg6872/lora_model	willg6872	2025-04-28T07:13:30Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation-inference", "unsloth", "trl", "en", "base_model:unsloth/llama-2-7b-bnb-4bit", "base_model:finetune:unsloth/llama-2-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-28T06:58:49Z	--- base_model: unsloth/llama-2-7b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: willg6872 - License: apache-2.0 - Finetuned from model : unsloth/llama-2-7b-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)
HoseaDev/llama3.1-alpaca-gpt4-data-zh	HoseaDev	2025-04-28T05:52:25Z	0	0	null	[ "gguf", "llama", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-28T04:55:12Z	--- license: apache-2.0 ---
Triangle104/Qwen2.5-32B-Instruct-Q3_K_L-GGUF	Triangle104	2025-04-28T05:31:05Z	4	0	transformers	[ "transformers", "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "arxiv:2407.10671", "base_model:Qwen/Qwen2.5-32B-Instruct", "base_model:quantized:Qwen/Qwen2.5-32B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-12-29T14:34:15Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-32B-Instruct/blob/main/LICENSE language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara pipeline_tag: text-generation base_model: Qwen/Qwen2.5-32B-Instruct tags: - chat - llama-cpp - gguf-my-repo library_name: transformers --- # Triangle104/Qwen2.5-32B-Instruct-Q3_K_L-GGUF This model was converted to GGUF format from [`Qwen/Qwen2.5-32B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) 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/Qwen/Qwen2.5-32B-Instruct) for more details on the model. --- Model Details: - Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains. Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots. Long-context Support up to 128K tokens and can generate up to 8K tokens. Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. This repo contains the instruction-tuned 32B Qwen2.5 model, which has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias Number of Parameters: 32.5B Number of Paramaters (Non-Embedding): 31.0B Number of Layers: 64 Number of Attention Heads (GQA): 40 for Q and 8 for KV Context Length: Full 131,072 tokens and generation 8192 tokens Please refer to this section for detailed instructions on how to deploy Qwen2.5 for handling long texts. For more details, please refer to our blog, GitHub, and Documentation. Requirements The code of Qwen2.5 has been in the latest Hugging face transformers and we advise you to use the latest version of transformers. With transformers<4.37.0, you will encounter the following error: KeyError: 'qwen2' Quickstart Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents. from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen2.5-32B-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Give me a short introduction to large language model." messages = [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] Processing Long Texts The current config.json is set for context length up to 32,768 tokens. To handle extensive inputs exceeding 32,768 tokens, we utilize YaRN, a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts. For supported frameworks, you could add the following to config.json to enable YaRN: { ..., "rope_scaling": { "factor": 4.0, "original_max_position_embeddings": 32768, "type": "yarn" } } For deployment, we recommend using vLLM. Please refer to our Documentation for usage if you are not familar with vLLM. Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts. We advise adding the rope_scaling configuration only when processing long contexts is required. Evaluation & Performance Detailed evaluation results are reported in this 📑 blog. For requirements on GPU memory and the respective throughput, see results here. Citation If you find our work helpful, feel free to give us a cite. @misc{qwen2.5, title = {Qwen2.5: A Party of Foundation Models}, url = {https://qwenlm.github.io/blog/qwen2.5/}, author = {Qwen Team}, month = {September}, year = {2024} } @article{qwen2, title={Qwen2 Technical Report}, author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan}, journal={arXiv preprint arXiv:2407.10671}, year={2024} } --- ## 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 Triangle104/Qwen2.5-32B-Instruct-Q3_K_L-GGUF --hf-file qwen2.5-32b-instruct-q3_k_l.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Qwen2.5-32B-Instruct-Q3_K_L-GGUF --hf-file qwen2.5-32b-instruct-q3_k_l.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 Triangle104/Qwen2.5-32B-Instruct-Q3_K_L-GGUF --hf-file qwen2.5-32b-instruct-q3_k_l.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Qwen2.5-32B-Instruct-Q3_K_L-GGUF --hf-file qwen2.5-32b-instruct-q3_k_l.gguf -c 2048 ```
TOMFORD79/S6	TOMFORD79	2025-04-28T05:06:02Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-04-28T04:02:34Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
mradermacher/Broken-Tutu-24B-GGUF	mradermacher	2025-04-28T03:43:19Z	0	1	transformers	[ "transformers", "gguf", "nsfw", "explicit", "roleplay", "unaligned", "ERP", "Erotic", "Horror", "Violence", "en", "base_model:ReadyArt/Broken-Tutu-24B", "base_model:quantized:ReadyArt/Broken-Tutu-24B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-27T19:13:31Z	--- base_model: ReadyArt/Broken-Tutu-24B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - nsfw - explicit - roleplay - unaligned - ERP - Erotic - Horror - Violence --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/ReadyArt/Broken-Tutu-24B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Broken-Tutu-24B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Broken-Tutu-24B-GGUF/resolve/main/Broken-Tutu-24B.Q2_K.gguf) \| Q2_K \| 9.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Broken-Tutu-24B-GGUF/resolve/main/Broken-Tutu-24B.Q3_K_S.gguf) \| Q3_K_S \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Broken-Tutu-24B-GGUF/resolve/main/Broken-Tutu-24B.Q3_K_M.gguf) \| Q3_K_M \| 11.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Broken-Tutu-24B-GGUF/resolve/main/Broken-Tutu-24B.Q3_K_L.gguf) \| Q3_K_L \| 12.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Broken-Tutu-24B-GGUF/resolve/main/Broken-Tutu-24B.IQ4_XS.gguf) \| IQ4_XS \| 13.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Broken-Tutu-24B-GGUF/resolve/main/Broken-Tutu-24B.Q4_K_S.gguf) \| Q4_K_S \| 13.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Broken-Tutu-24B-GGUF/resolve/main/Broken-Tutu-24B.Q4_K_M.gguf) \| Q4_K_M \| 14.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Broken-Tutu-24B-GGUF/resolve/main/Broken-Tutu-24B.Q5_K_S.gguf) \| Q5_K_S \| 16.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Broken-Tutu-24B-GGUF/resolve/main/Broken-Tutu-24B.Q5_K_M.gguf) \| Q5_K_M \| 16.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Broken-Tutu-24B-GGUF/resolve/main/Broken-Tutu-24B.Q6_K.gguf) \| Q6_K \| 19.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Broken-Tutu-24B-GGUF/resolve/main/Broken-Tutu-24B.Q8_0.gguf) \| Q8_0 \| 25.2 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Jongsim/Qwen2.5-32B-AGI-4.7bpw-exl2	Jongsim	2025-04-28T02:23:41Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "dataset:anthracite-org/kalo-opus-instruct-22k-no-refusal", "dataset:unalignment/toxic-dpo-v0.2", "dataset:Orion-zhen/dpo-toxic-zh", "base_model:Qwen/Qwen2.5-32B-Instruct", "base_model:finetune:Qwen/Qwen2.5-32B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-09-27T15:47:33Z	--- license: apache-2.0 language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara base_model: - Qwen/Qwen2.5-32B-Instruct datasets: - anthracite-org/kalo-opus-instruct-22k-no-refusal - unalignment/toxic-dpo-v0.2 - Orion-zhen/dpo-toxic-zh library_name: transformers --- AGI means Aspirational Grand Illusion First Qwen2.5 32B Finetune, to fix its Hypercensuritis > Hyper means high, and censura means censor, the suffix "-itis" is used to denote inflammation of a particular part or organ of the body.
jonahdvt/whisper-large-cv-fleurs-afri	jonahdvt	2025-04-28T00:21:10Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "sw,yo,ha,ig,lg", "generated_from_trainer", "multilingual", "dataset:common_voice", "base_model:jonahdvt/whisper-fleurs-large-afri", "base_model:finetune:jonahdvt/whisper-fleurs-large-afri", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-04-27T18:58:44Z	--- library_name: transformers language: - multilingual license: apache-2.0 base_model: jonahdvt/whisper-fleurs-large-afri tags: - sw,yo,ha,ig,lg - generated_from_trainer datasets: - common_voice model-index: - name: "Whisper Large \u2014 Common Voice Afri" 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. --> # Whisper Large — Common Voice Afri This model is a fine-tuned version of [jonahdvt/whisper-fleurs-large-afri](https://huggingface.co/jonahdvt/whisper-fleurs-large-afri) on the Common Voice 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: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 4600 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.48.3 - Pytorch 2.6.0+cu124 - Datasets 3.3.2 - Tokenizers 0.21.0
qingy2024/Fusion4-14B-Instruct	qingy2024	2025-04-27T23:53:33Z	15	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "arxiv:2306.01708", "base_model:Qwen/Qwen2.5-14B", "base_model:merge:Qwen/Qwen2.5-14B", "base_model:arcee-ai/Virtuoso-Small", "base_model:merge:arcee-ai/Virtuoso-Small", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-12-25T21:15:46Z	--- base_model: - arcee-ai/Virtuoso-Small - Qwen/Qwen2.5-14B library_name: transformers tags: - mergekit - merge language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [Qwen/Qwen2.5-14B](https://huggingface.co/Qwen/Qwen2.5-14B) as a base. ### Models Merged The following models were included in the merge: * [arcee-ai/Virtuoso-Small](https://huggingface.co/arcee-ai/Virtuoso-Small) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: arcee-ai/Virtuoso-Small parameters: weight: 1 density: 1 merge_method: ties base_model: Qwen/Qwen2.5-14B parameters: weight: 1 density: 1 normalize: true int8_mask: true dtype: float16 ```
shavkatsharipo/samhari-flex-LoRA	shavkatsharipo	2025-04-27T21:54:28Z	2	0	null	[ "license:other", "region:us" ]	null	2025-04-26T17:43:13Z	--- 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 ---
rnzrwnmgbry/rnz	rnzrwnmgbry	2025-04-27T21:13:05Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-27T21:13:05Z	--- license: apache-2.0 ---
Yudees28/MedRefine-QA	Yudees28	2025-04-27T19:37:11Z	0	0	null	[ "safetensors", "bert", "region:us" ]	null	2025-04-27T19:20:03Z	MedRefine-QA A medical question-answering system that provides reliable and accurate health information through semantic matching techniques. Designed to help users find trustworthy answers to common medical queries while emphasizing the importance of professional medical advice. Overview MedRefine-QA leverages domain-specific language models and semantic similarity matching to deliver accurate responses to medical questions. The system combines the strengths of biomedical models like PubMedBERT and Bio_ClinicalBERT with a curated dataset of verified medical information. Features Semantic matching using S-BioBert embeddings for question understanding Fallback mechanisms for questions outside the knowledge base Appropriate disclaimers regarding professional medical advice Simple and intuitive interface for medical inquiries Limitations This model is intended for informational purposes only and should not replace professional medical advice, diagnosis, or treatment. Users should always consult qualified healthcare providers for medical concerns. Citation If you use this model in your research, please cite: @software{MedRefine-QA, title = {MedRefine-QA: A Medical Question-Answering System}, year = {2025}, url = {https://huggingface.co/Yudees28/MedRefine-QA} }
agumi322/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-frisky_toothy_wallaby	agumi322	2025-04-27T15:52:06Z	4	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am frisky toothy wallaby", "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	2025-04-06T07:48:01Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-frisky_toothy_wallaby tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am frisky toothy wallaby - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-frisky_toothy_wallaby 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="agumi322/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-frisky_toothy_wallaby", 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: 4.51.3 - 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}} } ```
mlfoundations-dev/c1_science_nod_1s_3k	mlfoundations-dev	2025-04-27T15:20:09Z	0	0	transformers	[ "transformers", "tensorboard", "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:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-27T15:16:33Z	--- library_name: transformers license: other base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: c1_science_nod_1s_3k 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. --> # c1_science_nod_1s_3k 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/c1_science_nod_1s_3k 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: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - gradient_accumulation_steps: 6 - total_train_batch_size: 96 - total_eval_batch_size: 128 - 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 2.6.0a0+ecf3bae40a.nv25.01 - Datasets 3.5.0 - Tokenizers 0.20.3
nsadeq/ReDis-Mistral	nsadeq	2025-04-27T15:11:06Z	0	0	transformers	[ "transformers", "safetensors", "Inductive", "Reasoning", "text-generation", "en", "dataset:nsadeq/redis_generate_rule_alignment", "dataset:nsadeq/redis_generate_rule_sft", "dataset:nsadeq/redis_follow_rule_sft", "arxiv:2504.10647", "base_model:mistralai/Mistral-7B-Instruct-v0.3", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.3", "endpoints_compatible", "region:us" ]	text-generation	2025-04-27T15:03:00Z	--- library_name: transformers tags: - Inductive - Reasoning datasets: - nsadeq/redis_generate_rule_alignment - nsadeq/redis_generate_rule_sft - nsadeq/redis_follow_rule_sft language: - en base_model: - mistralai/Mistral-7B-Instruct-v0.3 pipeline_tag: text-generation --- # Model Card for Model ID ReDis-Llama is trained for improved inductive reasoning performance. ### Model Description - Developed by: Nafis Sadeq - Language(s) (NLP): English - Finetuned from model: mistralai/Mistral-7B-Instruct-v0.3 ### Model Sources [optional] - Repository: https://github.com/NafisSadeq/reasoning-distillation - Paper: https://arxiv.org/abs/2504.10647 ## How to Get Started with the Model Follow the instructions here: https://github.com/NafisSadeq/reasoning-distillation ## Training Details Training details can be found in the paper: https://arxiv.org/abs/2504.10647 ## Environmental Impact - Hardware Type: 2 × 48 GB Nvidia RTX A6000 GPUs - Hours used: 72 hours ### Model Architecture and Objective This model has the same architecture as mistralai/Mistral-7B-Instruct-v0.3 ### Compute Infrastructure 2 × 48 GB Nvidia RTX A6000 GPUs ## Citation If you use this model, please cite the following paper. @misc{sadeq2025improvingincontextlearningreasoning, title={Improving In-Context Learning with Reasoning Distillation}, author={Nafis Sadeq and Xin Xu and Zhouhang Xie and Julian McAuley and Byungkyu Kang and Prarit Lamba and Xiang Gao}, year={2025}, eprint={2504.10647}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2504.10647}, }
fhaslam/Llama-3.2-1B-Financial-Sentiment11	fhaslam	2025-04-27T15:09:57Z	0	0	transformers	[ "transformers", "safetensors", "facebook", "meta", "pytorch", "llama", "llama-3", "text-generation", "conversational", "en", "de", "fr", "it", "pt", "hi", "es", "th", "arxiv:2204.05149", "arxiv:2405.16406", "license:llama3.2", "endpoints_compatible", "region:us" ]	text-generation	2025-04-27T15:09:49Z	--- language: - en - de - fr - it - pt - hi - es - th library_name: transformers pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 license: llama3.2 extra_gated_prompt: >- ### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT Llama 3.2 Version Release Date: September 25, 2024 “Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. “Documentation” means the specifications, manuals and documentation accompanying Llama 3.2 distributed by Meta at https://llama.meta.com/doc/overview. “Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. “Llama 3.2” means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://www.llama.com/llama-downloads. “Llama Materials” means, collectively, Meta’s proprietary Llama 3.2 and Documentation (and any portion thereof) made available under this Agreement. “Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. 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If you access or use Llama 3.2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy). #### Prohibited Uses We want everyone to use Llama 3.2 safely and responsibly. You agree you will not use, or allow others to use, Llama 3.2 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. 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Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ) * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama 3.2: [email protected] extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: >- The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- ## Model Information The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. Model Developer: Meta Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. \| \| Training Data \| Params \| Input modalities \| Output modalities \| Context Length \| GQA \| Shared Embeddings \| Token count \| Knowledge cutoff \| \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| \| Llama 3.2 (text only) \| A new mix of publicly available online data. \| 1B (1.23B) \| Multilingual Text \| Multilingual Text and code \| 128k \| Yes \| Yes \| Up to 9T tokens \| December 2023 \| \| \| \| 3B (3.21B) \| Multilingual Text \| Multilingual Text and code \| \| \| \| \| \| \| Llama 3.2 Quantized (text only) \| A new mix of publicly available online data. \| 1B (1.23B) \| Multilingual Text \| Multilingual Text and code \| 8k \| Yes \| Yes \| Up to 9T tokens \| December 2023 \| \| \| \| 3B (3.21B) \| Multilingual Text \| Multilingual Text and code \| \| \| \| \| \| Supported Languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly. Llama 3.2 Model Family: Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability. Model Release Date: Sept 25, 2024 Status: This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety. License: Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement). Feedback: Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use Intended Use Cases: Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. Similarly, quantized models can be adapted for a variety of on-device use-cases with limited compute resources. Out of Scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card. ## How to use This repository contains two versions of Llama-3.2-1B-Instruct, for use with transformers and with the original `llama` codebase. ### Use with transformers Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function. Make sure to update your transformers installation via `pip install --upgrade transformers`. ```python import torch from transformers import pipeline model_id = "meta-llama/Llama-3.2-1B-Instruct" pipe = pipeline( "text-generation", model=model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] outputs = pipe( messages, max_new_tokens=256, ) print(outputs[0]["generated_text"][-1]) ``` Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes) ### Use with `llama` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama) To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Llama-3.2-1B-Instruct --include "original/" --local-dir Llama-3.2-1B-Instruct ``` ## Hardware and Software Training Factors:* We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure. Training Energy Use: Training utilized a cumulative of 916k GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. Training Greenhouse Gas Emissions: Estimated total location-based greenhouse gas emissions were 240 tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq. \| \| Training Time (GPU hours) \| Logit Generation Time (GPU Hours) \| Training Power Consumption (W) \| Training Location-Based Greenhouse Gas Emissions (tons CO2eq) \| Training Market-Based Greenhouse Gas Emissions (tons CO2eq) \| \| :---- \| :---: \| ----- \| :---: \| :---: \| :---: \| \| Llama 3.2 1B \| 370k \| \- \| 700 \| 107 \| 0 \| \| Llama 3.2 3B \| 460k \| \- \| 700 \| 133 \| 0 \| \| Llama 3.2 1B SpinQuant \| 1.7 \| 0 \| 700 \| Negligible\\ \| 0 \| \| Llama 3.2 3B SpinQuant \| 2.4 \| 0 \| 700 \| Negligible\\ \| 0 \| \| Llama 3.2 1B QLora \| 1.3k \| 0 \| 700 \| 0.381 \| 0 \| \| Llama 3.2 3B QLora \| 1.6k \| 0 \| 700 \| 0.461 \| 0 \| \| Total \| 833k \| 86k \| \| 240 \| 0 \| \\ The location-based CO2e emissions of Llama 3.2 1B SpinQuant and Llama 3.2 3B SpinQuant are less than 0.001 metric tonnes each. This is due to the minimal training GPU hours that are required. The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others. ## Training Data Overview: Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO). Data Freshness: The pretraining data has a cutoff of December 2023\. ## Quantization ### Quantization Scheme We designed the current quantization scheme with the [PyTorch’s ExecuTorch](https://github.com/pytorch/executorch) inference framework and Arm CPU backend in mind, taking into account metrics including model quality, prefill/decoding speed, and memory footprint. Our quantization scheme involves three parts: - All linear layers in all transformer blocks are quantized to a 4-bit groupwise scheme (with a group size of 32) for weights and 8-bit per-token dynamic quantization for activations. - The classification layer is quantized to 8-bit per-channel for weight and 8-bit per token dynamic quantization for activation. - Similar to classification layer, an 8-bit per channel quantization is used for embedding layer. ### Quantization-Aware Training and LoRA The quantization-aware training (QAT) with low-rank adaptation (LoRA) models went through only post-training stages, using the same data as the full precision models. To initialize QAT, we utilize BF16 Llama 3.2 model checkpoints obtained after supervised fine-tuning (SFT) and perform an additional full round of SFT training with QAT. We then freeze the backbone of the QAT model and perform another round of SFT with LoRA adaptors applied to all layers within the transformer block. Meanwhile, the LoRA adaptors' weights and activations are maintained in BF16. Because our approach is similar to QLoRA of Dettmers et al., (2023) (i.e., quantization followed by LoRA adapters), we refer this method as QLoRA. Finally, we fine-tune the resulting model (both backbone and LoRA adaptors) using direct preference optimization (DPO). ### SpinQuant [SpinQuant](https://arxiv.org/abs/2405.16406) was applied, together with generative post-training quantization (GPTQ). For the SpinQuant rotation matrix fine-tuning, we optimized for 100 iterations, using 800 samples with sequence-length 2048 from the WikiText 2 dataset. For GPTQ, we used 128 samples from the same dataset with the same sequence-length. ## Benchmarks \- English Text In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library. ### Base Pretrained Models \| Category \| Benchmark \| \# Shots \| Metric \| Llama 3.2 1B \| Llama 3.2 3B \| Llama 3.1 8B \| \| ----- \| ----- \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| MMLU \| 5 \| macro\_avg/acc\_char \| 32.2 \| 58 \| 66.7 \| \| \| AGIEval English \| 3-5 \| average/acc\_char \| 23.3 \| 39.2 \| 47.8 \| \| \| ARC-Challenge \| 25 \| acc\_char \| 32.8 \| 69.1 \| 79.7 \| \| Reading comprehension \| SQuAD \| 1 \| em \| 49.2 \| 67.7 \| 77 \| \| \| QuAC (F1) \| 1 \| f1 \| 37.9 \| 42.9 \| 44.9 \| \| \| DROP (F1) \| 3 \| f1 \| 28.0 \| 45.2 \| 59.5 \| \| Long Context \| Needle in Haystack \| 0 \| em \| 96.8 \| 1 \| 1 \| ### Instruction Tuned Models \| Capability \| \| Benchmark \| \# Shots \| Metric \| Llama 3.2 1B bf16 \| Llama 3.2 1B Vanilla PTQ\\ \| Llama 3.2 1B Spin Quant \| Llama 3.2 1B QLoRA \| Llama 3.2 3B bf16 \| Llama 3.2 3B Vanilla PTQ\\ \| Llama 3.2 3B Spin Quant \| Llama 3.2 3B QLoRA \| Llama 3.1 8B \| \| :---: \| ----- \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| \| MMLU \| 5 \| macro\_avg/acc \| 49.3 \| 43.3 \| 47.3 \| 49.0 \| 63.4 \| 60.5 \| 62 \| 62.4 \| 69.4 \| \| Re-writing \| \| Open-rewrite eval \| 0 \| micro\_avg/rougeL \| 41.6 \| 39.2 \| 40.9 \| 41.2 \| 40.1 \| 40.3 \| 40.8 \| 40.7 \| 40.9 \| \| Summarization \| \| TLDR9+ (test) \| 1 \| rougeL \| 16.8 \| 14.9 \| 16.7 \| 16.8 \| 19.0 \| 19.1 \| 19.2 \| 19.1 \| 17.2 \| \| Instruction following \| \| IFEval \| 0 \| Avg(Prompt/Instruction acc Loose/Strict) \| 59.5 \| 51.5 \| 58.4 \| 55.6 \| 77.4 \| 73.9 \| 73.5 \| 75.9 \| 80.4 \| \| Math \| \| GSM8K (CoT) \| 8 \| em\_maj1@1 \| 44.4 \| 33.1 \| 40.6 \| 46.5 \| 77.7 \| 72.9 \| 75.7 \| 77.9 \| 84.5 \| \| \| \| MATH (CoT) \| 0 \| final\_em \| 30.6 \| 20.5 \| 25.3 \| 31.0 \| 48.0 \| 44.2 \| 45.3 \| 49.2 \| 51.9 \| \| Reasoning \| \| ARC-C \| 0 \| acc \| 59.4 \| 54.3 \| 57 \| 60.7 \| 78.6 \| 75.6 \| 77.6 \| 77.6 \| 83.4 \| \| \| \| GPQA \| 0 \| acc \| 27.2 \| 25.9 \| 26.3 \| 25.9 \| 32.8 \| 32.8 \| 31.7 \| 33.9 \| 32.8 \| \| \| \| Hellaswag \| 0 \| acc \| 41.2 \| 38.1 \| 41.3 \| 41.5 \| 69.8 \| 66.3 \| 68 \| 66.3 \| 78.7 \| \| Tool Use \| \| BFCL V2 \| 0 \| acc \| 25.7 \| 14.3 \| 15.9 \| 23.7 \| 67.0 \| 53.4 \| 60.1 \| 63.5 \| 67.1 \| \| \| \| Nexus \| 0 \| macro\_avg/acc \| 13.5 \| 5.2 \| 9.6 \| 12.5 \| 34.3 \| 32.4 \| 31.5 \| 30.1 \| 38.5 \| \| Long Context \| \| InfiniteBench/En.QA \| 0 \| longbook\_qa/f1 \| 20.3 \| N/A \| N/A \| N/A \| 19.8 \| N/A \| N/A \| N/A \| 27.3 \| \| \| \| InfiniteBench/En.MC \| 0 \| longbook\_choice/acc \| 38.0 \| N/A \| N/A \| N/A \| 63.3 \| N/A \| N/A \| N/A \| 72.2 \| \| \| \| NIH/Multi-needle \| 0 \| recall \| 75.0 \| N/A \| N/A \| N/A \| 84.7 \| N/A \| N/A \| N/A \| 98.8 \| \| Multilingual \| \| MGSM (CoT) \| 0 \| em \| 24.5 \| 13.7 \| 18.2 \| 24.4 \| 58.2 \| 48.9 \| 54.3 \| 56.8 \| 68.9 \| \\for comparison purposes only. Model not released. ### Multilingual Benchmarks \| Category \| Benchmark \| Language \| Llama 3.2 1B \| Llama 3.2 1B Vanilla PTQ\\ \| Llama 3.2 1B Spin Quant \| Llama 3.2 1B QLoRA \| Llama 3.2 3B \| Llama 3.2 3B Vanilla PTQ\\ \| Llama 3.2 3B Spin Quant \| Llama 3.2 3B QLoRA \| Llama 3.1 8B \| \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| MMLU (5-shot, macro_avg/acc) \| Portuguese \| 39.8 \| 34.9 \| 38.9 \| 40.2 \| 54.5 \| 50.9 \| 53.3 \| 53.4 \| 62.1 \| \| \| \| Spanish \| 41.5 \| 36.0 \| 39.8 \| 41.8 \| 55.1 \| 51.9 \| 53.6 \| 53.6 \| 62.5 \| \| \| \| Italian \| 39.8 \| 34.9 \| 38.1 \| 40.6 \| 53.8 \| 49.9 \| 52.1 \| 51.7 \| 61.6 \| \| \| \| German \| 39.2 \| 34.9 \| 37.5 \| 39.6 \| 53.3 \| 50.0 \| 52.2 \| 51.3 \| 60.6 \| \| \| \| French \| 40.5 \| 34.8 \| 39.2 \| 40.8 \| 54.6 \| 51.2 \| 53.3 \| 53.3 \| 62.3 \| \| \| \| Hindi \| 33.5 \| 30.0 \| 32.1 \| 34.0 \| 43.3 \| 40.4 \| 42.0 \| 42.1 \| 50.9 \| \| \| \| Thai \| 34.7 \| 31.2 \| 32.4 \| 34.9 \| 44.5 \| 41.3 \| 44.0 \| 42.2 \| 50.3 \| \\for comparison purposes only. Model not released. ## Inference time In the below table, we compare the performance metrics of different quantization methods (SpinQuant and QAT \+ LoRA) with the BF16 baseline. The evaluation was done using the [ExecuTorch](https://github.com/pytorch/executorch) framework as the inference engine, with the ARM CPU as a backend using Android OnePlus 12 device. \| Category \| Decode (tokens/sec) \| Time-to-first-token (sec) \| Prefill (tokens/sec) \| Model size (PTE file size in MB) \| Memory size (RSS in MB) \| \| :---- \| ----- \| ----- \| ----- \| ----- \| ----- \| \| 1B BF16 (baseline) \| 19.2 \| 1.0 \| 60.3 \| 2358 \| 3,185 \| \| 1B SpinQuant \| 50.2 (2.6x) \| 0.3 (-76.9%) \| 260.5 (4.3x) \| 1083 (-54.1%) \| 1,921 (-39.7%) \| \| 1B QLoRA \| 45.8 (2.4x) \| 0.3 (-76.0%) \| 252.0 (4.2x) \| 1127 (-52.2%) \| 2,255 (-29.2%) \| \| 3B BF16 (baseline) \| 7.6 \| 3.0 \| 21.2 \| 6129 \| 7,419 \| \| 3B SpinQuant \| 19.7 (2.6x) \| 0.7 (-76.4%) \| 89.7 (4.2x) \| 2435 (-60.3%) \| 3,726 (-49.8%) \| \| 3B QLoRA \| 18.5 (2.4x) \| 0.7 (-76.1%) \| 88.8 (4.2x) \| 2529 (-58.7%) \| 4,060 (-45.3%) \| (\) The performance measurement is done using an adb binary-based approach. (\\) It is measured on an Android OnePlus 12 device. (\\\) Time-to-first-token (TTFT) is measured with prompt length=64 Footnote: - Decode (tokens/second) is for how quickly it keeps generating. Higher is better. - Time-to-first-token (TTFT for shorthand) is for how fast it generates the first token for a given prompt. Lower is better. - Prefill is the inverse of TTFT (aka 1/TTFT) in tokens/second. Higher is better - Model size \- how big is the model, measured by, PTE file, a binary file format for ExecuTorch - RSS size \- Memory usage in resident set size (RSS) ## Responsibility & Safety As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks: 1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama 2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm 3. Provide protections for the community to help prevent the misuse of our models ### Responsible Deployment Approach: Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/). #### Llama 3.2 Instruct Objective: Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/). Fine-Tuning Data: We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. Refusals and Tone: Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. #### Llama 3.2 Systems Safety as a System: Large language models, including Llama 3.2, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. ### New Capabilities and Use Cases Technological Advancement: Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well. Constrained Environments: Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version. ### Evaluations Scaled Evaluations: We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Red Teaming: We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. ### Critical Risks In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas: 1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons): Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models. 2\. Child Safety: Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. 3\. Cyber Attacks: For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed. Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models. ### Community Industry Partnerships: Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Grants: We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). Reporting: Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations Values: The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. Testing: Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
RRashmini/google-unimax-t5-small-20	RRashmini	2025-04-27T14:33:56Z	0	0	transformers	[ "transformers", "safetensors", "umt5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-04-27T14:32:46Z	--- 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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ramcargpt/BatQwen2.5-Small-19K	ramcargpt	2025-04-27T13:33:41Z	0	0	transformers	[ "transformers", "gguf", "qwen2", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Qwen2.5-3B-unsloth-bnb-4bit", "base_model:quantized:unsloth/Qwen2.5-3B-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-27T13:33:04Z	--- base_model: unsloth/Qwen2.5-3B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: ramcargpt - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-3B-unsloth-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)
atsuki-yamaguchi/Llama-3.1-8B-Instruct-ta-madlad-mean-trans0305-emb-special	atsuki-yamaguchi	2025-04-27T09:35:58Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "ta", "dataset:allenai/MADLAD-400", "arxiv:2412.11704", "base_model:atsuki-yamaguchi/Llama-3.1-8B-Instruct-ta-madlad-mean-tuned", "base_model:finetune:atsuki-yamaguchi/Llama-3.1-8B-Instruct-ta-madlad-mean-tuned", "license:llama3.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-29T23:50:02Z	--- license: llama3.1 datasets: - allenai/MADLAD-400 language: - ta base_model: - meta-llama/Llama-3.1-8B-Instruct - atsuki-yamaguchi/Llama-3.1-8B-Instruct-ta-madlad-mean-tuned library_name: transformers --- # Llama 3.1 8B Instruct for Tamil: ElChat (Linear merging) This model is built on top of Llama 3.1 8B Instruct adapted for Tamil using 500M target language tokens sampled from MADLAD-400. ## Model Details * Vocabulary: This model has an additional target vocabulary of 10K. * Target vocabulary initialization: The target weights of the embedding and LM head were initialized using mean initialization. * Training: This model was continually pre-trained on 500M target language tokens sampled from MADLAD-400. * Post-processing: The model was post-processed using the ElChat method with linear merging instead of SLERP. ## Model Description - Language: Tamil - License: Llama 3.1 Community License Agreement - Fine-tuned from model: meta-llama/Llama-3.1-8B-Instruct ## Model Sources - Repository: https://github.com/gucci-j/chat-cve - Paper: https://arxiv.org/abs/2412.11704 ## How to Get Started with the Model Use the code below to get started with the model. ```python from transformers import AutoTokenizer, AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained( "atsuki-yamaguchi/Llama-3.1-8B-Instruct-ta-madlad-mean-trans0305-emb-special" ) tokenizer = AutoTokenizer.from_pretrained( "atsuki-yamaguchi/Llama-3.1-8B-Instruct-ta-madlad-mean-trans0305-emb-special" ) ``` ## Citation ``` @misc{yamaguchi2024vocabularyexpansionchatmodels, title={{ElChat}: Adapting Chat Language Models Using Only Target Unlabeled Language Data}, author={Atsuki Yamaguchi and Terufumi Morishita and Aline Villavicencio and Nikolaos Aletras}, year={2024}, eprint={2412.11704}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2412.11704}, } ```
PAUL1122/mistral_korean2	PAUL1122	2025-04-27T08:21:23Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-27T08:17:42Z	--- 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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palpit/LLaVA-v1.5-13b-2000-llava-med-lora	palpit	2025-04-27T01:57:39Z	0	0	null	[ "safetensors", "llava_llama", "region:us" ]	null	2024-11-25T06:14:38Z	# Model Card for Model ID This is the llava-v1.5-7b model variant trained from the self-modified llava-med dataset.
tayyubGX/sign-language-model2	tayyubGX	2025-04-27T01:54:15Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-04-27T01:45:26Z	--- 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. 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mlfoundations-dev/c1_code_nod_4s_1k	mlfoundations-dev	2025-04-27T01:00:30Z	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	2025-04-26T23:56:10Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: c1_code_nod_4s_1k 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. --> # c1_code_nod_4s_1k 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/c1_code_nod_4s_1k 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: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - gradient_accumulation_steps: 6 - total_train_batch_size: 96 - total_eval_batch_size: 128 - 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 2.5.1 - Datasets 3.0.2 - Tokenizers 0.20.3
genki10/BERT_V8_sp10_lw40_ex100_lo100_k5_k5_fold4	genki10	2025-04-27T00:51:23Z	0	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-27T00:33:31Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: BERT_V8_sp10_lw40_ex100_lo100_k5_k5_fold4 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. --> # BERT_V8_sp10_lw40_ex100_lo100_k5_k5_fold4 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8568 - Qwk: 0.4126 - Mse: 0.8568 - Rmse: 0.9257 ## 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: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - 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: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\|:------:\| \| No log \| 1.0 \| 4 \| 10.0080 \| 0.0066 \| 10.0080 \| 3.1635 \| \| No log \| 2.0 \| 8 \| 5.8547 \| 0.0505 \| 5.8547 \| 2.4196 \| \| No log \| 3.0 \| 12 \| 3.1352 \| 0.0118 \| 3.1352 \| 1.7707 \| \| No log \| 4.0 \| 16 \| 1.7320 \| 0.0394 \| 1.7320 \| 1.3161 \| \| No log \| 5.0 \| 20 \| 1.2269 \| 0.0342 \| 1.2269 \| 1.1076 \| \| No log \| 6.0 \| 24 \| 0.8727 \| 0.1948 \| 0.8727 \| 0.9342 \| \| No log \| 7.0 \| 28 \| 1.0243 \| 0.0495 \| 1.0243 \| 1.0121 \| \| No log \| 8.0 \| 32 \| 0.7590 \| 0.4097 \| 0.7590 \| 0.8712 \| \| No log \| 9.0 \| 36 \| 0.6756 \| 0.5025 \| 0.6756 \| 0.8220 \| \| No log \| 10.0 \| 40 \| 1.0251 \| 0.3609 \| 1.0251 \| 1.0124 \| \| No log \| 11.0 \| 44 \| 0.9219 \| 0.4012 \| 0.9219 \| 0.9601 \| \| No log \| 12.0 \| 48 \| 0.6766 \| 0.4951 \| 0.6766 \| 0.8225 \| \| No log \| 13.0 \| 52 \| 1.1625 \| 0.3650 \| 1.1625 \| 1.0782 \| \| No log \| 14.0 \| 56 \| 0.8185 \| 0.4478 \| 0.8185 \| 0.9047 \| \| No log \| 15.0 \| 60 \| 1.1450 \| 0.3743 \| 1.1450 \| 1.0701 \| \| No log \| 16.0 \| 64 \| 0.7286 \| 0.4854 \| 0.7286 \| 0.8536 \| \| No log \| 17.0 \| 68 \| 0.9480 \| 0.3957 \| 0.9480 \| 0.9736 \| \| No log \| 18.0 \| 72 \| 0.9902 \| 0.3894 \| 0.9902 \| 0.9951 \| \| No log \| 19.0 \| 76 \| 0.7143 \| 0.5412 \| 0.7143 \| 0.8451 \| \| No log \| 20.0 \| 80 \| 1.3726 \| 0.3112 \| 1.3726 \| 1.1716 \| \| No log \| 21.0 \| 84 \| 0.9579 \| 0.4055 \| 0.9579 \| 0.9787 \| \| No log \| 22.0 \| 88 \| 0.9382 \| 0.3987 \| 0.9382 \| 0.9686 \| \| No log \| 23.0 \| 92 \| 0.9784 \| 0.3937 \| 0.9784 \| 0.9891 \| \| No log \| 24.0 \| 96 \| 1.3741 \| 0.2886 \| 1.3741 \| 1.1722 \| \| No log \| 25.0 \| 100 \| 0.7647 \| 0.4519 \| 0.7647 \| 0.8745 \| \| No log \| 26.0 \| 104 \| 1.6422 \| 0.2269 \| 1.6422 \| 1.2815 \| \| No log \| 27.0 \| 108 \| 0.7155 \| 0.4536 \| 0.7155 \| 0.8459 \| \| No log \| 28.0 \| 112 \| 0.9718 \| 0.3850 \| 0.9718 \| 0.9858 \| \| No log \| 29.0 \| 116 \| 0.8900 \| 0.3954 \| 0.8900 \| 0.9434 \| \| No log \| 30.0 \| 120 \| 1.1999 \| 0.3136 \| 1.1999 \| 1.0954 \| \| No log \| 31.0 \| 124 \| 0.8515 \| 0.4071 \| 0.8515 \| 0.9228 \| \| No log \| 32.0 \| 128 \| 1.1243 \| 0.3429 \| 1.1243 \| 1.0603 \| \| No log \| 33.0 \| 132 \| 1.0474 \| 0.3745 \| 1.0474 \| 1.0234 \| \| No log \| 34.0 \| 136 \| 0.9949 \| 0.3880 \| 0.9949 \| 0.9974 \| \| No log \| 35.0 \| 140 \| 1.3042 \| 0.2998 \| 1.3042 \| 1.1420 \| \| No log \| 36.0 \| 144 \| 0.7902 \| 0.3926 \| 0.7902 \| 0.8889 \| \| No log \| 37.0 \| 148 \| 1.0854 \| 0.3277 \| 1.0854 \| 1.0418 \| \| No log \| 38.0 \| 152 \| 0.8275 \| 0.4027 \| 0.8275 \| 0.9097 \| \| No log \| 39.0 \| 156 \| 1.1221 \| 0.3287 \| 1.1221 \| 1.0593 \| \| No log \| 40.0 \| 160 \| 0.8769 \| 0.3993 \| 0.8769 \| 0.9364 \| \| No log \| 41.0 \| 164 \| 1.1536 \| 0.3024 \| 1.1536 \| 1.0741 \| \| No log \| 42.0 \| 168 \| 0.9203 \| 0.3626 \| 0.9203 \| 0.9593 \| \| No log \| 43.0 \| 172 \| 1.1411 \| 0.3009 \| 1.1411 \| 1.0682 \| \| No log \| 44.0 \| 176 \| 0.8892 \| 0.4035 \| 0.8892 \| 0.9430 \| \| No log \| 45.0 \| 180 \| 1.2542 \| 0.3188 \| 1.2542 \| 1.1199 \| \| No log \| 46.0 \| 184 \| 0.8217 \| 0.4340 \| 0.8217 \| 0.9065 \| \| No log \| 47.0 \| 188 \| 1.2440 \| 0.3004 \| 1.2440 \| 1.1153 \| \| No log \| 48.0 \| 192 \| 0.7930 \| 0.4453 \| 0.7930 \| 0.8905 \| \| No log \| 49.0 \| 196 \| 0.8568 \| 0.4126 \| 0.8568 \| 0.9257 \| ### Framework versions - Transformers 4.51.1 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.21.0
kivisuals/Prince	kivisuals	2025-04-27T00:33:59Z	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	2025-04-27T00:04:08Z	--- 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: Prince --- # Prince <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 `Prince` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Prince", "lora_weights": "https://huggingface.co/kivisuals/Prince/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('kivisuals/Prince', weight_name='lora.safetensors') image = pipeline('Prince').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/kivisuals/Prince/discussions) to add images that show off what you’ve made with this LoRA.
mlfoundations-dev/b2_code_askllm_0.3k	mlfoundations-dev	2025-04-26T22:32:47Z	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	2025-04-26T20:55:17Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: b2_code_askllm_0.3k 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. --> # b2_code_askllm_0.3k 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/b2_code_askllm_0.3k 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: 1e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - total_eval_batch_size: 32 - 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: 0.1 - num_epochs: 13.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
team-9/gpt2-finetune-stackechange-baseline-10k	team-9	2025-04-26T20:56:23Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-26T09:44:36Z	--- library_name: transformers license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: gpt2-finetune-stackechange-baseline-10k 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. --> # gpt2-finetune-stackechange-baseline-10k This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9621 ## 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: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 64 - total_eval_batch_size: 64 - optimizer: Use 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 \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.2273 \| 1.0 \| 155 \| 3.0242 \| \| 3.0638 \| 2.0 \| 310 \| 2.9772 \| \| 3.0442 \| 3.0 \| 465 \| 2.9621 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
SynthoCraft/Whisper-large-v3	SynthoCraft	2025-04-26T17:47:43Z	0	0	null	[ "safetensors", "whisper", "audio", "automatic-speech-recognition", "hf-asr-leaderboard", "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "arxiv:2212.04356", "license:apache-2.0", "region:us" ]	automatic-speech-recognition	2025-04-26T17:03:58Z	--- language: - en - zh - de - es - ru - ko - fr - ja - pt - tr - pl - ca - nl - ar - sv - it - id - hi - fi - vi - he - uk - el - ms - cs - ro - da - hu - ta - no - th - ur - hr - bg - lt - la - mi - ml - cy - sk - te - fa - lv - bn - sr - az - sl - kn - et - mk - br - eu - is - hy - ne - mn - bs - kk - sq - sw - gl - mr - pa - si - km - sn - yo - so - af - oc - ka - be - tg - sd - gu - am - yi - lo - uz - fo - ht - ps - tk - nn - mt - sa - lb - my - bo - tl - mg - as - tt - haw - ln - ha - ba - jw - su tags: - audio - automatic-speech-recognition - hf-asr-leaderboard widget: - example_title: Librispeech sample 1 src: https://cdn-media.huggingface.co/speech_samples/sample1.flac - example_title: Librispeech sample 2 src: https://cdn-media.huggingface.co/speech_samples/sample2.flac pipeline_tag: automatic-speech-recognition license: apache-2.0 --- # Whisper Whisper is a state-of-the-art model for automatic speech recognition (ASR) and speech translation, proposed in the paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://huggingface.co/papers/2212.04356) by Alec Radford et al. from OpenAI. Trained on >5M hours of labeled data, Whisper demonstrates a strong ability to generalise to many datasets and domains in a zero-shot setting. Whisper large-v3 has the same architecture as the previous [large](https://huggingface.co/openai/whisper-large) and [large-v2](https://huggingface.co/openai/whisper-large-v2) models, except for the following minor differences: 1. The spectrogram input uses 128 Mel frequency bins instead of 80 2. A new language token for Cantonese The Whisper large-v3 model was trained on 1 million hours of weakly labeled audio and 4 million hours of pseudo-labeled audio collected using Whisper [large-v2](https://huggingface.co/openai/whisper-large-v2) . The model was trained for 2.0 epochs over this mixture dataset. The large-v3 model shows improved performance over a wide variety of languages, showing 10% to 20% reduction of errors compared to Whisper [large-v2](https://huggingface.co/openai/whisper-large-v2) . For more details on the different checkpoints available, refer to the section [Model details](#model-details). Disclaimer: Content for this model card has partly been written by the 🤗 Hugging Face team, and partly copied and pasted from the original model card. ## Usage Whisper large-v3 is supported in Hugging Face 🤗 Transformers. To run the model, first install the Transformers library. For this example, we'll also install 🤗 Datasets to load toy audio dataset from the Hugging Face Hub, and 🤗 Accelerate to reduce the model loading time: ```bash pip install --upgrade pip pip install --upgrade transformers datasets[audio] accelerate ``` The model can be used with the [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline) class to transcribe audios of arbitrary length: ```python import torch from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline from datasets import load_dataset device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "openai/whisper-large-v3" model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True ) model.to(device) processor = AutoProcessor.from_pretrained(model_id) pipe = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, torch_dtype=torch_dtype, device=device, ) dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation") sample = dataset[0]["audio"] result = pipe(sample) print(result["text"]) ``` To transcribe a local audio file, simply pass the path to your audio file when you call the pipeline: ```python result = pipe("audio.mp3") ``` Multiple audio files can be transcribed in parallel by specifying them as a list and setting the `batch_size` parameter: ```python result = pipe(["audio_1.mp3", "audio_2.mp3"], batch_size=2) ``` Transformers is compatible with all Whisper decoding strategies, such as temperature fallback and condition on previous tokens. The following example demonstrates how to enable these heuristics: ```python generate_kwargs = { "max_new_tokens": 448, "num_beams": 1, "condition_on_prev_tokens": False, "compression_ratio_threshold": 1.35, # zlib compression ratio threshold (in token space) "temperature": (0.0, 0.2, 0.4, 0.6, 0.8, 1.0), "logprob_threshold": -1.0, "no_speech_threshold": 0.6, "return_timestamps": True, } result = pipe(sample, generate_kwargs=generate_kwargs) ``` Whisper predicts the language of the source audio automatically. If the source audio language is known a-priori, it can be passed as an argument to the pipeline: ```python result = pipe(sample, generate_kwargs={"language": "english"}) ``` By default, Whisper performs the task of speech transcription, where the source audio language is the same as the target text language. To perform speech translation, where the target text is in English, set the task to `"translate"`: ```python result = pipe(sample, generate_kwargs={"task": "translate"}) ``` Finally, the model can be made to predict timestamps. For sentence-level timestamps, pass the `return_timestamps` argument: ```python result = pipe(sample, return_timestamps=True) print(result["chunks"]) ``` And for word-level timestamps: ```python result = pipe(sample, return_timestamps="word") print(result["chunks"]) ``` The above arguments can be used in isolation or in combination. For example, to perform the task of speech transcription where the source audio is in French, and we want to return sentence-level timestamps, the following can be used: ```python result = pipe(sample, return_timestamps=True, generate_kwargs={"language": "french", "task": "translate"}) print(result["chunks"]) ``` <details> <summary> For more control over the generation parameters, use the model + processor API directly: </summary> ```python import torch from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor from datasets import Audio, load_dataset device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "openai/whisper-large-v3" model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True ) model.to(device) processor = AutoProcessor.from_pretrained(model_id) dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") dataset = dataset.cast_column("audio", Audio(processor.feature_extractor.sampling_rate)) sample = dataset[0]["audio"] inputs = processor( sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt", truncation=False, padding="longest", return_attention_mask=True, ) inputs = inputs.to(device, dtype=torch_dtype) gen_kwargs = { "max_new_tokens": 448, "num_beams": 1, "condition_on_prev_tokens": False, "compression_ratio_threshold": 1.35, # zlib compression ratio threshold (in token space) "temperature": (0.0, 0.2, 0.4, 0.6, 0.8, 1.0), "logprob_threshold": -1.0, "no_speech_threshold": 0.6, "return_timestamps": True, } pred_ids = model.generate(inputs, gen_kwargs) pred_text = processor.batch_decode(pred_ids, skip_special_tokens=True, decode_with_timestamps=False) print(pred_text) ``` </details> ## Additional Speed & Memory Improvements You can apply additional speed and memory improvements to Whisper to further reduce the inference speed and VRAM requirements. ### Chunked Long-Form Whisper has a receptive field of 30-seconds. To transcribe audios longer than this, one of two long-form algorithms are required: 1. Sequential: uses a "sliding window" for buffered inference, transcribing 30-second slices one after the other 2. Chunked: splits long audio files into shorter ones (with a small overlap between segments), transcribes each segment independently, and stitches the resulting transcriptions at the boundaries The sequential long-form algorithm should be used in either of the following scenarios: 1. Transcription accuracy is the most important factor, and speed is less of a consideration 2. You are transcribing batches of long audio files, in which case the latency of sequential is comparable to chunked, while being up to 0.5% WER more accurate Conversely, the chunked algorithm should be used when: 1. Transcription speed is the most important factor 2. You are transcribing a single long audio file By default, Transformers uses the sequential algorithm. To enable the chunked algorithm, pass the `chunk_length_s` parameter to the `pipeline`. For large-v3, a chunk length of 30-seconds is optimal. To activate batching over long audio files, pass the argument `batch_size`: ```python import torch from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline from datasets import load_dataset device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "openai/whisper-large-v3" model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True ) model.to(device) processor = AutoProcessor.from_pretrained(model_id) pipe = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, chunk_length_s=30, batch_size=16, # batch size for inference - set based on your device torch_dtype=torch_dtype, device=device, ) dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation") sample = dataset[0]["audio"] result = pipe(sample) print(result["text"]) ``` #### Torch compile The Whisper forward pass is compatible with [`torch.compile`](https://pytorch.org/docs/stable/generated/torch.compile.html) for 4.5x speed-ups. Note: `torch.compile` is currently not compatible with the Chunked long-form algorithm or Flash Attention 2 ⚠️ ```python import torch from torch.nn.attention import SDPBackend, sdpa_kernel from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline from datasets import load_dataset from tqdm import tqdm torch.set_float32_matmul_precision("high") device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "openai/whisper-large-v3" model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True ).to(device) # Enable static cache and compile the forward pass model.generation_config.cache_implementation = "static" model.generation_config.max_new_tokens = 256 model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True) processor = AutoProcessor.from_pretrained(model_id) pipe = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, torch_dtype=torch_dtype, device=device, ) dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation") sample = dataset[0]["audio"] # 2 warmup steps for _ in tqdm(range(2), desc="Warm-up step"): with sdpa_kernel(SDPBackend.MATH): result = pipe(sample.copy(), generate_kwargs={"min_new_tokens": 256, "max_new_tokens": 256}) # fast run with sdpa_kernel(SDPBackend.MATH): result = pipe(sample.copy()) print(result["text"]) ``` #### Flash Attention 2 We recommend using [Flash-Attention 2](https://huggingface.co/docs/transformers/main/en/perf_infer_gpu_one#flashattention-2) if your GPU supports it and you are not using [torch.compile](#torch-compile). To do so, first install [Flash Attention](https://github.com/Dao-AILab/flash-attention): ``` pip install flash-attn --no-build-isolation ``` Then pass `attn_implementation="flash_attention_2"` to `from_pretrained`: ```python model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, attn_implementation="flash_attention_2") ``` #### Torch Scale-Product-Attention (SDPA) If your GPU does not support Flash Attention, we recommend making use of PyTorch [scaled dot-product attention (SDPA)](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html). This attention implementation is activated by default for PyTorch versions 2.1.1 or greater. To check whether you have a compatible PyTorch version, run the following Python code snippet: ```python from transformers.utils import is_torch_sdpa_available print(is_torch_sdpa_available()) ``` If the above returns `True`, you have a valid version of PyTorch installed and SDPA is activated by default. If it returns `False`, you need to upgrade your PyTorch version according to the [official instructions](https://pytorch.org/get-started/locally/) Once a valid PyTorch version is installed, SDPA is activated by default. It can also be set explicitly by specifying `attn_implementation="sdpa"` as follows: ```python model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, attn_implementation="sdpa") ``` For more information about how to use the SDPA refer to the [Transformers SDPA documentation](https://huggingface.co/docs/transformers/en/perf_infer_gpu_one#pytorch-scaled-dot-product-attention). ## Model details Whisper is a Transformer based encoder-decoder model, also referred to as a _sequence-to-sequence_ model. There are two flavours of Whisper model: English-only and multilingual. The English-only models were trained on the task of English speech recognition. The multilingual models were trained simultaneously on multilingual speech recognition and speech translation. For speech recognition, the model predicts transcriptions in the same language as the audio. For speech translation, the model predicts transcriptions to a different language to the audio. Whisper checkpoints come in five configurations of varying model sizes. The smallest four are available as English-only and multilingual. The largest checkpoints are multilingual only. All ten of the pre-trained checkpoints are available on the [Hugging Face Hub](https://huggingface.co/models?search=openai/whisper). The checkpoints are summarised in the following table with links to the models on the Hub: \| Size \| Parameters \| English-only \| Multilingual \| \|----------\|------------\|------------------------------------------------------\|-----------------------------------------------------\| \| tiny \| 39 M \| [✓](https://huggingface.co/openai/whisper-tiny.en) \| [✓](https://huggingface.co/openai/whisper-tiny) \| \| base \| 74 M \| [✓](https://huggingface.co/openai/whisper-base.en) \| [✓](https://huggingface.co/openai/whisper-base) \| \| small \| 244 M \| [✓](https://huggingface.co/openai/whisper-small.en) \| [✓](https://huggingface.co/openai/whisper-small) \| \| medium \| 769 M \| [✓](https://huggingface.co/openai/whisper-medium.en) \| [✓](https://huggingface.co/openai/whisper-medium) \| \| large \| 1550 M \| x \| [✓](https://huggingface.co/openai/whisper-large) \| \| large-v2 \| 1550 M \| x \| [✓](https://huggingface.co/openai/whisper-large-v2) \| \| large-v3 \| 1550 M \| x \| [✓](https://huggingface.co/openai/whisper-large-v3) \| ## Fine-Tuning The pre-trained Whisper model demonstrates a strong ability to generalise to different datasets and domains. However, its predictive capabilities can be improved further for certain languages and tasks through fine-tuning. The blog post [Fine-Tune Whisper with 🤗 Transformers](https://huggingface.co/blog/fine-tune-whisper) provides a step-by-step guide to fine-tuning the Whisper model with as little as 5 hours of labelled data. ### Evaluated Use The primary intended users of these models are AI researchers studying robustness, generalization, capabilities, biases, and constraints of the current model. However, Whisper is also potentially quite useful as an ASR solution for developers, especially for English speech recognition. We recognize that once models are released, it is impossible to restrict access to only “intended” uses or to draw reasonable guidelines around what is or is not research. The models are primarily trained and evaluated on ASR and speech translation to English tasks. They show strong ASR results in ~10 languages. They may exhibit additional capabilities, particularly if fine-tuned on certain tasks like voice activity detection, speaker classification, or speaker diarization but have not been robustly evaluated in these areas. We strongly recommend that users perform robust evaluations of the models in a particular context and domain before deploying them. In particular, we caution against using Whisper models to transcribe recordings of individuals taken without their consent or purporting to use these models for any kind of subjective classification. We recommend against use in high-risk domains like decision-making contexts, where flaws in accuracy can lead to pronounced flaws in outcomes. The models are intended to transcribe and translate speech, use of the model for classification is not only not evaluated but also not appropriate, particularly to infer human attributes. ## Training Data The large-v3 checkpoint is trained on 1 million hours of weakly labeled audio and 4 million hours of pseudo-labeled audio collected using Whisper large-v2. As discussed in [the accompanying paper](https://cdn.openai.com/papers/whisper.pdf), we see that performance on transcription in a given language is directly correlated with the amount of training data we employ in that language. ## Performance and Limitations Our studies show that, over many existing ASR systems, the models exhibit improved robustness to accents, background noise, technical language, as well as zero shot translation from multiple languages into English; and that accuracy on speech recognition and translation is near the state-of-the-art level. However, because the models are trained in a weakly supervised manner using large-scale noisy data, the predictions may include texts that are not actually spoken in the audio input (i.e. hallucination). We hypothesize that this happens because, given their general knowledge of language, the models combine trying to predict the next word in audio with trying to transcribe the audio itself. Our models perform unevenly across languages, and we observe lower accuracy on low-resource and/or low-discoverability languages or languages where we have less training data. The models also exhibit disparate performance on different accents and dialects of particular languages, which may include higher word error rate across speakers of different genders, races, ages, or other demographic criteria. Our full evaluation results are presented in [the paper accompanying this release](https://cdn.openai.com/papers/whisper.pdf). In addition, the sequence-to-sequence architecture of the model makes it prone to generating repetitive texts, which can be mitigated to some degree by beam search and temperature scheduling but not perfectly. Further analysis on these limitations are provided in [the paper](https://cdn.openai.com/papers/whisper.pdf). It is likely that this behavior and hallucinations may be worse on lower-resource and/or lower-discoverability languages. ## Broader Implications We anticipate that Whisper models’ transcription capabilities may be used for improving accessibility tools. While Whisper models cannot be used for real-time transcription out of the box – their speed and size suggest that others may be able to build applications on top of them that allow for near-real-time speech recognition and translation. The real value of beneficial applications built on top of Whisper models suggests that the disparate performance of these models may have real economic implications. There are also potential dual use concerns that come with releasing Whisper. While we hope the technology will be used primarily for beneficial purposes, making ASR technology more accessible could enable more actors to build capable surveillance technologies or scale up existing surveillance efforts, as the speed and accuracy allow for affordable automatic transcription and translation of large volumes of audio communication. Moreover, these models may have some capabilities to recognize specific individuals out of the box, which in turn presents safety concerns related both to dual use and disparate performance. In practice, we expect that the cost of transcription is not the limiting factor of scaling up surveillance projects. ### BibTeX entry and citation info ```bibtex @misc{radford2022whisper, doi = {10.48550/ARXIV.2212.04356}, url = {https://arxiv.org/abs/2212.04356}, author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya}, title = {Robust Speech Recognition via Large-Scale Weak Supervision}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} } ```
NightRaven109/MATFUSE-sd-rdy2gominusdatset	NightRaven109	2025-04-26T17:47:10Z	0	0	null	[ "arxiv:2308.11408", "region:us" ]	null	2025-04-26T17:46:08Z	<div align="center"> # MatFuse: Controllable Material Generation with Diffusion Models [Giuseppe Vecchio](https://github.com/giuvecchio), [Renato Sortino](https://rensortino.github.io), [Simone Palazzo](https://github.com/simopal6) and [Concetto Spampinato](https://github.com/cspampin) [![arXiv](https://img.shields.io/badge/arXiv-_-darkgreen?style=flat-square&logo=arxiv)](https://arxiv.org/abs/2308.11408) [![Project page](https://img.shields.io/badge/🚀_Project_Page-_-darkgreen?style=flat-square)](https://gvecchio.com/matfuse/) [![cvf](https://img.shields.io/badge/CVPR_2024-_-darkgreen?style=flat-square)](https://cvpr.thecvf.com/Conferences/2024/AcceptedPapers#:~:text=MatFuse:%20Controllable%20Material%20Generation%20with%20Diffusion%20Models) [![huggingface](https://img.shields.io/badge/🤗_Weights-_-darkgreen?style=flat-square)](https://huggingface.co/gvecchio/MatFuse) </div> <img title="teaser image" alt="Teaser" src="./imgs/teaser.png"/> # 🔍 Overview The official PyTorch implementation for paper __"MatFuse: Controllable Material Generation with Diffusion Models"__. MatFuse is a novel approach that simplifies the creation of SVBRDF (Spatially-Varying Bidirectional Reflectance Distribution Function) maps. It leverages the generative power of diffusion models (DM) to streamline the material synthesis process. By integrating multiple sources of conditioning, including color palettes, sketches, text, and pictures, it provides fine-grained control and flexibility in material generation. Additionally, MatFuse enabled editing or refining the synthesized materials after their initial generation. It supports a map-level editing by masking specific areas of specific maps or the entire material. <div align="center"> <img src="./imgs/editing.png" style="max-width: 70%; border-radius: 10px"/> </div> #### See more exaples from MatFuse at the [project page](https://gvecchio.com/matfuse). ### 📄 Paper abstract Creating high-quality materials in computer graphics is a challenging and time-consuming task, which requires great expertise. To simply this process, we introduce MatFuse, a unified approach that harnesses the generative power of diffusion models to simplify the creation of SVBRDF maps. Our pipeline integrates multiple sources of conditioning, including color palettes, sketches, text, and pictures, for a fine-grained control and flexibility in material synthesis. This design enables the combination of diverse information sources (e.g., sketch + text), enhancing creative possibilities in line with the principle of compositionality. Additionally, we propose a multi-encoder compression model with a two-fold purpose: it improves reconstruction performance by learning a separate latent representation for each map and enables a map-level material editing capabilities. We demonstrate the effectiveness of MatFuse under multiple conditioning settings and explore the potential of material editing. We also quantitatively assess the quality of the generated materials in terms of CLIP-IQA and FID scores. <div align="center"> <img src="./imgs/diffusion.png" style="max-width: 70%"/> </div> #### Read the full MatFuse paper on [arXiv](https://arxiv.org/abs/2308.11408). ### 📂 Repository details This repo relies on the original latent diffusion implementation (https://github.com/CompVis/stable-diffusion) which has been modified to include the features described in the MatFuse paper. If you're familiar with the original stable diffusion codebase you should have no problems running this one. The most relevant changes are: - a new multi-encoder vq-vae architecture which processes each material map (diffuse, normal, roughness and specular) independently, learning a disentangled latent representation. - a new a _VQMaterialLoss_ which combines the original _VQLPIPSWithDiscriminator_ with a rendering loss. - multi-condition fusion mechanism. MatFuse is trained on a combination of the dataset by Deschaintre et al. (2018) and materials from the [PolyHeaven](https://polyhaven.com/) library. We do not plan to release such dataset as it can be easily collected. Anyway, if you plan to train your own MatFuse, we strongly recomend using the recently released dataset [MatSynth](https://gvecchio.com/matsynth) which contains a wider variety of high-resolution materials and annotations. # 💿 Installation #### 1. Clone the repo ```shell git clone https://github.com/giuvecchio/matfuse-sd.git cd matfuse-sd ``` #### 2. Setting up the virtualenv This is assuming you have navigated to the `matfuse-sd` root after cloning it. NOTE: This is tested under `python3.10`. For other python versions, you might encounter version conflicts. PyTorch 1.13.1 ```shell # create environment (can use venv instead of conda) conda create -n matfuse python==3.10.13 conda activate matfuse # install required packages pip install -r requirements.txt ``` # 💪 Training Training of MatFuse requires two steps: 1. Training of the autoencoder (VQ-VAE) 2. Training of the diffusion model (LDM) Both is accessed through the `main.py` script in the `src` folder and relies on the use of config files to setup the models, datasets and losses. \ Config files are located under `src/configs/`, and are split in `autoencoder` and `diffusion` subfolders. \ Use the right config file depending on the part of the model you want to train. The general command to launch a training is: ```shell python src/main.py --base src/configs/<model>/<config.yaml> --train --gpus <indices,> ``` ## Data preparation We provide a dataset class for the training of MatFuse. This dataset expects the data folder to be structured as shown below. ``` ./data/MatFuse/{split}/ ├── bricks_045 │ ├── metadata.json │ ├── diffuse.png │ ├── normal.png │ ├── roughness.png │ ├── specular.png │ ├── sketch.png │ ├── renders │ ├── render_00.png │ ├── render_01.png │ ├── ... ├── ... ``` Data shouldbe split between `train` and `test` sets. Each material folder contains the required SVBRDF maps (diffuse, normal, roughness, specular), the sketch and a `metadata.json` file with the text caption and the color palette. ⚠️ Note: To run a training, update the `data_root` property in the config file to point to the folder where you have your dataset stored. ### Processing the data We provide a script to extract the color palette from the renders under the `src/scripts/data` folder. To run it run: ```shell python src/scripts/data/extract_palette.py --data <path/to/dataset> ``` ## Training the autoencoder Configs for training an autoencoder are provided at `src/configs/autoencoder`. \ MatFuse uses a VQ-regularized model. For more info see the [taming-transformers](https://github.com/CompVis/taming-transformers) repository. Training can be started by running ```shell python src/main.py --base src/configs/autoencoder/multi-vq_f8.yaml --train --gpus 0, ``` ## Training the LDM In `src/configs/diffusion/` we provide configs for training the MatFuse LDMs. \ ⚠️ Before moving on to the next step update the `ckpt_path` under `first_stage_config` in the `matfuse-ldm-vq_f8.yaml` to point to your vq-vae checkpoint. Training can be started by running ```shell python src/main.py --base src/configs/diffusion/matfuse-ldm-vq_f8.yaml --train --gpus 0, ``` ### Resuming a training To resume a training append the arguments `--resume <log/folder>` to the training command. ### Notes: If you're training on Windows remember to set the distributed backend to `gloo`. Others are not supported! ```shell $env:PL_TORCH_DISTRIBUTED_BACKEND='gloo' ``` To limit the number of visible GPUs use: ```shell CUDA_VISIBLE_DEVICES=<GPU_ID> python src/main.py ... ``` The experiments are automatically logged using [Weights and Biases](https://wandb.ai/site). To specify your own project space and project name set the following environment variables: ```shell WANDB_PROJECT='{YOUR_PROJECT_NAME}' WANDB_ENTITY='{YOUR_PROJECT_SPACE_NAME}' ``` # 🧪 Inference To run inference on a trained model, run the `gradio_app.py` script specifying the path to the model checkpoint and the configuration. \ This will open a web interface to perform conditional generation and material editing. ### ⚠️ Notes - For inference, at least 12GB of GPU VRAM are necessary. - Weights are available at [huggingface.co/gvecchio/MatFuse](https://huggingface.co/gvecchio/MatFuse). We provide both ema only weights (pruned) and full weights. The gradio app expects the full weights to be provided. ```shell python src/gradio_app.py --ckpt <path/to/checkpoint.ckpt> --config src/configs/diffusion/<config.yaml> ``` <div align="center"> <img src="./imgs/gradio_generate.png" style="max-width: 49%; border-radius: 10px"/> <img src="./imgs/gradio_edit.png" style="max-width: 49%; border-radius: 10px"/> </div> #### # 📜 Citation ```bibtex @inproceedings{vecchio2024matfuse, author = {Vecchio, Giuseppe and Sortino, Renato and Palazzo, Simone and Spampinato, Concetto}, title = {MatFuse: Controllable Material Generation with Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {4429-4438} } ```
TOMFORD79/Menu_v1_9	TOMFORD79	2025-04-26T17:18:04Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-04-26T16:26:24Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
RichardErkhov/Anteia_-_Qwen2-7B-Instruct-v1.1-gguf	RichardErkhov	2025-04-26T16:22:30Z	0	0	null	[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-26T14:20:57Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Qwen2-7B-Instruct-v1.1 - GGUF - Model creator: https://huggingface.co/Anteia/ - Original model: https://huggingface.co/Anteia/Qwen2-7B-Instruct-v1.1/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Qwen2-7B-Instruct-v1.1.Q2_K.gguf](https://huggingface.co/RichardErkhov/Anteia_-_Qwen2-7B-Instruct-v1.1-gguf/blob/main/Qwen2-7B-Instruct-v1.1.Q2_K.gguf) \| Q2_K \| 2.81GB \| \| [Qwen2-7B-Instruct-v1.1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Anteia_-_Qwen2-7B-Instruct-v1.1-gguf/blob/main/Qwen2-7B-Instruct-v1.1.IQ3_XS.gguf) \| IQ3_XS \| 3.12GB \| \| [Qwen2-7B-Instruct-v1.1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Anteia_-_Qwen2-7B-Instruct-v1.1-gguf/blob/main/Qwen2-7B-Instruct-v1.1.IQ3_S.gguf) \| IQ3_S \| 3.26GB \| \| [Qwen2-7B-Instruct-v1.1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Anteia_-_Qwen2-7B-Instruct-v1.1-gguf/blob/main/Qwen2-7B-Instruct-v1.1.Q3_K_S.gguf) \| Q3_K_S \| 3.25GB \| \| [Qwen2-7B-Instruct-v1.1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Anteia_-_Qwen2-7B-Instruct-v1.1-gguf/blob/main/Qwen2-7B-Instruct-v1.1.IQ3_M.gguf) \| IQ3_M \| 3.33GB \| \| [Qwen2-7B-Instruct-v1.1.Q3_K.gguf](https://huggingface.co/RichardErkhov/Anteia_-_Qwen2-7B-Instruct-v1.1-gguf/blob/main/Qwen2-7B-Instruct-v1.1.Q3_K.gguf) \| Q3_K \| 3.55GB \| \| [Qwen2-7B-Instruct-v1.1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Anteia_-_Qwen2-7B-Instruct-v1.1-gguf/blob/main/Qwen2-7B-Instruct-v1.1.Q3_K_M.gguf) \| Q3_K_M \| 3.55GB \| \| [Qwen2-7B-Instruct-v1.1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Anteia_-_Qwen2-7B-Instruct-v1.1-gguf/blob/main/Qwen2-7B-Instruct-v1.1.Q3_K_L.gguf) \| Q3_K_L \| 3.81GB \| \| [Qwen2-7B-Instruct-v1.1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Anteia_-_Qwen2-7B-Instruct-v1.1-gguf/blob/main/Qwen2-7B-Instruct-v1.1.IQ4_XS.gguf) \| IQ4_XS \| 3.96GB \| \| [Qwen2-7B-Instruct-v1.1.Q4_0.gguf](https://huggingface.co/RichardErkhov/Anteia_-_Qwen2-7B-Instruct-v1.1-gguf/blob/main/Qwen2-7B-Instruct-v1.1.Q4_0.gguf) \| Q4_0 \| 4.13GB \| \| [Qwen2-7B-Instruct-v1.1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Anteia_-_Qwen2-7B-Instruct-v1.1-gguf/blob/main/Qwen2-7B-Instruct-v1.1.IQ4_NL.gguf) \| IQ4_NL \| 4.16GB \| \| [Qwen2-7B-Instruct-v1.1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Anteia_-_Qwen2-7B-Instruct-v1.1-gguf/blob/main/Qwen2-7B-Instruct-v1.1.Q4_K_S.gguf) \| Q4_K_S \| 4.15GB \| \| [Qwen2-7B-Instruct-v1.1.Q4_K.gguf](https://huggingface.co/RichardErkhov/Anteia_-_Qwen2-7B-Instruct-v1.1-gguf/blob/main/Qwen2-7B-Instruct-v1.1.Q4_K.gguf) \| Q4_K \| 4.36GB \| \| [Qwen2-7B-Instruct-v1.1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Anteia_-_Qwen2-7B-Instruct-v1.1-gguf/blob/main/Qwen2-7B-Instruct-v1.1.Q4_K_M.gguf) \| Q4_K_M \| 4.36GB \| \| [Qwen2-7B-Instruct-v1.1.Q4_1.gguf](https://huggingface.co/RichardErkhov/Anteia_-_Qwen2-7B-Instruct-v1.1-gguf/blob/main/Qwen2-7B-Instruct-v1.1.Q4_1.gguf) \| Q4_1 \| 4.54GB \| \| [Qwen2-7B-Instruct-v1.1.Q5_0.gguf](https://huggingface.co/RichardErkhov/Anteia_-_Qwen2-7B-Instruct-v1.1-gguf/blob/main/Qwen2-7B-Instruct-v1.1.Q5_0.gguf) \| Q5_0 \| 4.95GB \| \| [Qwen2-7B-Instruct-v1.1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Anteia_-_Qwen2-7B-Instruct-v1.1-gguf/blob/main/Qwen2-7B-Instruct-v1.1.Q5_K_S.gguf) \| Q5_K_S \| 4.95GB \| \| [Qwen2-7B-Instruct-v1.1.Q5_K.gguf](https://huggingface.co/RichardErkhov/Anteia_-_Qwen2-7B-Instruct-v1.1-gguf/blob/main/Qwen2-7B-Instruct-v1.1.Q5_K.gguf) \| Q5_K \| 5.07GB \| \| [Qwen2-7B-Instruct-v1.1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Anteia_-_Qwen2-7B-Instruct-v1.1-gguf/blob/main/Qwen2-7B-Instruct-v1.1.Q5_K_M.gguf) \| Q5_K_M \| 5.07GB \| \| [Qwen2-7B-Instruct-v1.1.Q5_1.gguf](https://huggingface.co/RichardErkhov/Anteia_-_Qwen2-7B-Instruct-v1.1-gguf/blob/main/Qwen2-7B-Instruct-v1.1.Q5_1.gguf) \| Q5_1 \| 5.36GB \| \| [Qwen2-7B-Instruct-v1.1.Q6_K.gguf](https://huggingface.co/RichardErkhov/Anteia_-_Qwen2-7B-Instruct-v1.1-gguf/blob/main/Qwen2-7B-Instruct-v1.1.Q6_K.gguf) \| Q6_K \| 5.82GB \| \| [Qwen2-7B-Instruct-v1.1.Q8_0.gguf](https://huggingface.co/RichardErkhov/Anteia_-_Qwen2-7B-Instruct-v1.1-gguf/blob/main/Qwen2-7B-Instruct-v1.1.Q8_0.gguf) \| Q8_0 \| 7.54GB \| Original model description: --- library_name: transformers tags: - krx --- # 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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o-Sophie-Rain-Spiderman-Video/Sophie.Rain.Spiderman.Video.Official	o-Sophie-Rain-Spiderman-Video	2025-04-26T16:09:16Z	0	0	null	[ "region:us" ]	null	2025-04-26T16:08:39Z	<p><a href="https://social.danielwellington.com/srain" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a></p> <p><a href="https://social.danielwellington.com/srain" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a></p> <p><a href="https://social.danielwellington.com/srain" rel="nofollow"><img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif"></a></p>
HeavensHackDev/Nova_0.5_Alpha	HeavensHackDev	2025-04-26T08:05:50Z	0	0	adapter-transformers	[ "adapter-transformers", "code", "music", "chemistry", "biology", "legal", "ru", "en", "uk", "kk", "dataset:nvidia/OpenCodeReasoning", "license:openrail", "region:us" ]	null	2025-04-26T08:02:08Z	--- license: openrail datasets: - nvidia/OpenCodeReasoning language: - ru - en - uk - kk metrics: - code_eval - charcut_mt library_name: adapter-transformers tags: - code - music - chemistry - biology - legal ---
imkrish/heart-disease-models	imkrish	2025-04-26T07:38:18Z	0	0	null	[ "region:us" ]	null	2025-04-26T07:02:47Z	# Heart Disease Prediction Models Four sklearn classifiers trained on the Kaggle heart-failure dataset. Pickles included: - LogisticR.pkl - SVM.pkl - DecisionTree.pkl - RandomForest.pkl Load with `pickle.load(open(...))` or use via HF Inference API.
theomani/theomani	theomani	2025-04-26T03:23:28Z	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	2025-04-26T02:54:39Z	--- 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: theomani --- # Theomani <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 `theomani` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "theomani", "lora_weights": "https://huggingface.co/theomani/theomani/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('theomani/theomani', weight_name='lora.safetensors') image = pipeline('theomani').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/theomani/theomani/discussions) to add images that show off what you’ve made with this LoRA.
Kannaseka/yolov8	Kannaseka	2025-04-25T13:15:10Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-25T13:14:59Z	--- license: apache-2.0 ---
Hartunka/distilbert_rand_5_v2_mrpc	Hartunka	2025-04-22T18:23:02Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "base_model:Hartunka/distilbert_rand_5_v2", "base_model:finetune:Hartunka/distilbert_rand_5_v2", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-22T18:21:54Z	--- library_name: transformers language: - en base_model: Hartunka/distilbert_rand_5_v2 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: distilbert_rand_5_v2_mrpc results: - task: name: Text Classification type: text-classification dataset: name: GLUE MRPC type: glue args: mrpc metrics: - name: Accuracy type: accuracy value: 0.696078431372549 - name: F1 type: f1 value: 0.7980456026058632 --- <!-- 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. --> # distilbert_rand_5_v2_mrpc This model is a fine-tuned version of [Hartunka/distilbert_rand_5_v2](https://huggingface.co/Hartunka/distilbert_rand_5_v2) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.5870 - Accuracy: 0.6961 - F1: 0.7980 - Combined Score: 0.7471 ## 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: 256 - eval_batch_size: 256 - seed: 10 - optimizer: Use 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: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Combined Score \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------------:\| \| 0.6303 \| 1.0 \| 15 \| 0.5948 \| 0.6936 \| 0.8031 \| 0.7484 \| \| 0.5774 \| 2.0 \| 30 \| 0.5870 \| 0.6961 \| 0.7980 \| 0.7471 \| \| 0.5136 \| 3.0 \| 45 \| 0.6408 \| 0.6936 \| 0.8019 \| 0.7478 \| \| 0.4314 \| 4.0 \| 60 \| 0.7090 \| 0.6765 \| 0.7651 \| 0.7208 \| \| 0.2865 \| 5.0 \| 75 \| 0.9271 \| 0.6716 \| 0.7674 \| 0.7195 \| \| 0.1755 \| 6.0 \| 90 \| 1.2391 \| 0.6054 \| 0.7046 \| 0.6550 \| \| 0.0968 \| 7.0 \| 105 \| 1.5553 \| 0.6348 \| 0.7296 \| 0.6822 \| ### Framework versions - Transformers 4.50.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.21.1
Hosseinka/qwen2-vl-run_vera_4-29	Hosseinka	2025-04-21T01:37:31Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2-VL-7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-04-21T00:50:53Z	--- base_model: Qwen/Qwen2-VL-7B-Instruct library_name: transformers model_name: qwen2-vl-run_vera_4-29 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for qwen2-vl-run_vera_4-29 This model is a fine-tuned version of [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-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="Hosseinka/qwen2-vl-run_vera_4-29", 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/hosseinksh/qwen2-vl-run_vera_4-29/runs/38qvq2oo) This model was trained with SFT. ### Framework versions - TRL: 0.16.0 - Transformers: 4.50.3 - Pytorch: 2.4.1+cu121 - Datasets: 3.5.0 - 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}} } ```

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