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nawazadroit/vaidya1st_toolcalling	nawazadroit	2025-05-03T19:36:32Z	5	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Llama-3.2-1B-Instruct", "base_model:quantized:unsloth/Llama-3.2-1B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-01T23:29:22Z	--- base_model: unsloth/Llama-3.2-1B-Instruct tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # 🧠 Llama 3.2 - 1B Instruct \| Toolcalling Test Finetuned Model (ADROIT NOT USING ANYMORE DEVELOPED FOR TESTING ONLY) - Developed by: nawazadroit - License: [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) - Finetuned from base model: [`unsloth/Llama-3.2-1B-Instruct`](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct) --- ## 🧪 Purpose This is a simple tool-calling test model, finetuned specifically on a custom dataset (`dataset.json`) related to scheme application workflows. It is designed for structured assistant-like behavior with basic tool invocation capabilities, especially in the domain of public healthcare scheme assistance (e.g., applying to MJPJAY / PM-JAY schemes in government hospitals). --- ## 🚀 Features - ✅ Lightweight 1B parameter model - ✅ Uses Huggingface's TRL library for reward modeling and instruction tuning - ✅ Ideal for testing local tool-calling setups - ✅ Compatible with GGUF format for efficient inference --- ## 📂 Dataset Info The model was trained on a JSON dataset (`dataset.json`) containing multi-turn dialogues structured for: - Verifying patient eligibility - Applying health schemes - Extracting document information - Calling specific tools via structured API-like responses --- ## 🔗 Links - 📚 [Hugging Face Transformers](https://github.com/huggingface/transformers) - 🔬 [TRL Library](https://github.com/huggingface/trl) ---
memevis/supp44	memevis	2025-05-03T19:34:36Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T19:33:58Z	--- 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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MinaMila/phi3_LoRa_ACSEmployment_2_cfda_ep7_22	MinaMila	2025-05-03T19:33:41Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-03T19:33:34Z	--- 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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Bosh353/Reinforce-CartpolePolicy	Bosh353	2025-05-03T19:31:27Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2025-05-03T19:31:16Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartpolePolicy results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 422.00 +/- 132.62 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
Gandarych/xlm-roberta-base-finetuned-panx-en	Gandarych	2025-05-03T19:27:49Z	0	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-05-03T19:24:22Z	--- library_name: transformers license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-en 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. --> # xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3875 - F1: 0.7035 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.0308 \| 1.0 \| 50 \| 0.4977 \| 0.5765 \| \| 0.4871 \| 2.0 \| 100 \| 0.3848 \| 0.6805 \| \| 0.363 \| 3.0 \| 150 \| 0.3875 \| 0.7035 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
Dharil/Combined-labelled-psudeo-labelled-data-toxic-bert	Dharil	2025-05-03T19:27:15Z	8	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:unitary/toxic-bert", "base_model:finetune:unitary/toxic-bert", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-03-10T17:24:18Z	--- library_name: transformers license: apache-2.0 base_model: unitary/toxic-bert tags: - generated_from_trainer metrics: - accuracy model-index: - name: results 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. --> # results This model is a fine-tuned version of [unitary/toxic-bert](https://huggingface.co/unitary/toxic-bert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1082 - Accuracy: 80.4114 - Hamming Loss: 0.0464 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 128 - 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: 500 - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Hamming Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------------:\| \| 0.1217 \| 1.0 \| 140 \| 0.1082 \| 80.4114 \| 0.0464 \| \| 0.2059 \| 2.0 \| 280 \| 0.2015 \| 75.9392 \| 0.0625 \| \| 0.2193 \| 3.0 \| 420 \| 0.2308 \| 76.1181 \| 0.0732 \| \| 0.204 \| 4.0 \| 560 \| 0.2173 \| 76.1181 \| 0.0732 \| ### Framework versions - Transformers 4.46.3 - Pytorch 2.4.1+cu121 - Tokenizers 0.20.3
Mr-FineTuner/Test___01_withNewEval_andWithin-1	Mr-FineTuner	2025-05-03T19:26:58Z	0	0	null	[ "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-03T19:24:59Z	# Fine-Tuned LLaMA-3-8B CEFR Model This is a fine-tuned version of `unsloth/llama-3-8b-instruct-bnb-4bit` for CEFR-level sentence generation. - Base Model: unsloth/llama-3-8b-instruct-bnb-4bit - Fine-Tuning: LoRA with SMOTE-balanced dataset - Training Details: - Dataset: CEFR-level sentences with SMOTE and undersampling for balance - LoRA Parameters: r=32, lora_alpha=32, lora_dropout=0.5 - Training Args: learning_rate=2e-5, batch_size=8, epochs=0.1, cosine scheduler - Optimizer: adamw_8bit - Early Stopping: Patience=3, threshold=0.01 - Evaluation Metrics (Exact Matches): - CEFR Classifier Accuracy: 0.250 - Precision (Macro): 0.130 - Recall (Macro): 0.250 - F1-Score (Macro): 0.153 - Evaluation Metrics (Within ±1 Level): - CEFR Classifier Accuracy: 0.733 - Precision (Macro): 0.701 - Recall (Macro): 0.733 - F1-Score (Macro): 0.687 - Other Metrics: - Perplexity: 14.218 - Diversity (Unique Sentences): 0.933 - Inference Time (ms): 2208.386 - Model Size (GB): 4.8 - Robustness (F1): 0.145 - Confusion Matrix (Exact Matches): - CSV: [confusion_matrix.csv](confusion_matrix.csv) - Image: [confusion_matrix.png](confusion_matrix.png) - Per-Class Confusion Metrics (Exact Matches): - A1: TP=0, FP=2, FN=10, TN=48 - A2: TP=0, FP=0, FN=10, TN=50 - B1: TP=10, FP=29, FN=0, TN=21 - B2: TP=2, FP=7, FN=8, TN=43 - C1: TP=3, FP=7, FN=7, TN=43 - C2: TP=0, FP=0, FN=10, TN=50 - Usage: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("Mr-FineTuner/Test___01_withNewEval") tokenizer = AutoTokenizer.from_pretrained("Mr-FineTuner/Test___01_withNewEval") # Example inference prompt = "<\|user\|>Generate a CEFR B1 level sentence.<\|end\|>" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_length=50) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` Uploaded using `huggingface_hub`.
linoyts/hidream-yarn-art-lora-v2-trainer-multi	linoyts	2025-05-03T19:26:38Z	0	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "hidream", "hidream-diffusers", "template:sd-lora", "base_model:HiDream-ai/HiDream-I1-Full", "base_model:adapter:HiDream-ai/HiDream-I1-Full", "license:mit", "region:us" ]	text-to-image	2025-05-02T15:12:26Z	--- base_model: HiDream-ai/HiDream-I1-Full library_name: diffusers license: mit instance_prompt: a dog, yarn art style widget: - text: yoda, yarn art style output: url: image_0.png - text: yoda, yarn art style output: url: image_1.png - text: yoda, yarn art style output: url: image_2.png - text: yoda, yarn art style output: url: image_3.png tags: - text-to-image - diffusers-training - diffusers - lora - hidream - hidream-diffusers - template:sd-lora - text-to-image - diffusers-training - diffusers - lora - hidream - hidream-diffusers - template:sd-lora --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # HiDream Image DreamBooth LoRA - linoyts/hidream-yarn-art-lora-v2-trainer-multi <Gallery /> ## Model description These are linoyts/hidream-yarn-art-lora-v2-trainer-multi DreamBooth LoRA weights for HiDream-ai/HiDream-I1-Full. The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [HiDream Image diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_hidream.md). ## Trigger words You should use `a dog, yarn art style` to trigger the image generation. ## Download model [Download the *.safetensors LoRA](linoyts/hidream-yarn-art-lora-v2-trainer-multi/tree/main) in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py >>> import torch >>> from transformers import PreTrainedTokenizerFast, LlamaForCausalLM >>> from diffusers import HiDreamImagePipeline >>> tokenizer_4 = PreTrainedTokenizerFast.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct") >>> text_encoder_4 = LlamaForCausalLM.from_pretrained( ... "meta-llama/Meta-Llama-3.1-8B-Instruct", ... output_hidden_states=True, ... output_attentions=True, ... torch_dtype=torch.bfloat16, ... ) >>> pipe = HiDreamImagePipeline.from_pretrained( ... "HiDream-ai/HiDream-I1-Full", ... tokenizer_4=tokenizer_4, ... text_encoder_4=text_encoder_4, ... torch_dtype=torch.bfloat16, ... ) >>> pipe.enable_model_cpu_offload() >>> pipe.load_lora_weights(f"linoyts/hidream-yarn-art-lora-v2-trainer-multi") >>> image = pipe(f"a dog, yarn art style").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) ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
dgambettaphd/M_llm2_gen9_WXS_doc1000_synt64_lr1e-04_acm_SYNLAST	dgambettaphd	2025-05-03T19:25:55Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-03T19:25:43Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
memevis/supp43	memevis	2025-05-03T19:25:35Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T19:25:00Z	--- 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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SiddharthaDeyChetia/corgy_face_LoRA	SiddharthaDeyChetia	2025-05-03T19:25:31Z	0	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-05-03T19:23:25Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: person widget: [] tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - SiddharthaDeyChetia/corgy_face_LoRA <Gallery /> ## Model description These are SiddharthaDeyChetia/corgy_face_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use person to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](SiddharthaDeyChetia/corgy_face_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
Gandarych/xlm-roberta-base-finetuned-panx-it	Gandarych	2025-05-03T19:24:16Z	0	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-05-03T19:20:38Z	--- library_name: transformers license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-it 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. --> # xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2465 - F1: 0.8298 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.7606 \| 1.0 \| 70 \| 0.3201 \| 0.7487 \| \| 0.2895 \| 2.0 \| 140 \| 0.2722 \| 0.7857 \| \| 0.1834 \| 3.0 \| 210 \| 0.2465 \| 0.8298 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
Flo0620/Qwen2_5-VL-7B-8bit_FixedBinary	Flo0620	2025-05-03T19:16:42Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-03T15:55:23Z	--- base_model: Qwen/Qwen2.5-VL-7B-Instruct library_name: transformers model_name: Qwen2_5-VL-7B-8bit_FixedBinary tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Qwen2_5-VL-7B-8bit_FixedBinary This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-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="Flo0620/Qwen2_5-VL-7B-8bit_FixedBinary", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.52.0.dev0 - Pytorch: 2.6.0+cu124 - 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}} } ```
hshankar113/region-id-pred-noCrops-convnext	hshankar113	2025-05-03T19:15:25Z	0	0	transformers	[ "transformers", "safetensors", "convnext", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:facebook/convnext-large-224-22k-1k", "base_model:finetune:facebook/convnext-large-224-22k-1k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-03T13:39:57Z	--- library_name: transformers license: apache-2.0 base_model: facebook/convnext-large-224-22k-1k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: region-id-pred-noCrops-convnext results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.948509485094851 --- <!-- 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. --> # region-id-pred-noCrops-convnext This model is a fine-tuned version of [facebook/convnext-large-224-22k-1k](https://huggingface.co/facebook/convnext-large-224-22k-1k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1967 - Accuracy: 0.9485 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - 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: cosine - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.51.1 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.21.0
Gandarych/xlm-roberta-base-finetuned-panx-de-fr	Gandarych	2025-05-03T19:13:32Z	0	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-05-01T15:57:25Z	--- library_name: transformers license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr 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. --> # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1608 - F1: 0.8611 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2843 \| 1.0 \| 715 \| 0.1784 \| 0.8286 \| \| 0.1453 \| 2.0 \| 1430 \| 0.1629 \| 0.8477 \| \| 0.0949 \| 3.0 \| 2145 \| 0.1608 \| 0.8611 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
jdchang/full-with-label-bs-1024-sg-2-step-2430	jdchang	2025-05-03T19:13:11Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-05-03T19:13:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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AhmedCodes65/parser_qwen2_3b_instruct_finetune	AhmedCodes65	2025-05-03T19:11:05Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T19:04:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
zenon015/heyman	zenon015	2025-05-03T19:08:20Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-03T19:08:20Z	--- license: apache-2.0 ---
TareksLab/Carnelian-DL-V1-70B	TareksLab	2025-05-03T19:04:21Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2406.11617", "base_model:ArliAI/Llama-3.3-70B-ArliAI-RPMax-v1.4", "base_model:merge:ArliAI/Llama-3.3-70B-ArliAI-RPMax-v1.4", "base_model:TheDrummer/Anubis-70B-v1", "base_model:merge:TheDrummer/Anubis-70B-v1", "base_model:huihui-ai/DeepSeek-R1-Distill-Llama-70B-abliterated", "base_model:merge:huihui-ai/DeepSeek-R1-Distill-Llama-70B-abliterated", "base_model:nbeerbower/Llama-3.1-Nemotron-lorablated-70B", "base_model:merge:nbeerbower/Llama-3.1-Nemotron-lorablated-70B", "base_model:nbeerbower/llama3.1-kartoffeldes-70B", "base_model:merge:nbeerbower/llama3.1-kartoffeldes-70B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T18:53:34Z	--- base_model: - ArliAI/Llama-3.3-70B-ArliAI-RPMax-v1.4 - nbeerbower/Llama-3.1-Nemotron-lorablated-70B - TheDrummer/Anubis-70B-v1 - huihui-ai/DeepSeek-R1-Distill-Llama-70B-abliterated - nbeerbower/llama3.1-kartoffeldes-70B library_name: transformers tags: - mergekit - merge --- # MERGE2 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 [Linear DELLA](https://arxiv.org/abs/2406.11617) merge method using [nbeerbower/Llama-3.1-Nemotron-lorablated-70B](https://huggingface.co/nbeerbower/Llama-3.1-Nemotron-lorablated-70B) as a base. ### Models Merged The following models were included in the merge: * [ArliAI/Llama-3.3-70B-ArliAI-RPMax-v1.4](https://huggingface.co/ArliAI/Llama-3.3-70B-ArliAI-RPMax-v1.4) * [TheDrummer/Anubis-70B-v1](https://huggingface.co/TheDrummer/Anubis-70B-v1) * [huihui-ai/DeepSeek-R1-Distill-Llama-70B-abliterated](https://huggingface.co/huihui-ai/DeepSeek-R1-Distill-Llama-70B-abliterated) * [nbeerbower/llama3.1-kartoffeldes-70B](https://huggingface.co/nbeerbower/llama3.1-kartoffeldes-70B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: nbeerbower/llama3.1-kartoffeldes-70B parameters: weight: 0.20 density: 0.7 epsilon: 0.2 lambda: 1.1 - model: huihui-ai/DeepSeek-R1-Distill-Llama-70B-abliterated parameters: weight: 0.20 density: 0.7 epsilon: 0.2 lambda: 1.1 - model: TheDrummer/Anubis-70B-v1 parameters: weight: 0.20 density: 0.7 epsilon: 0.2 lambda: 1.1 - model: ArliAI/Llama-3.3-70B-ArliAI-RPMax-v1.4 parameters: weight: 0.20 density: 0.7 epsilon: 0.2 lambda: 1.1 - model: nbeerbower/Llama-3.1-Nemotron-lorablated-70B parameters: weight: 0.20 density: 0.7 epsilon: 0.1 lambda: 1.0 merge_method: della_linear base_model: nbeerbower/Llama-3.1-Nemotron-lorablated-70B parameters: normalize: false int8_mask: true dtype: float32 out_dtype: bfloat16 chat_template: llama3 tokenizer: source: nbeerbower/llama3.1-kartoffeldes-70B pad_to_multiple_of: 8 ```
Aaquib/gemma-2b-sft-alpaca	Aaquib	2025-05-03T19:02:06Z	0	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T18:21:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID SFT'd version of google/gemma-2b. Training performed solely on yahma/alpaca-cleaned. No further learning was performed. ## Model Details Hyperparameters to replicate: - lr=1e-5 - num_epochs=1 - train_batch_size=40 - test_batch_size=32 - max_seq_len=256 ### Model Description - Finetuned from model: [google/gemma-2b], uses the same tokenizer and chat template as google/gemma-2b-it
Tiledish/Tiledish	Tiledish	2025-05-03T19:01:59Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-03T19:01:59Z	--- license: apache-2.0 ---
mlx-community/Llama-4-Scout-17B-16E-Instruct-4bit	mlx-community	2025-05-03T18:59:15Z	1,909	6	transformers	[ "transformers", "safetensors", "llama4", "image-text-to-text", "facebook", "meta", "pytorch", "llama", "llama-4", "mlx", "conversational", "ar", "de", "en", "es", "fr", "hi", "id", "it", "pt", "th", "tl", "vi", "base_model:meta-llama/Llama-4-Scout-17B-16E", "base_model:finetune:meta-llama/Llama-4-Scout-17B-16E", "license:other", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-04-06T17:48:42Z	--- library_name: transformers language: - ar - de - en - es - fr - hi - id - it - pt - th - tl - vi base_model: - meta-llama/Llama-4-Scout-17B-16E tags: - facebook - meta - pytorch - llama - llama-4 - mlx extra_gated_prompt: 'LLAMA 4 COMMUNITY LICENSE AGREEMENT Llama 4 Version Effective Date: April 5, 2025 "Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. 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The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement.' 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 extra_gated_heading: Please be sure to provide your full legal name, date of birth, and full organization name with all corporate identifiers. Avoid the use of acronyms and special characters. Failure to follow these instructions may prevent you from accessing this model and others on Hugging Face. You will not have the ability to edit this form after submission, so please ensure all information is accurate. license: other license_name: llama4 --- # mlx-community/Llama-4-Scout-17B-16E-Instruct-4bit This model was converted to MLX format from [`meta-llama/Llama-4-Scout-17B-16E-Instruct`]() using mlx-vlm version 0.1.21. Refer to the [original model card](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct) for more details on the model. ## Use with mlx ```bash pip install -U mlx-vlm ``` ```bash python -m mlx_vlm.generate --model mlx-community/Llama-4-Scout-17B-16E-Instruct-4bit --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image> ```
grazh/Qwen2.5-0.5B-finetuned-paraphrasing-bfloat16-v1	grazh	2025-05-03T18:58:51Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T18:57:50Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
neptunbeast/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-whistling_barky_capybara	neptunbeast	2025-05-03T18:57:35Z	10	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am whistling barky capybara", "trl", "conversational", "arxiv:2402.03300", "base_model:unsloth/Qwen2.5-0.5B-Instruct", "base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-06T03:45:23Z	--- base_model: unsloth/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-whistling_barky_capybara tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am whistling barky capybara - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-whistling_barky_capybara This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/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="neptunbeast/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-whistling_barky_capybara", 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: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.7.0 - Datasets: 3.5.1 - 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
madhav-k/llama-3-8b-chat-indic	madhav-k	2025-05-03T18:57:30Z	0	1	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-03T14:51:43Z	--- 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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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mdlbkp/kakaro28dbackup	mdlbkp	2025-05-03T18:56:10Z	0	0	null	[ "text-to-image", "region:us" ]	text-to-image	2025-05-03T18:53:44Z	--- license_name: fair-ai-public-license-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ pipeline_tag: text-to-image --- backup of https://civitai.com/models/1538319 model merge made by vay_kakarot
thkotsikas/runpodmodel	thkotsikas	2025-05-03T18:56:08Z	0	0	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "fluxgym", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-03T18:41:23Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym base_model: black-forest-labs/FLUX.1-dev instance_prompt: adwnis bouboukos 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 --- # adwnis A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `adwnis bouboukos` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc. Weights for this model are available in Safetensors format.
HarethahMo/qwen2.5-1.5B-base-abliterated	HarethahMo	2025-05-03T18:53:59Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T18:48:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Gandarych/xlm-roberta-base-finetuned-panx-de	Gandarych	2025-05-03T18:53:04Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-05-01T15:10:33Z	--- library_name: transformers license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de 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. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1399 - F1: 0.8620 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2556 \| 1.0 \| 525 \| 0.1498 \| 0.8286 \| \| 0.1305 \| 2.0 \| 1050 \| 0.1374 \| 0.8535 \| \| 0.0786 \| 3.0 \| 1575 \| 0.1399 \| 0.8620 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
memevis/walk16	memevis	2025-05-03T18:52:55Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T18:52:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
kostiantynk1205/703e77ec-08b9-41a3-8235-d90d6b9eafbb	kostiantynk1205	2025-05-03T18:51:46Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "unsloth", "endpoints_compatible", "region:us" ]	null	2025-05-03T18:51:11Z	--- library_name: transformers model_name: kostiantynk1205/703e77ec-08b9-41a3-8235-d90d6b9eafbb tags: - generated_from_trainer - unsloth licence: license --- # Model Card for kostiantynk1205/703e77ec-08b9-41a3-8235-d90d6b9eafbb This model is a fine-tuned version of [None](https://huggingface.co/None). 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="None", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.5.1+cu124 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
geetach/legal-ft-450c1026-6554-476b-96f1-34f426f777c8	geetach	2025-05-03T18:45:07Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:156", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "base_model:Snowflake/snowflake-arctic-embed-l", "base_model:finetune:Snowflake/snowflake-arctic-embed-l", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-05-03T18:44:00Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:156 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: Snowflake/snowflake-arctic-embed-l widget: - source_sentence: 'What are some challenges mentioned in building large language models like GPT-4? ' sentences: - 'This prompt-driven custom interface feature is so powerful and easy to build (once you’ve figured out the gnarly details of browser sandboxing) that I expect it to show up as a feature in a wide range of products in 2025. Universal access to the best models lasted for just a few short months For a few short months this year all three of the best available models—GPT-4o, Claude 3.5 Sonnet and Gemini 1.5 Pro—were freely available to most of the world.' - 'Large Language Models They’re actually quite easy to build You can run LLMs on your own devices Hobbyists can build their own fine-tuned models We don’t yet know how to build GPT-4 Vibes Based Development LLMs are really smart, and also really, really dumb Gullibility is the biggest unsolved problem Code may be the best application The ethics of this space remain diabolically complex My blog in 2023' - 'I’ve found myself using this a lot. I noticed how much I was relying on it in October and wrote Everything I built with Claude Artifacts this week, describing 14 little tools I had put together in a seven day period. Since then, a whole bunch of other teams have built similar systems. GitHub announced their version of this—GitHub Spark—in October. Mistral Chat added it as a feature called Canvas in November. Steve Krouse from Val Town built a version of it against Cerebras, showcasing how a 2,000 token/second LLM can iterate on an application with changes visible in less than a second.' - source_sentence: 'What are some examples of large language models that can run entirely in a browser or on personal devices mentioned in the context? ' sentences: - 'The boring yet crucial secret behind good system prompts is test-driven development. You don’t write down a system prompt and find ways to test it. You write down tests and find a system prompt that passes them. It’s become abundantly clear over the course of 2024 that writing good automated evals for LLM-powered systems is the skill that’s most needed to build useful applications on top of these models. If you have a strong eval suite you can adopt new models faster, iterate better and build more reliable and useful product features than your competition. Vercel’s Malte Ubl:' - 'Now add a walrus: Prompt engineering in DALL-E 3 32.8k 41.2k Web LLM runs the vicuna-7b Large Language Model entirely in your browser, and it’s very impressive 32.5k 38.2k ChatGPT can’t access the internet, even though it really looks like it can 30.5k 34.2k Stanford Alpaca, and the acceleration of on-device large language model development 29.7k 35.7k Run Llama 2 on your own Mac using LLM and Homebrew 27.9k 33.6k Midjourney 5.1 26.7k 33.4k Think of language models like ChatGPT as a “calculator for words” 25k 31.8k Multi-modal prompt injection image attacks against GPT-4V 23.7k 27.4k' - 'Things we learned about LLMs in 2024 Simon Willison’s Weblog Subscribe Things we learned about LLMs in 2024 31st December 2024 A lot has happened in the world of Large Language Models over the course of 2024. Here’s a review of things we figured out about the field in the past twelve months, plus my attempt at identifying key themes and pivotal moments. This is a sequel to my review of 2023. In this article:' - source_sentence: 'What were some of the economic consequences of the railway construction boom in the 1800s? ' sentences: - 'I run a bunch of them on my laptop. I run Mistral 7B (a surprisingly great model) on my iPhone. You can install several different apps to get your own, local, completely private LLM. My own LLM project provides a CLI tool for running an array of different models via plugins. You can even run them entirely in your browser using WebAssembly and the latest Chrome! Hobbyists can build their own fine-tuned models I said earlier that building an LLM was still out of reach of hobbyists. That may be true for training from scratch, but fine-tuning one of those models is another matter entirely.' - 'An interesting point of comparison here could be the way railways rolled out around the world in the 1800s. Constructing these required enormous investments and had a massive environmental impact, and many of the lines that were built turned out to be unnecessary—sometimes multiple lines from different companies serving the exact same routes! The resulting bubbles contributed to several financial crashes, see Wikipedia for Panic of 1873, Panic of 1893, Panic of 1901 and the UK’s Railway Mania. They left us with a lot of useful infrastructure and a great deal of bankruptcies and environmental damage. The year of slop' - 'In 2024, almost every significant model vendor released multi-modal models. We saw the Claude 3 series from Anthropic in March, Gemini 1.5 Pro in April (images, audio and video), then September brought Qwen2-VL and Mistral’s Pixtral 12B and Meta’s Llama 3.2 11B and 90B vision models. We got audio input and output from OpenAI in October, then November saw SmolVLM from Hugging Face and December saw image and video models from Amazon Nova. In October I upgraded my LLM CLI tool to support multi-modal models via attachments. It now has plugins for a whole collection of different vision models.' - source_sentence: Why was the model named o3 instead of o2, and when is it expected to ship? sentences: - 'A lot of people are excited about AI agents—an infuriatingly vague term that seems to be converging on “AI systems that can go away and act on your behalf”. We’ve been talking about them all year, but I’ve seen few if any examples of them running in production, despite lots of exciting prototypes. I think this is because of gullibility. Can we solve this? Honestly, I’m beginning to suspect that you can’t fully solve gullibility without achieving AGI. So it may be quite a while before those agent dreams can really start to come true! Code may be the best application Over the course of the year, it’s become increasingly clear that writing code is one of the things LLMs are most capable of.' - 'There’s now a fascinating ecosystem of people training their own models on top of these foundations, publishing those models, building fine-tuning datasets and sharing those too. The Hugging Face Open LLM Leaderboard is one place that tracks these. I can’t even attempt to count them, and any count would be out-of-date within a few hours. The best overall openly licensed LLM at any time is rarely a foundation model: instead, it’s whichever fine-tuned community model has most recently discovered the best combination of fine-tuning data. This is a huge advantage for open over closed models: the closed, hosted models don’t have thousands of researchers and hobbyists around the world collaborating and competing to improve them.' - 'The biggest innovation here is that it opens up a new way to scale a model: instead of improving model performance purely through additional compute at training time, models can now take on harder problems by spending more compute on inference. The sequel to o1, o3 (they skipped “o2” for European trademark reasons) was announced on 20th December with an impressive result against the ARC-AGI benchmark, albeit one that likely involved more than $1,000,000 of compute time expense! o3 is expected to ship in January. I doubt many people have real-world problems that would benefit from that level of compute expenditure—I certainly don’t!—but it appears to be a genuine next step in LLM architecture for taking on much harder problems.' - source_sentence: 'When did Meta release the original Llama model? ' sentences: - 'Then in February, Meta released Llama. And a few weeks later in March, Georgi Gerganov released code that got it working on a MacBook. I wrote about how Large language models are having their Stable Diffusion moment, and with hindsight that was a very good call! This unleashed a whirlwind of innovation, which was accelerated further in July when Meta released Llama 2—an improved version which, crucially, included permission for commercial use. Today there are literally thousands of LLMs that can be run locally, on all manner of different devices.' - 'Each photo would need 260 input tokens and around 100 output tokens. 260 * 68,000 = 17,680,000 input tokens 17,680,000 * $0.0375/million = $0.66 100 * 68,000 = 6,800,000 output tokens 6,800,000 * $0.15/million = $1.02 That’s a total cost of $1.68 to process 68,000 images. That’s so absurdly cheap I had to run the numbers three times to confirm I got it right. How good are those descriptions? Here’s what I got from this command: llm -m gemini-1.5-flash-8b-latest describe -a IMG_1825.jpeg' - 'So far, I think they’re a net positive. I’ve used them on a personal level to improve my productivity (and entertain myself) in all sorts of different ways. I think people who learn how to use them effectively can gain a significant boost to their quality of life. A lot of people are yet to be sold on their value! Some think their negatives outweigh their positives, some think they are all hot air, and some even think they represent an existential threat to humanity. They’re actually quite easy to build The most surprising thing we’ve learned about LLMs this year is that they’re actually quite easy to build.' pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 model-index: - name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l results: - task: type: information-retrieval name: Information Retrieval dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy@1 value: 0.875 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 1.0 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 1.0 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.875 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.20000000000000004 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.10000000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.875 name: Cosine Recall@1 - type: cosine_recall@3 value: 1.0 name: Cosine Recall@3 - type: cosine_recall@5 value: 1.0 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9538662191964322 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9375 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9375 name: Cosine Map@100 --- # SentenceTransformer based on Snowflake/snowflake-arctic-embed-l This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l) <!-- at revision d8fb21ca8d905d2832ee8b96c894d3298964346b --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 1024 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("geetach/legal-ft-450c1026-6554-476b-96f1-34f426f777c8") # Run inference sentences = [ 'When did Meta release the original Llama model? ', 'Then in February, Meta released Llama. And a few weeks later in March, Georgi Gerganov released code that got it working on a MacBook.\nI wrote about how Large language models are having their Stable Diffusion moment, and with hindsight that was a very good call!\nThis unleashed a whirlwind of innovation, which was accelerated further in July when Meta released Llama 2—an improved version which, crucially, included permission for commercial use.\nToday there are literally thousands of LLMs that can be run locally, on all manner of different devices.', 'So far, I think they’re a net positive. I’ve used them on a personal level to improve my productivity (and entertain myself) in all sorts of different ways. I think people who learn how to use them effectively can gain a significant boost to their quality of life.\nA lot of people are yet to be sold on their value! Some think their negatives outweigh their positives, some think they are all hot air, and some even think they represent an existential threat to humanity.\nThey’re actually quite easy to build\nThe most surprising thing we’ve learned about LLMs this year is that they’re actually quite easy to build.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Information Retrieval * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.875 \| \| cosine_accuracy@3 \| 1.0 \| \| cosine_accuracy@5 \| 1.0 \| \| cosine_accuracy@10 \| 1.0 \| \| cosine_precision@1 \| 0.875 \| \| cosine_precision@3 \| 0.3333 \| \| cosine_precision@5 \| 0.2 \| \| cosine_precision@10 \| 0.1 \| \| cosine_recall@1 \| 0.875 \| \| cosine_recall@3 \| 1.0 \| \| cosine_recall@5 \| 1.0 \| \| cosine_recall@10 \| 1.0 \| \| cosine_ndcg@10 \| 0.9539 \| \| cosine_mrr@10 \| 0.9375 \| \| cosine_map@100 \| 0.9375 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 156 training samples * Columns: <code>sentence_0</code> and <code>sentence_1</code> * Approximate statistics based on the first 156 samples: \| \| sentence_0 \| sentence_1 \| \|:--------\|:-----------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 12 tokens</li><li>mean: 20.86 tokens</li><li>max: 34 tokens</li></ul> \| <ul><li>min: 43 tokens</li><li>mean: 135.14 tokens</li><li>max: 214 tokens</li></ul> \| * Samples: \| sentence_0 \| sentence_1 \| \|:-----------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>What are some companies mentioned that have developed multi-modal audio models? </code> \| <code>Your browser does not support the audio element.<br><br>OpenAI aren’t the only group with a multi-modal audio model. Google’s Gemini also accepts audio input, and the Google Gemini apps can speak in a similar way to ChatGPT now. Amazon also pre-announced voice mode for Amazon Nova, but that’s meant to roll out in Q1 of 2025.<br>Google’s NotebookLM, released in September, took audio output to a new level by producing spookily realistic conversations between two “podcast hosts” about anything you fed into their tool. They later added custom instructions, so naturally I turned them into pelicans:<br><br><br>Your browser does not support the audio element.</code> \| \| <code>How did Google’s NotebookLM enhance audio output in its September release?</code> \| <code>Your browser does not support the audio element.<br><br>OpenAI aren’t the only group with a multi-modal audio model. Google’s Gemini also accepts audio input, and the Google Gemini apps can speak in a similar way to ChatGPT now. Amazon also pre-announced voice mode for Amazon Nova, but that’s meant to roll out in Q1 of 2025.<br>Google’s NotebookLM, released in September, took audio output to a new level by producing spookily realistic conversations between two “podcast hosts” about anything you fed into their tool. They later added custom instructions, so naturally I turned them into pelicans:<br><br><br>Your browser does not support the audio element.</code> \| \| <code>What model and specifications does the personal laptop mentioned in the context have? </code> \| <code>My personal laptop is a 64GB M2 MacBook Pro from 2023. It’s a powerful machine, but it’s also nearly two years old now—and crucially it’s the same laptop I’ve been using ever since I first ran an LLM on my computer back in March 2023 (see Large language models are having their Stable Diffusion moment).<br>That same laptop that could just about run a GPT-3-class model in March last year has now run multiple GPT-4 class models! Some of my notes on that:</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 10 - `per_device_eval_batch_size`: 10 - `num_train_epochs`: 10 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 10 - `per_device_eval_batch_size`: 10 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 10 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `tp_size`: 0 - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin </details> ### Training Logs \| Epoch \| Step \| cosine_ndcg@10 \| \|:-----:\|:----:\|:--------------:\| \| 1.0 \| 16 \| 0.9583 \| \| 2.0 \| 32 \| 0.9455 \| \| 3.0 \| 48 \| 0.9430 \| \| 3.125 \| 50 \| 0.9430 \| \| 4.0 \| 64 \| 0.9539 \| \| 5.0 \| 80 \| 0.9539 \| \| 6.0 \| 96 \| 0.9539 \| \| 6.25 \| 100 \| 0.9539 \| \| 7.0 \| 112 \| 0.9539 \| \| 8.0 \| 128 \| 0.9539 \| \| 9.0 \| 144 \| 0.9539 \| \| 9.375 \| 150 \| 0.9539 \| \| 10.0 \| 160 \| 0.9539 \| ### Framework Versions - Python: 3.11.12 - Sentence Transformers: 4.1.0 - Transformers: 4.51.3 - PyTorch: 2.6.0+cu124 - Accelerate: 1.6.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
tyfeng1997/Qwen3-14B-Reasoning-Conversational	tyfeng1997	2025-05-03T18:44:32Z	0	0	transformers	[ "transformers", "pytorch", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T17:56:49Z	--- base_model: unsloth/qwen3-14b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: tyfeng1997 - License: apache-2.0 - Finetuned from model : unsloth/qwen3-14b-unsloth-bnb-4bit This qwen3 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)
Mr-FineTuner/Test___01_withNewEvalofConfusionMatrix	Mr-FineTuner	2025-05-03T18:43:13Z	0	0	null	[ "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-03T18:41:04Z	# Fine-Tuned LLaMA-3-8B CEFR Model This is a fine-tuned version of `unsloth/llama-3-8b-instruct-bnb-4bit` for CEFR-level sentence generation. - Base Model: unsloth/llama-3-8b-instruct-bnb-4bit - Fine-Tuning: LoRA with SMOTE-balanced dataset - Training Details: - Dataset: CEFR-level sentences with SMOTE and undersampling for balance - LoRA Parameters: r=32, lora_alpha=32, lora_dropout=0.5 - Training Args: learning_rate=2e-5, batch_size=8, epochs=0.1, cosine scheduler - Optimizer: adamw_8bit - Early Stopping: Patience=3, threshold=0.01 - Evaluation Metrics: - CEFR Classifier Accuracy: 0.250 - Precision (Macro): 0.130 - Recall (Macro): 0.250 - F1-Score (Macro): 0.153 - Perplexity: 14.218 - Diversity (Unique Sentences): 0.933 - Inference Time (ms): 2242.946 - Model Size (GB): 4.8 - Robustness (F1): 0.145 - Confusion Matrix: - CSV: [confusion_matrix.csv](confusion_matrix.csv) - Image: [confusion_matrix.png](confusion_matrix.png) - Per-Class Confusion Metrics: - A1: TP=0, FP=2, FN=10, TN=48 - A2: TP=0, FP=0, FN=10, TN=50 - B1: TP=10, FP=29, FN=0, TN=21 - B2: TP=2, FP=7, FN=8, TN=43 - C1: TP=3, FP=7, FN=7, TN=43 - C2: TP=0, FP=0, FN=10, TN=50 - Usage: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("Mr-FineTuner/Test___01_withNewEval") tokenizer = AutoTokenizer.from_pretrained("Mr-FineTuner/Test___01_withNewEval") # Example inference prompt = "<\|user\|>Generate a CEFR B1 level sentence.<\|end\|>" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_length=50) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` Uploaded using `huggingface_hub`.
Sypee19/Sypee	Sypee19	2025-05-03T18:43:01Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-03T18:43:01Z	--- license: apache-2.0 ---
Hachipo/OpenCoder-8B-Base-MIFT-ja_1000_2	Hachipo	2025-05-03T18:40:01Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T18:36:19Z	--- library_name: transformers tags: - trl - sft --- # 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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AWARRITech/NewQwen2.5-0.5B	AWARRITech	2025-05-03T18:39:53Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-02T15:07:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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pawan2411/modernbert-ct4a-11-no-aug	pawan2411	2025-05-03T18:39:25Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "modernbert", "text-classification", "generated_from_trainer", "base_model:answerdotai/ModernBERT-large", "base_model:finetune:answerdotai/ModernBERT-large", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-03T18:26:38Z	--- library_name: transformers license: apache-2.0 base_model: answerdotai/ModernBERT-large tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: modernbert-ct4a-11-no-aug 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. --> # modernbert-ct4a-11-no-aug This model is a fine-tuned version of [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4779 - Accuracy: 0.9075 - F1: 0.7601 - Auc: 0.8332 - Accuracy Per Label: [0.9051094890510949, 0.9197080291970803, 0.8978102189781022] - F1 Per Label: [0.7547169811320755, 0.7441860465116279, 0.78125] - Auc Per Label: [0.853083853083853, 0.8031878031878031, 0.8433752141633354] ## 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: 8 - eval_batch_size: 8 - 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Auc \| Accuracy Per Label \| F1 Per Label \| Auc Per Label \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:------:\|:------------------------------------------------------------:\|:------------------------------------------------------------:\|:------------------------------------------------------------:\| \| No log \| 1.0 \| 154 \| 0.2880 \| 0.8686 \| 0.5709 \| 0.7054 \| [0.8759124087591241, 0.8686131386861314, 0.8613138686131386] \| [0.5853658536585366, 0.5, 0.6274509803921569] \| [0.7172557172557175, 0.6685724185724186, 0.73043974871502] \| \| No log \| 2.0 \| 308 \| 0.2495 \| 0.8954 \| 0.6899 \| 0.7750 \| [0.8686131386861314, 0.9197080291970803, 0.8978102189781022] \| [0.5714285714285714, 0.7317073170731707, 0.7666666666666667] \| [0.7127512127512129, 0.7884615384615384, 0.8236721873215306] \| \| No log \| 3.0 \| 462 \| 0.3805 \| 0.9100 \| 0.7742 \| 0.8496 \| [0.9051094890510949, 0.9197080291970803, 0.9051094890510949] \| [0.7450980392156863, 0.7659574468085106, 0.8115942028985508] \| [0.8383575883575884, 0.8326403326403327, 0.8777841233580811] \| \| 0.1833 \| 4.0 \| 616 \| 0.4659 \| 0.9051 \| 0.7504 \| 0.8234 \| [0.9051094890510949, 0.9197080291970803, 0.8905109489051095] \| [0.7450980392156863, 0.7441860465116279, 0.7619047619047619] \| [0.8383575883575884, 0.8031878031878031, 0.8286693318103941] \| \| 0.1833 \| 5.0 \| 770 \| 0.4779 \| 0.9075 \| 0.7601 \| 0.8332 \| [0.9051094890510949, 0.9197080291970803, 0.8978102189781022] \| [0.7547169811320755, 0.7441860465116279, 0.78125] \| [0.853083853083853, 0.8031878031878031, 0.8433752141633354] \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
AthenaAgent42/llama-r1-ft13k-ex5-lora	AthenaAgent42	2025-05-03T18:38:09Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-03T16:52:55Z	--- base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: AthenaAgent42 - License: apache-2.0 - Finetuned from model : unsloth/deepseek-r1-distill-llama-8b-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)
theanhth12/08052002	theanhth12	2025-05-03T18:37:27Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-03T18:37:27Z	--- license: apache-2.0 ---
mradermacher/MedicalEDI-14b-EDI-Reasoning-test-i1-GGUF	mradermacher	2025-05-03T18:37:21Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:Shaleen123/MedicalEDI-14b-EDI-Reasoning-test", "base_model:quantized:Shaleen123/MedicalEDI-14b-EDI-Reasoning-test", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-03T14:12:39Z	--- base_model: Shaleen123/MedicalEDI-14b-EDI-Reasoning-test language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Shaleen123/MedicalEDI-14b-EDI-Reasoning-test <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-test-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/MedicalEDI-14b-EDI-Reasoning-test-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-test.i1-IQ1_S.gguf) \| i1-IQ1_S \| 3.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-test-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-test.i1-IQ1_M.gguf) \| i1-IQ1_M \| 4.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-test-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-test.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-test-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-test.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-test-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-test.i1-IQ2_S.gguf) \| i1-IQ2_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-test-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-test.i1-IQ2_M.gguf) \| i1-IQ2_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-test-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-test.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 5.5 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-test-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-test.i1-Q2_K.gguf) \| i1-Q2_K \| 5.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-test-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-test.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 6.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-test-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-test.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 6.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-test-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-test.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 6.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-test-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-test.i1-IQ3_S.gguf) \| i1-IQ3_S \| 6.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-test-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-test.i1-IQ3_M.gguf) \| i1-IQ3_M \| 7.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-test-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-test.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 7.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-test-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-test.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 8.0 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-test-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-test.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 8.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-test-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-test.i1-Q4_0.gguf) \| i1-Q4_0 \| 8.6 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-test-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-test.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 8.6 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-test-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-test.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 8.7 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-test-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-test.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-test-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-test.i1-Q4_1.gguf) \| i1-Q4_1 \| 9.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-test-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-test.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-test-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-test.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-test-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-test.i1-Q6_K.gguf) \| i1-Q6_K \| 12.2 \| 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 -->
Dhanielji9asdx/cipher	Dhanielji9asdx	2025-05-03T18:36:45Z	0	0	null	[ "license:other", "region:us" ]	null	2025-05-03T18:00:11Z	--- 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 ---
Disya/Smoothie-Qwen3-8B-exl2-6.5bpw-h8	Disya	2025-05-03T18:36:00Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "dnotitia", "nlp", "llm", "slm", "conversation", "chat", "reasoning", "conversational", "en", "ko", "base_model:dnotitia/Smoothie-Qwen3-8B", "base_model:quantized:dnotitia/Smoothie-Qwen3-8B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2025-05-03T18:30:29Z	--- language: - en - ko license: apache-2.0 tags: - dnotitia - nlp - llm - slm - conversation - chat - reasoning base_model: - dnotitia/Smoothie-Qwen3-8B library_name: transformers pipeline_tag: text-generation --- # Smoothie Qwen <img src="https://github.com/dnotitia/smoothie-qwen/raw/main/asset/smoothie-qwen-logo.png" width="400" style="max-width: 100%;"> Smoothie Qwen is a lightweight adjustment tool that smooths token probabilities in Qwen and similar models, enhancing balanced multilingual generation capabilities. For more details, please refer to <https://github.com/dnotitia/smoothie-qwen>. ## Configuration - Base model: Qwen/Qwen3-8B - Minimum scale factor: 0.5 - Smoothness: 10.0 - Sample size: 1000 - Window size: 4 - N-gram weights: [0.5, 0.3, 0.2] ## Unicode Ranges - Range 1: 0x4e00 - 0x9fff - Range 2: 0x3400 - 0x4dbf - Range 3: 0x20000 - 0x2a6df - Range 4: 0xf900 - 0xfaff - Range 5: 0x2e80 - 0x2eff - Range 6: 0x2f00 - 0x2fdf - Range 7: 0x2ff0 - 0x2fff - Range 8: 0x3000 - 0x303f - Range 9: 0x31c0 - 0x31ef - Range 10: 0x3200 - 0x32ff - Range 11: 0x3300 - 0x33ff ## Statistics - Target tokens: 26,153 - Broken tokens: 1,457 - Modified tokens: 27,564
Momin-Shahzad/Reinforce-Unit4.2	Momin-Shahzad	2025-05-03T18:34:15Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2025-05-03T18:22:43Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Unit4.2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 11.04 +/- 12.22 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
Talyiamira/nvidia-large-llm-final	Talyiamira	2025-05-03T18:33:36Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-03T18:31:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ashish-soni08/llama3_2_3B_f16_gguf	ashish-soni08	2025-05-03T18:33:15Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T18:31:15Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: ashish-soni08 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-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)
rodrigomt/whisper-large-v3-turbo	rodrigomt	2025-05-03T18:33:14Z	0	0	transformers.js	[ "transformers.js", "onnx", "whisper", "automatic-speech-recognition", "base_model:openai/whisper-large-v3-turbo", "base_model:quantized:openai/whisper-large-v3-turbo", "region:us" ]	automatic-speech-recognition	2025-05-03T18:33:13Z	--- base_model: openai/whisper-large-v3-turbo library_name: transformers.js --- https://huggingface.co/openai/whisper-large-v3-turbo with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
zhyh1436/myTrainlow	zhyh1436	2025-05-03T18:33:10Z	0	0	null	[ "gguf", "qwen2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-03T16:35:32Z	--- license: apache-2.0 ---
ArtemBuk/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-vocal_foraging_ibis	ArtemBuk	2025-05-03T18:29:12Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am vocal foraging ibis", "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	2025-04-21T22:12:26Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-vocal_foraging_ibis tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am vocal foraging ibis - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-vocal_foraging_ibis 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="ArtemBuk/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-vocal_foraging_ibis", 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: 2.6.0 - 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}} } ```
xbilek25/whisper-medium-en-cv-6.0	xbilek25	2025-05-03T18:28:41Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "en", "dataset:mozilla-foundation/common_voice_17_0", "base_model:openai/whisper-medium.en", "base_model:finetune:openai/whisper-medium.en", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-03T17:05:37Z	--- library_name: transformers language: - en license: apache-2.0 base_model: openai/whisper-medium.en tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_17_0 metrics: - wer model-index: - name: whisper-medium-en-cv-6.0 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 17.0 type: mozilla-foundation/common_voice_17_0 args: 'config: en, split: test' metrics: - name: Wer type: wer value: 34.72137170851194 --- <!-- 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-medium-en-cv-6.0 This model is a fine-tuned version of [openai/whisper-medium.en](https://huggingface.co/openai/whisper-medium.en) on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set: - Loss: 1.1135 - Wer: 34.7214 ## 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: 48 - eval_batch_size: 4 - 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 - lr_scheduler_warmup_steps: 150 - training_steps: 1500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| No log \| 0 \| 0 \| 2.4185 \| 46.5401 \| \| 0.7543 \| 0.2 \| 300 \| 0.9822 \| 37.0178 \| \| 0.3116 \| 1.2 \| 600 \| 0.9713 \| 35.2725 \| \| 0.124 \| 2.2 \| 900 \| 1.0252 \| 34.4152 \| \| 0.0523 \| 3.2 \| 1200 \| 1.0789 \| 34.4764 \| \| 0.0269 \| 4.2 \| 1500 \| 1.1135 \| 34.7214 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
Used2last/Flyfly	Used2last	2025-05-03T18:25:44Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-03T18:25:44Z	--- license: apache-2.0 ---
Hachipo/OpenCoder-8B-Base-EnTrans_1000_2	Hachipo	2025-05-03T18:25:21Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T18:21:30Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
nmictgbd13/NMI	nmictgbd13	2025-05-03T18:24:05Z	0	0	null	[ "license:bsd-3-clause-clear", "region:us" ]	null	2025-05-03T18:24:05Z	--- license: bsd-3-clause-clear ---
sky-2002/Marathi-SmolLM2-145M	sky-2002	2025-05-03T18:23:04Z	3	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "dataset:ai4bharat/sangraha", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-21T14:01:51Z	--- library_name: transformers tags: [] datasets: - ai4bharat/sangraha --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details An experimental 145M parameter pre-trained base model for marathi. Inspired by SmolLM2 and its architecture. Pre-trained on verified marathi split of the [`ai4bharat/sangraha`](https://huggingface.co/datasets/ai4bharat/sangraha) dataset, around ~2.8 billion tokens. Note: This is an experimental model and will be followed by more pre-training, followed by task specific instruction finetuning. ## How to use ```python # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("sky-2002/Marathi-SmolLM2-145M") model = AutoModelForCausalLM.from_pretrained("sky-2002/Marathi-SmolLM2-145M") sentence = "पुणे विद्यापीठाने म्हटले आहे" inputs = tokenizer(sentence, return_tensors="pt") output = model.generate(inputs, max_length=50) print(tokenizer.batch_decode(output, skip_special_tokens=True)) ``` ### Model Description, data and training details Architecture: SmolLM2 based Tokenizer: Uses the `sarvamai/sarvam-1` tokenizer, since it has been trained on indic languages and has lower fertility rates than existing multilingual tokenizers. Training dataset: The training dataset covers the following domains. ![alt text](image.png) <!-- Provide a longer summary of what this model is. --> Training: - Trained using modal platform on an A100. - Trained for 1 epoch on verified marathi split of sangraha dataset, covering ~5.8M samples. This model can generate coherent text, especially in the domains similar to those in the training dataset. <!-- ### 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 model is trained on data of 2.8 B tokens and using a context length of 512, due to computational constraints of training. Often gives out gibberish if prompt is not related to domains shown, or if in a conversational style. <!-- This section is meant to convey both technical and sociotechnical limitations. --> <!-- [More Information Needed] --> <!-- ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> <!-- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. --> <!-- ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] --> <!-- ## Training Details ### Training Data --> <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> <!-- [More Information Needed] ### Training Procedure --> <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> <!-- #### Preprocessing [optional] [More Information Needed] --> <!-- #### Training Hyperparameters --> <!-- - Training regime: [More Information Needed] fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> <!-- #### Speeds, Sizes, Times [optional] --> <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> <!-- [More Information Needed] --> <!-- ## Evaluation --> <!-- This section describes the evaluation protocols and provides the results. --> <!-- ### Testing Data, Factors & Metrics #### Testing Data --> <!-- This should link to a Dataset Card if possible. --> <!-- [More Information Needed] #### Factors --> <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> <!-- [More Information Needed] #### Metrics --> <!-- These are the evaluation metrics being used, ideally with a description of why. --> <!-- [More Information Needed] ### Results [More Information Needed] #### Summary --> <!-- ## Model Examination [optional] --> <!-- Relevant interpretability work for the model goes here --> <!-- [More Information Needed] ## Environmental Impact --> <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> <!-- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: A100 - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] --> <!-- ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] --> <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> <!-- BibTeX: [More Information Needed] APA:** [More Information Needed] --> <!-- ## Glossary [optional] --> <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> <!-- [More Information Needed] ## More Information [optional] --> <!-- [More Information Needed] ## Model Card Authors [optional] --> <!-- [More Information Needed] ## Model Card Contact [More Information Needed] -->
mradermacher/Qwen3-235B-A22B-i1-GGUF	mradermacher	2025-05-03T18:21:52Z	0	3	transformers	[ "transformers", "en", "base_model:Qwen/Qwen3-235B-A22B", "base_model:finetune:Qwen/Qwen3-235B-A22B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-01T23:18:52Z	--- base_model: Qwen/Qwen3-235B-A22B language: - en library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-235B-A22B/blob/main/LICENSE quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Qwen/Qwen3-235B-A22B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Qwen3-235B-A22B-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 \| \|:-----\|:-----\|--------:\|:------\| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ2_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ2_M.gguf.part2of2) \| i1-IQ2_M \| 77.3 \| \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q2_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q2_K.gguf.part2of2) \| i1-Q2_K \| 85.8 \| IQ3_XXS probably better \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ3_XXS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ3_XXS.gguf.part2of2) \| i1-IQ3_XXS \| 90.5 \| lower quality \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ3_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ3_XS.gguf.part2of2) \| i1-IQ3_XS \| 96.1 \| \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q3_K_S.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q3_K_S.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q3_K_S.gguf.part3of3) \| i1-Q3_K_S \| 101.5 \| IQ3_XS probably better \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ3_S.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ3_S.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ3_S.gguf.part3of3) \| i1-IQ3_S \| 101.6 \| beats Q3_K* \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ3_M.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ3_M.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ3_M.gguf.part3of3) \| i1-IQ3_M \| 103.2 \| \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q3_K_M.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q3_K_M.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q3_K_M.gguf.part3of3) \| i1-Q3_K_M \| 112.5 \| IQ3_S probably better \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q3_K_L.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q3_K_L.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q3_K_L.gguf.part3of3) \| i1-Q3_K_L \| 121.9 \| IQ3_M probably better \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ4_XS.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ4_XS.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-IQ4_XS.gguf.part3of3) \| i1-IQ4_XS \| 125.4 \| \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q4_0.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q4_0.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q4_0.gguf.part3of3) \| i1-Q4_0 \| 133.2 \| fast, low quality \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q4_K_S.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q4_K_S.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q4_K_S.gguf.part3of3) \| i1-Q4_K_S \| 133.8 \| optimal size/speed/quality \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q4_K_M.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q4_K_M.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q4_K_M.gguf.part3of3) \| i1-Q4_K_M \| 142.3 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q4_1.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q4_1.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q4_1.gguf.part3of3) \| i1-Q4_1 \| 147.3 \| \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q5_K_S.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q5_K_S.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q5_K_S.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q5_K_S.gguf.part4of4) \| i1-Q5_K_S \| 162.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q5_K_M.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q5_K_M.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q5_K_M.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q5_K_M.gguf.part4of4) \| i1-Q5_K_M \| 166.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q6_K.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q6_K.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q6_K.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/Qwen3-235B-A22B-i1-GGUF/resolve/main/Qwen3-235B-A22B.i1-Q6_K.gguf.part4of4) \| i1-Q6_K \| 193.1 \| 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 -->
Luisdure/oma-lora	Luisdure	2025-05-03T18:21:34Z	0	0	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "fluxgym", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-03T18:21:25Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym base_model: black-forest-labs/FLUX.1-dev instance_prompt: oma_lora 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 --- # oma_lora A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `oma_lora` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc. Weights for this model are available in Safetensors format.
MR-Jones/Qwen3-vLLM	MR-Jones	2025-05-03T18:21:19Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-03T18:01:13Z	--- base_model: unsloth/qwen3-14b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: MR-Jones - License: apache-2.0 - Finetuned from model : unsloth/qwen3-14b-unsloth-bnb-4bit This qwen3 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)
fax4ever/test-culturalitems	fax4ever	2025-05-03T18:19:54Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "text-classification", "license:apache-2.0", "region:us" ]	text-classification	2025-05-03T18:19:52Z	--- license: apache-2.0 pipeline_tag: text-classification tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: fax4ever/culturalitems-no-transformer - Paper: [More Information Needed] - Docs: [More Information Needed]
andriuusa/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-graceful_stalking_capybara	andriuusa	2025-05-03T18:17:57Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am graceful stalking capybara", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-03T17:01:44Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-graceful_stalking_capybara tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am graceful stalking capybara - unsloth - trl licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-graceful_stalking_capybara This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="andriuusa/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-graceful_stalking_capybara", 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: 2.6.0 - Datasets: 3.5.1 - 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}} } ```
new-sapna-shah-viral-videos-original-link/exclusive.sapna.shah.viral.video.original.link	new-sapna-shah-viral-videos-original-link	2025-05-03T18:16:49Z	0	0	null	[ "region:us" ]	null	2025-05-03T18:16:23Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/fn84hrnu?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
mradermacher/kepler-urdu-poetry-tiny-GGUF	mradermacher	2025-05-03T18:13:58Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:keplersystems/kepler-urdu-poetry-tiny", "base_model:quantized:keplersystems/kepler-urdu-poetry-tiny", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T18:01:16Z	--- base_model: keplersystems/kepler-urdu-poetry-tiny language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/keplersystems/kepler-urdu-poetry-tiny <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/kepler-urdu-poetry-tiny-GGUF/resolve/main/kepler-urdu-poetry-tiny.Q2_K.gguf) \| Q2_K \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/kepler-urdu-poetry-tiny-GGUF/resolve/main/kepler-urdu-poetry-tiny.Q3_K_S.gguf) \| Q3_K_S \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/kepler-urdu-poetry-tiny-GGUF/resolve/main/kepler-urdu-poetry-tiny.Q3_K_M.gguf) \| Q3_K_M \| 1.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/kepler-urdu-poetry-tiny-GGUF/resolve/main/kepler-urdu-poetry-tiny.Q3_K_L.gguf) \| Q3_K_L \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/kepler-urdu-poetry-tiny-GGUF/resolve/main/kepler-urdu-poetry-tiny.IQ4_XS.gguf) \| IQ4_XS \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/kepler-urdu-poetry-tiny-GGUF/resolve/main/kepler-urdu-poetry-tiny.Q4_K_S.gguf) \| Q4_K_S \| 1.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/kepler-urdu-poetry-tiny-GGUF/resolve/main/kepler-urdu-poetry-tiny.Q4_K_M.gguf) \| Q4_K_M \| 1.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/kepler-urdu-poetry-tiny-GGUF/resolve/main/kepler-urdu-poetry-tiny.Q5_K_S.gguf) \| Q5_K_S \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/kepler-urdu-poetry-tiny-GGUF/resolve/main/kepler-urdu-poetry-tiny.Q5_K_M.gguf) \| Q5_K_M \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/kepler-urdu-poetry-tiny-GGUF/resolve/main/kepler-urdu-poetry-tiny.Q6_K.gguf) \| Q6_K \| 1.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/kepler-urdu-poetry-tiny-GGUF/resolve/main/kepler-urdu-poetry-tiny.Q8_0.gguf) \| Q8_0 \| 2.3 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/kepler-urdu-poetry-tiny-GGUF/resolve/main/kepler-urdu-poetry-tiny.f16.gguf) \| f16 \| 4.2 \| 16 bpw, overkill \| 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 -->
cooplanki/salopin	cooplanki	2025-05-03T18:13:09Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2025-05-03T18:13:09Z	--- license: bigscience-openrail-m ---
lisabdunlap/Llama-3.1-8B-Instruct-unsloth-bnb-4bit-r32-e3-lr0.0001-mixed-actors_reviews_markdown-new	lisabdunlap	2025-05-03T18:12:03Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Llama-3.1-8B-Instruct-unsloth-bnb-4bit", "base_model:finetune:unsloth/Llama-3.1-8B-Instruct-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T18:09:44Z	--- base_model: unsloth/Llama-3.1-8B-Instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: lisabdunlap - License: apache-2.0 - Finetuned from model : unsloth/Llama-3.1-8B-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)
ma921/gpt2-large_h_dpo_imdb_noise40_epoch5_gamma0.3	ma921	2025-05-03T18:10:34Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:ma921/gpt2-large-sft-imdb", "base_model:finetune:ma921/gpt2-large-sft-imdb", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T18:09:40Z	--- library_name: transformers license: mit base_model: ma921/gpt2-large-sft-imdb tags: - generated_from_trainer model-index: - name: gpt2-large_h_dpo_imdb_noise40_epoch5_gamma0.3 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-large_h_dpo_imdb_noise40_epoch5_gamma0.3 This model is a fine-tuned version of [ma921/gpt2-large-sft-imdb](https://huggingface.co/ma921/gpt2-large-sft-imdb) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 32 - total_train_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: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
ma921/gpt2-large_c_dpo_imdb_noise0_epoch5	ma921	2025-05-03T18:09:52Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:ma921/gpt2-large-sft-imdb", "base_model:finetune:ma921/gpt2-large-sft-imdb", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T18:08:53Z	--- library_name: transformers license: mit base_model: ma921/gpt2-large-sft-imdb tags: - generated_from_trainer model-index: - name: gpt2-large_c_dpo_imdb_noise0_epoch5 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-large_c_dpo_imdb_noise0_epoch5 This model is a fine-tuned version of [ma921/gpt2-large-sft-imdb](https://huggingface.co/ma921/gpt2-large-sft-imdb) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 32 - total_train_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: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
RichardErkhov/JBTheDev_-_bryan_16b_model-gguf	RichardErkhov	2025-05-03T18:08:28Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T15:59:33Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) bryan_16b_model - GGUF - Model creator: https://huggingface.co/JBTheDev/ - Original model: https://huggingface.co/JBTheDev/bryan_16b_model/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [bryan_16b_model.Q2_K.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q2_K.gguf) \| Q2_K \| 2.96GB \| \| [bryan_16b_model.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.IQ3_XS.gguf) \| IQ3_XS \| 3.28GB \| \| [bryan_16b_model.IQ3_S.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.IQ3_S.gguf) \| IQ3_S \| 3.43GB \| \| [bryan_16b_model.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q3_K_S.gguf) \| Q3_K_S \| 3.41GB \| \| [bryan_16b_model.IQ3_M.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.IQ3_M.gguf) \| IQ3_M \| 3.52GB \| \| [bryan_16b_model.Q3_K.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q3_K.gguf) \| Q3_K \| 3.74GB \| \| [bryan_16b_model.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q3_K_M.gguf) \| Q3_K_M \| 3.74GB \| \| [bryan_16b_model.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q3_K_L.gguf) \| Q3_K_L \| 4.03GB \| \| [bryan_16b_model.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.IQ4_XS.gguf) \| IQ4_XS \| 4.18GB \| \| [bryan_16b_model.Q4_0.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q4_0.gguf) \| Q4_0 \| 4.34GB \| \| [bryan_16b_model.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.IQ4_NL.gguf) \| IQ4_NL \| 4.38GB \| \| [bryan_16b_model.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q4_K_S.gguf) \| Q4_K_S \| 4.37GB \| \| [bryan_16b_model.Q4_K.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q4_K.gguf) \| Q4_K \| 4.58GB \| \| [bryan_16b_model.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q4_K_M.gguf) \| Q4_K_M \| 4.58GB \| \| [bryan_16b_model.Q4_1.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q4_1.gguf) \| Q4_1 \| 4.78GB \| \| [bryan_16b_model.Q5_0.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q5_0.gguf) \| Q5_0 \| 5.21GB \| \| [bryan_16b_model.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q5_K_S.gguf) \| Q5_K_S \| 5.21GB \| \| [bryan_16b_model.Q5_K.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q5_K.gguf) \| Q5_K \| 5.34GB \| \| [bryan_16b_model.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q5_K_M.gguf) \| Q5_K_M \| 5.34GB \| \| [bryan_16b_model.Q5_1.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q5_1.gguf) \| Q5_1 \| 5.65GB \| \| [bryan_16b_model.Q6_K.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q6_K.gguf) \| Q6_K \| 6.14GB \| \| [bryan_16b_model.Q8_0.gguf](https://huggingface.co/RichardErkhov/JBTheDev_-_bryan_16b_model-gguf/blob/main/bryan_16b_model.Q8_0.gguf) \| Q8_0 \| 7.95GB \| Original model description: --- base_model: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft --- # Uploaded model - Developed by: JBTheDev - License: apache-2.0 - Finetuned from model : unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mradermacher/ruozhiReasoner-Qwen3-4B-GGUF	mradermacher	2025-05-03T18:03:50Z	0	0	transformers	[ "transformers", "gguf", "llama-factory", "en", "base_model:XzWang/ruozhiReasoner-Qwen3-4B", "base_model:quantized:XzWang/ruozhiReasoner-Qwen3-4B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T17:36:07Z	--- base_model: XzWang/ruozhiReasoner-Qwen3-4B language: - en library_name: transformers quantized_by: mradermacher tags: - llama-factory --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/XzWang/ruozhiReasoner-Qwen3-4B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/ruozhiReasoner-Qwen3-4B-GGUF/resolve/main/ruozhiReasoner-Qwen3-4B.Q2_K.gguf) \| Q2_K \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-4B-GGUF/resolve/main/ruozhiReasoner-Qwen3-4B.Q3_K_S.gguf) \| Q3_K_S \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-4B-GGUF/resolve/main/ruozhiReasoner-Qwen3-4B.Q3_K_M.gguf) \| Q3_K_M \| 2.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-4B-GGUF/resolve/main/ruozhiReasoner-Qwen3-4B.Q3_K_L.gguf) \| Q3_K_L \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-4B-GGUF/resolve/main/ruozhiReasoner-Qwen3-4B.IQ4_XS.gguf) \| IQ4_XS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-4B-GGUF/resolve/main/ruozhiReasoner-Qwen3-4B.Q4_K_S.gguf) \| Q4_K_S \| 2.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-4B-GGUF/resolve/main/ruozhiReasoner-Qwen3-4B.Q4_K_M.gguf) \| Q4_K_M \| 2.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-4B-GGUF/resolve/main/ruozhiReasoner-Qwen3-4B.Q5_K_S.gguf) \| Q5_K_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-4B-GGUF/resolve/main/ruozhiReasoner-Qwen3-4B.Q5_K_M.gguf) \| Q5_K_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-4B-GGUF/resolve/main/ruozhiReasoner-Qwen3-4B.Q6_K.gguf) \| Q6_K \| 3.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-4B-GGUF/resolve/main/ruozhiReasoner-Qwen3-4B.Q8_0.gguf) \| Q8_0 \| 4.4 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-4B-GGUF/resolve/main/ruozhiReasoner-Qwen3-4B.f16.gguf) \| f16 \| 8.2 \| 16 bpw, overkill \| 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 -->
Momin-Shahzad/Reinforce-unit4.1	Momin-Shahzad	2025-05-03T18:03:24Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2025-05-03T18:03:13Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-unit4.1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
ashish-soni08/llama3_2_3B_lora_model	ashish-soni08	2025-05-03T18:02:35Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-03T18:02:17Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: ashish-soni08 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-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)
CrimsonZockt/LiviaInhudes-FLUXLORA	CrimsonZockt	2025-05-03T18:02:27Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]	text-to-image	2025-05-03T18:02:01Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: Livia Inhudes, black tanktop, professional headshot, photoshoot. output: url: images/Livia Inhudes, black tanktop, professional head....png base_model: black-forest-labs/FLUX.1-dev instance_prompt: Livia Inhudes --- # LiviaInhudes <Gallery /> ## Model description This is a LORA Model that i have train on Weights.gg ## Trigger words You should use `Livia Inhudes` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/CrimsonZockt/LiviaInhudes-FLUXLORA/tree/main) them in the Files & versions tab.
colmansekh/coolman	colmansekh	2025-05-03T18:01:47Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2025-05-03T18:01:47Z	--- license: artistic-2.0 ---
AbstractPhil/SD15-Surge-V1	AbstractPhil	2025-05-03T18:00:53Z	11	0	diffusers	[ "diffusers", "safetensors", "diffusers:OmegaDiffusionPipeline", "region:us" ]	null	2025-05-02T03:08:38Z	Doesn't work. Needs additional data. Early surge formula is partly implemented here with the adasurge and cascade's derived single model forms.
mradermacher/G1-0.6B-GGUF	mradermacher	2025-05-03T17:58:52Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:ShriKaranHanda/G1-0.6B", "base_model:quantized:ShriKaranHanda/G1-0.6B", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T17:52:14Z	--- base_model: ShriKaranHanda/G1-0.6B language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/ShriKaranHanda/G1-0.6B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/G1-0.6B-GGUF/resolve/main/G1-0.6B.Q2_K.gguf) \| Q2_K \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/G1-0.6B-GGUF/resolve/main/G1-0.6B.Q3_K_S.gguf) \| Q3_K_S \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/G1-0.6B-GGUF/resolve/main/G1-0.6B.Q3_K_M.gguf) \| Q3_K_M \| 0.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/G1-0.6B-GGUF/resolve/main/G1-0.6B.Q3_K_L.gguf) \| Q3_K_L \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/G1-0.6B-GGUF/resolve/main/G1-0.6B.IQ4_XS.gguf) \| IQ4_XS \| 0.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/G1-0.6B-GGUF/resolve/main/G1-0.6B.Q4_K_S.gguf) \| Q4_K_S \| 0.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/G1-0.6B-GGUF/resolve/main/G1-0.6B.Q4_K_M.gguf) \| Q4_K_M \| 0.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/G1-0.6B-GGUF/resolve/main/G1-0.6B.Q5_K_S.gguf) \| Q5_K_S \| 0.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/G1-0.6B-GGUF/resolve/main/G1-0.6B.Q5_K_M.gguf) \| Q5_K_M \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/G1-0.6B-GGUF/resolve/main/G1-0.6B.Q6_K.gguf) \| Q6_K \| 0.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/G1-0.6B-GGUF/resolve/main/G1-0.6B.Q8_0.gguf) \| Q8_0 \| 0.9 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/G1-0.6B-GGUF/resolve/main/G1-0.6B.f16.gguf) \| f16 \| 1.6 \| 16 bpw, overkill \| 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 -->
malditogenio/genio	malditogenio	2025-05-03T17:57:42Z	0	0	null	[ "license:other", "region:us" ]	null	2025-05-03T16:57:24Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---
ASethi04/meta-llama-Llama-3.1-8B-pubmedqa-first-lora-4-0.0004	ASethi04	2025-05-03T17:56:58Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "endpoints_compatible", "region:us" ]	null	2025-05-03T16:13:23Z	--- base_model: meta-llama/Llama-3.1-8B library_name: transformers model_name: meta-llama-Llama-3.1-8B-pubmedqa-first-lora-4-0.0004 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for meta-llama-Llama-3.1-8B-pubmedqa-first-lora-4-0.0004 This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B). 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="ASethi04/meta-llama-Llama-3.1-8B-pubmedqa-first-lora-4-0.0004", 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/torchql-org/huggingface/runs/02ne3gp7) This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - 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}} } ```
Membersuger/Euro_24	Membersuger	2025-05-03T17:56:35Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T17:15:07Z	--- 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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Ihssane123/w2v-bert-2.0-mongolian-colab-CV16.0	Ihssane123	2025-05-03T17:55:20Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-03T17:54:15Z	--- 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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TOMFORD79/Fly52	TOMFORD79	2025-05-03T17:55:19Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-03T17:44:27Z	--- 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).
hydroxai/grpo_saved_lora_7	hydroxai	2025-05-03T17:54:28Z	0	0	peft	[ "peft", "safetensors", "arxiv:2503.21819", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:adapter:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-05-03T17:23:08Z	--- base_model: - Qwen/Qwen2.5-7B-Instruct library_name: peft license: apache-2.0 --- # GRPO-LoRA-Base This is a LoRA adapter trained using the GRPO (Group Relative Policy Optimization) algorithm with a multi-label reward model, fine-tuned on Qwen2.5-0.5B for safe and aligned language generation. ## 🔍 Overview - Base Model: Qwen/Qwen2.5-0.5B-Instruct - Tuning Method: GRPO (No value critic, group-based relative rewards) - LoRA Adapter: Applied to attention and MLP projection layers - Epochs: 3 - Steps: 1000 - GPU Memory Usage: ~50% (4-bit + LoRA) ## 📊 Reward Model A RoBERTa-based multi-label regression model was used to compute rewards on four alignment axes: - Politeness - Meaningfulness - Actionability - Safety Each output was scored in [0,1], and the sum of the four scores was used as the scalar reward. ## 🧪 Training Data - Dataset: 7,000 adversarial prompts crafted to challenge LLM alignment - Format: Prompt-response pairs with human-annotated alignment scores - Split: 6K training / 1K validation ## 🏁 Evaluation \| Metric \| Base \| Fine-Tuned \| Δ \| \|---------------\|------\|------------\|-------\| \| Politeness \| 0.48 \| 0.59 \| +0.11 \| \| Meaningfulness \| 0.61 \| 0.65 \| +0.04 \| \| Actionability \| 0.53 \| 0.66 \| +0.13 \| \| Safety \| 0.42 \| 0.70 \| +0.28 \| \| Combined \| 0.54 \| 0.66 \| +0.12 \| ## 🚀 How to Use ```python from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct") tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct") adapter = PeftModel.from_pretrained(base_model, "hydroxai/grpo_saved_lora_7") inputs = tokenizer("How can we improve online safety?", return_tensors="pt") outputs = adapter.generate(**inputs, max_new_tokens=100) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## ✍️ Citation If you use this model, please cite: ```bibtex @article{li2025safegrpo, title = {Optimizing Safe and Aligned Language Generation: A Multi-Objective GRPO Approach}, author = {Li, Xuying and Li, Zhuo and Kosuga, Yuji and Bian, Victor}, journal = {arXiv preprint arXiv:2503.21819}, year = {2025}, url = {https://arxiv.org/abs/2503.21819} } ``` Maintained by HydroX AI.
Rank001/whisper-tiny-vaani-hindi-ONNX	Rank001	2025-05-03T17:52:54Z	0	0	transformers.js	[ "transformers.js", "onnx", "whisper", "automatic-speech-recognition", "base_model:ARTPARK-IISc/whisper-tiny-vaani-hindi", "base_model:quantized:ARTPARK-IISc/whisper-tiny-vaani-hindi", "region:us" ]	automatic-speech-recognition	2025-05-03T17:52:20Z	--- library_name: transformers.js base_model: - ARTPARK-IISc/whisper-tiny-vaani-hindi --- # whisper-tiny-vaani-hindi (ONNX) This is an ONNX version of [ARTPARK-IISc/whisper-tiny-vaani-hindi](https://huggingface.co/ARTPARK-IISc/whisper-tiny-vaani-hindi). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
stokemctoke/Rachel-McAdams_v01_F1D	stokemctoke	2025-05-03T17:52:50Z	0	0	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "ai-toolkit", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-03T17:51:03Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - ai-toolkit widget: - text: R4CH3LMC4D4M5 a woman playing chess at the park, bomb going off in the background output: url: samples/1746294622040__000005000_0.jpg - text: R4CH3LMC4D4M5 a woman holding a coffee cup, in a beanie, sitting at a cafe output: url: samples/1746294637841__000005000_1.jpg - text: R4CH3LMC4D4M5 a woman holding a sign that says, 'Stoke LoRA' output: url: samples/1746294653645__000005000_2.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: R4CH3LMC4D4M5 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 --- # Rachel-McAdams_v01_F1D Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) <Gallery /> ## Trigger words You should use `R4CH3LMC4D4M5` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc. Weights for this model are available in Safetensors format. [Download](/stokemctoke/Rachel-McAdams_v01_F1D/tree/main) them in the Files & versions tab. ## 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.bfloat16).to('cuda') pipeline.load_lora_weights('stokemctoke/Rachel-McAdams_v01_F1D', weight_name='Rachel-McAdams_v01_F1D.safetensors') image = pipeline('R4CH3LMC4D4M5 a woman playing chess at the park, bomb going off in the background').images[0] image.save("my_image.png") ``` 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)
jmalejandrob79/nrmexpmfm	jmalejandrob79	2025-05-03T17:52:33Z	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	2025-05-03T17:03:12Z	--- 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: nrmexpmfm --- # Nrmexpmfm <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 `nrmexpmfm` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "nrmexpmfm", "lora_weights": "https://huggingface.co/jmalejandrob79/nrmexpmfm/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('jmalejandrob79/nrmexpmfm', weight_name='lora.safetensors') image = pipeline('nrmexpmfm').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: 4000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/jmalejandrob79/nrmexpmfm/discussions) to add images that show off what you’ve made with this LoRA.
dgambettaphd/M_llm2_gen8_WXS_doc1000_synt64_lr1e-04_acm_SYNLAST	dgambettaphd	2025-05-03T17:49:58Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-03T17:49:43Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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jnjj/model_no_bias_qwen3-0.6B-Q3_K_L-GGUF	jnjj	2025-05-03T17:48:47Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "base_model:jnjj/model_no_bias_qwen3-0.6B", "base_model:quantized:jnjj/model_no_bias_qwen3-0.6B", "endpoints_compatible", "region:us" ]	null	2025-05-03T16:58:35Z	--- base_model: jnjj/model_no_bias_qwen3-0.6B library_name: transformers tags: - llama-cpp - gguf-my-repo --- # jnjj/model_no_bias_qwen3-0.6B-Q3_K_L-GGUF This model was converted to GGUF format from [`jnjj/model_no_bias_qwen3-0.6B`](https://huggingface.co/jnjj/model_no_bias_qwen3-0.6B) 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/jnjj/model_no_bias_qwen3-0.6B) 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 jnjj/model_no_bias_qwen3-0.6B-Q3_K_L-GGUF --hf-file model_no_bias_qwen3-0.6b-q3_k_l.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo jnjj/model_no_bias_qwen3-0.6B-Q3_K_L-GGUF --hf-file model_no_bias_qwen3-0.6b-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 jnjj/model_no_bias_qwen3-0.6B-Q3_K_L-GGUF --hf-file model_no_bias_qwen3-0.6b-q3_k_l.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo jnjj/model_no_bias_qwen3-0.6B-Q3_K_L-GGUF --hf-file model_no_bias_qwen3-0.6b-q3_k_l.gguf -c 2048 ```
hydroxai/grpo_saved_lora_05	hydroxai	2025-05-03T17:48:27Z	0	0	null	[ "safetensors", "arxiv:2503.21819", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-05-03T17:19:38Z	--- license: apache-2.0 base_model: - Qwen/Qwen2.5-0.5B-Instruct --- # GRPO-LoRA-Base This is a LoRA adapter trained using the GRPO (Group Relative Policy Optimization) algorithm with a multi-label reward model, fine-tuned on Qwen2.5-0.5B for safe and aligned language generation. ## 🔍 Overview - Base Model: Qwen/Qwen2.5-0.5B-Instruct - Tuning Method: GRPO (No value critic, group-based relative rewards) - LoRA Adapter: Applied to attention and MLP projection layers - Epochs: 3 - Steps: 1000 - GPU Memory Usage: ~50% (4-bit + LoRA) ## 📊 Reward Model A RoBERTa-based multi-label regression model was used to compute rewards on four alignment axes: - Politeness - Meaningfulness - Actionability - Safety Each output was scored in [0,1], and the sum of the four scores was used as the scalar reward. ## 🧪 Training Data - Dataset: 7,000 adversarial prompts crafted to challenge LLM alignment - Format: Prompt-response pairs with human-annotated alignment scores - Split: 6K training / 1K validation ## 🏁 Evaluation \| Metric \| Base \| Fine-Tuned \| Δ \| \|---------------\|------\|------------\|-------\| \| Politeness \| 0.48 \| 0.59 \| +0.11 \| \| Meaningfulness \| 0.61 \| 0.65 \| +0.04 \| \| Actionability \| 0.53 \| 0.66 \| +0.13 \| \| Safety \| 0.42 \| 0.70 \| +0.28 \| \| Combined \| 0.54 \| 0.66 \| +0.12 \| ## 🚀 How to Use ```python from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct") tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct") adapter = PeftModel.from_pretrained(base_model, "hydroxai/grpo_saved_lora") inputs = tokenizer("How can we improve online safety?", return_tensors="pt") outputs = adapter.generate(**inputs, max_new_tokens=100) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## ✍️ Citation If you use this model, please cite: ```bibtex @article{li2025safegrpo, title = {Optimizing Safe and Aligned Language Generation: A Multi-Objective GRPO Approach}, author = {Li, Xuying and Li, Zhuo and Kosuga, Yuji and Bian, Victor}, journal = {arXiv preprint arXiv:2503.21819}, year = {2025}, url = {https://arxiv.org/abs/2503.21819} } ``` Maintained by HydroX AI.
Triangle104/QWQ-32B-Dawnwhisper-Q4_K_M-GGUF	Triangle104	2025-05-03T17:48:01Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:DoppelReflEx/QWQ-32B-Dawnwhisper", "base_model:quantized:DoppelReflEx/QWQ-32B-Dawnwhisper", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T17:46:29Z	--- base_model: DoppelReflEx/QWQ-32B-Dawnwhisper library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # Triangle104/QWQ-32B-Dawnwhisper-Q4_K_M-GGUF This model was converted to GGUF format from [`DoppelReflEx/QWQ-32B-Dawnwhisper`](https://huggingface.co/DoppelReflEx/QWQ-32B-Dawnwhisper) 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/DoppelReflEx/QWQ-32B-Dawnwhisper) 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/QWQ-32B-Dawnwhisper-Q4_K_M-GGUF --hf-file qwq-32b-dawnwhisper-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/QWQ-32B-Dawnwhisper-Q4_K_M-GGUF --hf-file qwq-32b-dawnwhisper-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/QWQ-32B-Dawnwhisper-Q4_K_M-GGUF --hf-file qwq-32b-dawnwhisper-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/QWQ-32B-Dawnwhisper-Q4_K_M-GGUF --hf-file qwq-32b-dawnwhisper-q4_k_m.gguf -c 2048 ```
hydroxai/grpo_saved_lora_15	hydroxai	2025-05-03T17:47:45Z	0	0	null	[ "safetensors", "arxiv:2503.21819", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-05-03T17:28:06Z	--- license: apache-2.0 base_model: - Qwen/Qwen2.5-1.5B-Instruct --- # GRPO-LoRA-Base This is a LoRA adapter trained using the GRPO (Group Relative Policy Optimization) algorithm with a multi-label reward model, fine-tuned on Qwen2.5-0.5B for safe and aligned language generation. ## 🔍 Overview - Base Model: Qwen/Qwen2.5-1.5B-Instruct - Tuning Method: GRPO (No value critic, group-based relative rewards) - LoRA Adapter: Applied to attention and MLP projection layers - Epochs: 3 - Steps: 1000 - GPU Memory Usage: ~50% (4-bit + LoRA) ## 📊 Reward Model A RoBERTa-based multi-label regression model was used to compute rewards on four alignment axes: - Politeness - Meaningfulness - Actionability - Safety Each output was scored in [0,1], and the sum of the four scores was used as the scalar reward. ## 🧪 Training Data - Dataset: 7,000 adversarial prompts crafted to challenge LLM alignment - Format: Prompt-response pairs with human-annotated alignment scores - Split: 6K training / 1K validation ## 🏁 Evaluation \| Metric \| Base \| Fine-Tuned \| Δ \| \|---------------\|------\|------------\|-------\| \| Politeness \| 0.48 \| 0.59 \| +0.11 \| \| Meaningfulness \| 0.61 \| 0.65 \| +0.04 \| \| Actionability \| 0.53 \| 0.66 \| +0.13 \| \| Safety \| 0.42 \| 0.70 \| +0.28 \| \| Combined \| 0.54 \| 0.66 \| +0.12 \| ## 🚀 How to Use ```python from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct") tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct") adapter = PeftModel.from_pretrained(base_model, "hydroxai/grpo_saved_lora_15") inputs = tokenizer("How can we improve online safety?", return_tensors="pt") outputs = adapter.generate(**inputs, max_new_tokens=100) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## ✍️ Citation If you use this model, please cite: ```bibtex @article{li2025safegrpo, title = {Optimizing Safe and Aligned Language Generation: A Multi-Objective GRPO Approach}, author = {Li, Xuying and Li, Zhuo and Kosuga, Yuji and Bian, Victor}, journal = {arXiv preprint arXiv:2503.21819}, year = {2025}, url = {https://arxiv.org/abs/2503.21819} } ``` Maintained by HydroX AI.
Isylimanov099/DeepSeekLawyer1000	Isylimanov099	2025-05-03T17:46:57Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-03T17:46:49Z	--- base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Isylimanov099 - License: apache-2.0 - Finetuned from model : unsloth/deepseek-r1-distill-llama-8b-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)
MAAT-EL-DUAT/CHURCH.OF.THE.MEMETIC.MATRIX.STABLE.DIFFUSION.PART.II	MAAT-EL-DUAT	2025-05-03T17:44:08Z	0	0	null	[ "region:us" ]	null	2025-05-03T17:43:55Z	HEAVY RITUAL MEMETIC STABLE DIFFUSION PART/II
MAAT-EL-DUAT/CHURCH.OF.THE.MEMETIC.MATRIX.STABLE.DIFFUSION.PART.I	MAAT-EL-DUAT	2025-05-03T17:43:36Z	0	0	null	[ "region:us" ]	null	2025-05-03T17:43:08Z	HEAVY RITUAL MEMETIC STABLE DIFFUSION GENERATION PART/I
vnyaryan/model_q4_k_m_aks	vnyaryan	2025-05-03T17:42:48Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T17:42:12Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: vnyaryan - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-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)
TareksLab/Amethyst-DT-V1-70B	TareksLab	2025-05-03T17:41:24Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2311.03099", "base_model:Mawdistical/Lured-Lapine-70B", "base_model:merge:Mawdistical/Lured-Lapine-70B", "base_model:Sao10K/70B-L3.3-Cirrus-x1", "base_model:merge:Sao10K/70B-L3.3-Cirrus-x1", "base_model:Sao10K/L3-70B-Euryale-v2.1", "base_model:merge:Sao10K/L3-70B-Euryale-v2.1", "base_model:Sao10K/L3.1-70B-Hanami-x1", "base_model:merge:Sao10K/L3.1-70B-Hanami-x1", "base_model:mlabonne/Hermes-3-Llama-3.1-70B-lorablated", "base_model:merge:mlabonne/Hermes-3-Llama-3.1-70B-lorablated", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T17:30:36Z	--- base_model: - Sao10K/L3-70B-Euryale-v2.1 - mlabonne/Hermes-3-Llama-3.1-70B-lorablated - Sao10K/70B-L3.3-Cirrus-x1 - Mawdistical/Lured-Lapine-70B - Sao10K/L3.1-70B-Hanami-x1 library_name: transformers tags: - mergekit - merge --- # MERGE3 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 [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using [mlabonne/Hermes-3-Llama-3.1-70B-lorablated](https://huggingface.co/mlabonne/Hermes-3-Llama-3.1-70B-lorablated) as a base. ### Models Merged The following models were included in the merge: * [Sao10K/L3-70B-Euryale-v2.1](https://huggingface.co/Sao10K/L3-70B-Euryale-v2.1) * [Sao10K/70B-L3.3-Cirrus-x1](https://huggingface.co/Sao10K/70B-L3.3-Cirrus-x1) * [Mawdistical/Lured-Lapine-70B](https://huggingface.co/Mawdistical/Lured-Lapine-70B) * [Sao10K/L3.1-70B-Hanami-x1](https://huggingface.co/Sao10K/L3.1-70B-Hanami-x1) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Mawdistical/Lured-Lapine-70B parameters: weight: 0.20 density: 0.5 - model: Sao10K/L3.1-70B-Hanami-x1 parameters: weight: 0.20 density: 0.5 - model: Sao10K/L3-70B-Euryale-v2.1 parameters: weight: 0.20 density: 0.5 - model: Sao10K/70B-L3.3-Cirrus-x1 parameters: weight: 0.20 density: 0.5 - model: mlabonne/Hermes-3-Llama-3.1-70B-lorablated parameters: weight: 0.20 density: 0.5 merge_method: dare_ties base_model: mlabonne/Hermes-3-Llama-3.1-70B-lorablated parameters: normalize: false int8_mask: true dtype: float32 out_dtype: bfloat16 chat_template: llama3 tokenizer: source: union pad_to_multiple_of: 8 ```
adi0308/sutd_rag_chatbot	adi0308	2025-05-03T17:39:44Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-1B", "base_model:adapter:meta-llama/Llama-3.2-1B", "license:llama3.2", "region:us" ]	null	2025-05-03T17:39:07Z	--- library_name: peft license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags: - generated_from_trainer model-index: - name: sutd_rag_chatbot 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. --> # sutd_rag_chatbot This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 5 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 40 - 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: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu118 - Datasets 3.5.1 - Tokenizers 0.21.1
Satyach/outputs	Satyach	2025-05-03T17:38:00Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "unsloth", "trl", "grpo", "arxiv:2402.03300", "endpoints_compatible", "region:us" ]	null	2025-05-02T18:21:23Z	--- base_model: unsloth/deepseek-r1-distill-qwen-7b-unsloth-bnb-4bit library_name: transformers model_name: outputs tags: - generated_from_trainer - unsloth - trl - grpo licence: license --- # Model Card for outputs This model is a fine-tuned version of [unsloth/deepseek-r1-distill-qwen-7b-unsloth-bnb-4bit](https://huggingface.co/unsloth/deepseek-r1-distill-qwen-7b-unsloth-bnb-4bit). 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="Satyach/outputs", 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/satyanarayana1999-sn-amazon-web-services/huggingface/runs/huggingface-pytorch-training-2025-05-02-20-50-44-978-fb5q2e-algo-1) 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: 4.52.0.dev0 - Pytorch: 2.6.0 - Datasets: 3.1.0 - 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}} } ```
MR-Jones/Qwen3-lora_model	MR-Jones	2025-05-03T17:37:11Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-03T17:36:54Z	--- base_model: unsloth/qwen3-14b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: MR-Jones - License: apache-2.0 - Finetuned from model : unsloth/qwen3-14b-unsloth-bnb-4bit This qwen3 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)
Lucy-in-the-Sky/DeepSeek-R1-Distill-Qwen-1.5B-DPO-Q4_K_M-GGUF	Lucy-in-the-Sky	2025-05-03T17:33:48Z	0	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "open-r1", "trl", "dpo", "llama-cpp", "gguf-my-repo", "dataset:LuyiCui/numina-deepseek-r1-qwen-7b-efficient-test-preference", "base_model:LuyiCui/DeepSeek-R1-Distill-Qwen-1.5B-DPO", "base_model:quantized:LuyiCui/DeepSeek-R1-Distill-Qwen-1.5B-DPO", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T17:33:40Z	--- base_model: LuyiCui/DeepSeek-R1-Distill-Qwen-1.5B-DPO datasets: LuyiCui/numina-deepseek-r1-qwen-7b-efficient-test-preference library_name: transformers model_name: DeepSeek-R1-Distill-Qwen-1.5B-DPO tags: - generated_from_trainer - open-r1 - trl - dpo - llama-cpp - gguf-my-repo licence: license --- # Lucy-in-the-Sky/DeepSeek-R1-Distill-Qwen-1.5B-DPO-Q4_K_M-GGUF This model was converted to GGUF format from [`LuyiCui/DeepSeek-R1-Distill-Qwen-1.5B-DPO`](https://huggingface.co/LuyiCui/DeepSeek-R1-Distill-Qwen-1.5B-DPO) 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/LuyiCui/DeepSeek-R1-Distill-Qwen-1.5B-DPO) 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 Lucy-in-the-Sky/DeepSeek-R1-Distill-Qwen-1.5B-DPO-Q4_K_M-GGUF --hf-file deepseek-r1-distill-qwen-1.5b-dpo-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Lucy-in-the-Sky/DeepSeek-R1-Distill-Qwen-1.5B-DPO-Q4_K_M-GGUF --hf-file deepseek-r1-distill-qwen-1.5b-dpo-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Lucy-in-the-Sky/DeepSeek-R1-Distill-Qwen-1.5B-DPO-Q4_K_M-GGUF --hf-file deepseek-r1-distill-qwen-1.5b-dpo-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Lucy-in-the-Sky/DeepSeek-R1-Distill-Qwen-1.5B-DPO-Q4_K_M-GGUF --hf-file deepseek-r1-distill-qwen-1.5b-dpo-q4_k_m.gguf -c 2048 ```
mlfoundations-dev/no_pipeline_code_3k	mlfoundations-dev	2025-05-03T17:33:06Z	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	2025-05-03T15:42:00Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: no_pipeline_code_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. --> # no_pipeline_code_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/no_pipeline_code_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 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 3.1.0 - Tokenizers 0.20.3
memevis/walk11	memevis	2025-05-03T17:31:45Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T17:30:54Z	--- 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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mradermacher/Jose_AI-GGUF	mradermacher	2025-05-03T17:31:20Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:idkwhoamilol/Jose_AI", "base_model:quantized:idkwhoamilol/Jose_AI", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-03T17:29:33Z	--- base_model: idkwhoamilol/Jose_AI language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/idkwhoamilol/Jose_AI <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Jose_AI-GGUF/resolve/main/Jose_AI.Q2_K.gguf) \| Q2_K \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jose_AI-GGUF/resolve/main/Jose_AI.Q3_K_S.gguf) \| Q3_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jose_AI-GGUF/resolve/main/Jose_AI.Q3_K_M.gguf) \| Q3_K_M \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Jose_AI-GGUF/resolve/main/Jose_AI.IQ4_XS.gguf) \| IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jose_AI-GGUF/resolve/main/Jose_AI.Q4_K_S.gguf) \| Q4_K_S \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Jose_AI-GGUF/resolve/main/Jose_AI.Q3_K_L.gguf) \| Q3_K_L \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jose_AI-GGUF/resolve/main/Jose_AI.Q4_K_M.gguf) \| Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Jose_AI-GGUF/resolve/main/Jose_AI.Q5_K_S.gguf) \| Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jose_AI-GGUF/resolve/main/Jose_AI.Q5_K_M.gguf) \| Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jose_AI-GGUF/resolve/main/Jose_AI.Q6_K.gguf) \| Q6_K \| 0.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Jose_AI-GGUF/resolve/main/Jose_AI.Q8_0.gguf) \| Q8_0 \| 0.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Jose_AI-GGUF/resolve/main/Jose_AI.f16.gguf) \| f16 \| 0.4 \| 16 bpw, overkill \| 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 -->
mradermacher/Phi-4-reasoning-GGUF	mradermacher	2025-05-03T17:31:15Z	0	0	transformers	[ "transformers", "gguf", "phi", "nlp", "math", "code", "chat", "conversational", "reasoning", "en", "base_model:microsoft/Phi-4-reasoning", "base_model:quantized:microsoft/Phi-4-reasoning", "license:mit", "endpoints_compatible", "region:us" ]	null	2025-05-02T18:14:22Z	--- base_model: microsoft/Phi-4-reasoning language: - en library_name: transformers license: mit license_link: https://huggingface.co/microsoft/Phi-4-reasoning/resolve/main/LICENSE quantized_by: mradermacher tags: - phi - nlp - math - code - chat - conversational - reasoning --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/microsoft/Phi-4-reasoning <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Phi-4-reasoning-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/Phi-4-reasoning-GGUF/resolve/main/Phi-4-reasoning.Q2_K.gguf) \| Q2_K \| 5.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-GGUF/resolve/main/Phi-4-reasoning.Q3_K_S.gguf) \| Q3_K_S \| 6.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-GGUF/resolve/main/Phi-4-reasoning.Q3_K_M.gguf) \| Q3_K_M \| 7.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-GGUF/resolve/main/Phi-4-reasoning.Q3_K_L.gguf) \| Q3_K_L \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-GGUF/resolve/main/Phi-4-reasoning.IQ4_XS.gguf) \| IQ4_XS \| 8.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-GGUF/resolve/main/Phi-4-reasoning.Q4_K_S.gguf) \| Q4_K_S \| 8.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-GGUF/resolve/main/Phi-4-reasoning.Q4_K_M.gguf) \| Q4_K_M \| 9.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-GGUF/resolve/main/Phi-4-reasoning.Q5_K_S.gguf) \| Q5_K_S \| 10.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-GGUF/resolve/main/Phi-4-reasoning.Q5_K_M.gguf) \| Q5_K_M \| 10.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-GGUF/resolve/main/Phi-4-reasoning.Q6_K.gguf) \| Q6_K \| 12.1 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-GGUF/resolve/main/Phi-4-reasoning.Q8_0.gguf) \| Q8_0 \| 15.7 \| 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 -->

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