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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-23 18:28:48	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 573 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-23 18:28:01	card stringlengths 11 1.01M
onnxmodelzoo/nfnet_l0_Opset17	onnxmodelzoo	2025-09-22T05:18:13Z	0	0	null	[ "onnx", "Computer_Vision", "en", "license:apache-2.0", "region:us" ]	null	2025-09-22T05:17:57Z	--- language: en license: apache-2.0 model_name: nfnet_l0_Opset17.onnx tags: - Computer_Vision ---
onnxmodelzoo/nf_resnet50_Opset17	onnxmodelzoo	2025-09-22T05:17:44Z	0	0	null	[ "onnx", "Computer_Vision", "en", "license:apache-2.0", "region:us" ]	null	2025-09-22T05:17:32Z	--- language: en license: apache-2.0 model_name: nf_resnet50_Opset17.onnx tags: - Computer_Vision ---
hoan17/saving_LAVilas50e1_100	hoan17	2025-09-22T05:16:23Z	0	0	diffusers	[ "diffusers", "safetensors", "trl", "o2o", "reinforcement-learning", "text-to-image", "stable-diffusion", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-09-22T05:15:57Z	--- license: apache-2.0 tags: - trl - o2o - diffusers - reinforcement-learning - text-to-image - stable-diffusion --- # TRL O2O Model This is a diffusion model that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for image generation conditioned with text.
onnxmodelzoo/mobilevitv2_125_Opset18	onnxmodelzoo	2025-09-22T05:16:10Z	0	0	null	[ "onnx", "Computer_Vision", "en", "license:apache-2.0", "region:us" ]	null	2025-09-22T05:16:05Z	--- language: en license: apache-2.0 model_name: mobilevitv2_125_Opset18.onnx tags: - Computer_Vision ---
onnxmodelzoo/mobilevitv2_100_Opset18	onnxmodelzoo	2025-09-22T05:15:55Z	0	0	null	[ "onnx", "Computer_Vision", "en", "license:apache-2.0", "region:us" ]	null	2025-09-22T05:15:51Z	--- language: en license: apache-2.0 model_name: mobilevitv2_100_Opset18.onnx tags: - Computer_Vision ---
onnxmodelzoo/mobilevitv2_100_Opset16	onnxmodelzoo	2025-09-22T05:15:46Z	0	0	null	[ "onnx", "Computer_Vision", "en", "license:apache-2.0", "region:us" ]	null	2025-09-22T05:15:42Z	--- language: en license: apache-2.0 model_name: mobilevitv2_100_Opset16.onnx tags: - Computer_Vision ---
onnxmodelzoo/mobilevitv2_075_Opset18	onnxmodelzoo	2025-09-22T05:15:42Z	0	0	null	[ "onnx", "Computer_Vision", "en", "license:apache-2.0", "region:us" ]	null	2025-09-22T05:15:38Z	--- language: en license: apache-2.0 model_name: mobilevitv2_075_Opset18.onnx tags: - Computer_Vision ---
onnxmodelzoo/mobilevitv2_075_Opset16	onnxmodelzoo	2025-09-22T05:15:33Z	0	0	null	[ "onnx", "Computer_Vision", "en", "license:apache-2.0", "region:us" ]	null	2025-09-22T05:15:30Z	--- language: en license: apache-2.0 model_name: mobilevitv2_075_Opset16.onnx tags: - Computer_Vision ---
onnxmodelzoo/mobilevitv2_050_Opset16	onnxmodelzoo	2025-09-22T05:15:22Z	0	0	null	[ "onnx", "Computer_Vision", "en", "license:apache-2.0", "region:us" ]	null	2025-09-22T05:15:18Z	--- language: en license: apache-2.0 model_name: mobilevitv2_050_Opset16.onnx tags: - Computer_Vision ---
onnxmodelzoo/mobilevit_xxs_Opset18	onnxmodelzoo	2025-09-22T05:15:18Z	0	0	null	[ "onnx", "Computer_Vision", "en", "license:apache-2.0", "region:us" ]	null	2025-09-22T05:15:14Z	--- language: en license: apache-2.0 model_name: mobilevit_xxs_Opset18.onnx tags: - Computer_Vision ---
ChenWu98/numina_qwen_2.5_7b_sft_teachers_no_reasoning_source_condition_2048_0.5	ChenWu98	2025-09-22T05:14:53Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-7B", "base_model:finetune:Qwen/Qwen2.5-7B", "endpoints_compatible", "region:us" ]	null	2025-09-22T04:42:56Z	--- base_model: Qwen/Qwen2.5-7B library_name: transformers model_name: numina_qwen_2.5_7b_sft_teachers_no_reasoning_source_condition_2048_0.5 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for numina_qwen_2.5_7b_sft_teachers_no_reasoning_source_condition_2048_0.5 This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B). 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"]) ``` ## 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/chenwu/huggingface/runs/34mfen5r) This model was trained with SFT. ### Framework versions - TRL: 0.19.1 - Transformers: 4.51.1 - Pytorch: 2.7.0 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
mradermacher/llama70B-3.1-40layer-GGUF	mradermacher	2025-09-22T05:00:11Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:japawblob/llama70B-3.1-40layer", "base_model:quantized:japawblob/llama70B-3.1-40layer", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-22T00:28:15Z	--- base_model: japawblob/llama70B-3.1-40layer language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/japawblob/llama70B-3.1-40layer <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#llama70B-3.1-40layer-GGUF).* 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/llama70B-3.1-40layer-GGUF/resolve/main/llama70B-3.1-40layer.Q2_K.gguf) \| Q2_K \| 13.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama70B-3.1-40layer-GGUF/resolve/main/llama70B-3.1-40layer.Q3_K_S.gguf) \| Q3_K_S \| 16.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama70B-3.1-40layer-GGUF/resolve/main/llama70B-3.1-40layer.Q3_K_M.gguf) \| Q3_K_M \| 17.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/llama70B-3.1-40layer-GGUF/resolve/main/llama70B-3.1-40layer.Q3_K_L.gguf) \| Q3_K_L \| 19.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama70B-3.1-40layer-GGUF/resolve/main/llama70B-3.1-40layer.IQ4_XS.gguf) \| IQ4_XS \| 19.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama70B-3.1-40layer-GGUF/resolve/main/llama70B-3.1-40layer.Q4_K_S.gguf) \| Q4_K_S \| 21.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llama70B-3.1-40layer-GGUF/resolve/main/llama70B-3.1-40layer.Q4_K_M.gguf) \| Q4_K_M \| 22.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llama70B-3.1-40layer-GGUF/resolve/main/llama70B-3.1-40layer.Q5_K_S.gguf) \| Q5_K_S \| 25.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama70B-3.1-40layer-GGUF/resolve/main/llama70B-3.1-40layer.Q5_K_M.gguf) \| Q5_K_M \| 25.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama70B-3.1-40layer-GGUF/resolve/main/llama70B-3.1-40layer.Q6_K.gguf) \| Q6_K \| 29.9 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/llama70B-3.1-40layer-GGUF/resolve/main/llama70B-3.1-40layer.Q8_0.gguf) \| Q8_0 \| 38.7 \| 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 -->
luckeciano/Qwen-2.5-7B-GRPO-Adam-FisherMaskToken-1e-4-HessianMaskToken-0.01-CAPOOnly-v2_4646	luckeciano	2025-09-22T04:53:03Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:DigitalLearningGmbH/MATH-lighteval", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-Math-7B", "base_model:finetune:Qwen/Qwen2.5-Math-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T00:02:37Z	--- base_model: Qwen/Qwen2.5-Math-7B datasets: DigitalLearningGmbH/MATH-lighteval library_name: transformers model_name: Qwen-2.5-7B-GRPO-Adam-FisherMaskToken-1e-4-HessianMaskToken-0.01-CAPOOnly-v2_4646 tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for Qwen-2.5-7B-GRPO-Adam-FisherMaskToken-1e-4-HessianMaskToken-0.01-CAPOOnly-v2_4646 This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-GRPO-Adam-FisherMaskToken-1e-4-HessianMaskToken-0.01-CAPOOnly-v2_4646", 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/max-ent-llms/PolicyGradientStability/runs/muqtkocz) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.5.1 - Datasets: 3.4.1 - Tokenizers: 0.21.2 ## 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}} } ```
a3ilab-llm-uncertainty/new_2560_3_epoch_xlam_apigen	a3ilab-llm-uncertainty	2025-09-22T04:27:11Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "base_model:adapter:Salesforce/Llama-xLAM-2-8b-fc-r", "lora", "sft", "transformers", "trl", "text-generation", "conversational", "arxiv:1910.09700", "base_model:Salesforce/Llama-xLAM-2-8b-fc-r", "region:us" ]	text-generation	2025-09-22T04:12:07Z	--- base_model: Salesforce/Llama-xLAM-2-8b-fc-r library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:Salesforce/Llama-xLAM-2-8b-fc-r - lora - sft - transformers - trl --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.17.1
rl-rag/qwen3-8B-v20250915_sampled_ablations	rl-rag	2025-09-22T04:13:27Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen3-8B", "base_model:finetune:Qwen/Qwen3-8B", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T04:12:25Z	--- library_name: transformers license: other base_model: Qwen/Qwen3-8B tags: - llama-factory - full - generated_from_trainer model-index: - name: qwen3-8B-v20250915_sampled_ablations 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. --> # qwen3-8B-v20250915_sampled_ablations This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) on the rl-rag/v20250915_sampled_ablations dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.52.4 - Pytorch 2.8.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
Yuchiwang02/DelaySentinel	Yuchiwang02	2025-09-22T04:11:51Z	4	0	null	[ "safetensors", "llama", "text-classification", "en", "base_model:meta-llama/Llama-3.2-1B", "base_model:finetune:meta-llama/Llama-3.2-1B", "license:apache-2.0", "region:us" ]	text-classification	2025-09-20T03:09:59Z	--- license: apache-2.0 language: - en base_model: - meta-llama/Llama-3.2-1B pipeline_tag: text-classification --- # Model Card for DelaySentinel: AI-Powered Logistics Delay Prediction ## Model Details ### Model Description - Developed by: Yuchi Wang - Model type: Instruction fine-tuned large language model (LLM) for binary classification - Language(s): English (structured prompts with business/logistics features) - License: Apache-2.0 - Finetuned from model: `meta-llama/Llama-3.2-1B-Instruct` This model, DelaySentinel, was fine-tuned to predict whether a logistics order will be delayed (`1`) or not (`0`) before shipment, using structured order-level features. The project demonstrates how instruction fine-tuning of LLMs can be applied to supply chain risk management. ### Model Sources - Repository: [Hugging Face Model Repo] - Demo: Hugging Face Gradio Space (interactive CSV/Excel upload) --- ## Uses ### Direct Use - Pre-shipment logistics delay prediction - Business analytics demos for supply chain risk management - Educational showcase of LLM instruction fine-tuning on structured business data ### Downstream Use - Adaptation to other supply chain KPIs (e.g., demand forecasting, lead-time prediction) - Further fine-tuning on proprietary logistics datasets ### Out-of-Scope Use - Not intended for sensitive decision-making in live operations without validation - Not suitable for medical, legal, or financial advisory --- ## Bias, Risks, and Limitations - Dataset comes from Kaggle (synthetic/aggregated logistics data), so real-world generalization may be limited. - Model outputs are strictly `0`/`1` and do not provide uncertainty estimates unless re-trained for probabilities. - Risk of data drift if deployed in real supply chains with different carriers/regions. Recommendations: Users should validate predictions against recent operational data before deployment in practice. --- ## How to Get Started with the Model ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("yuchi/DelaySentinel") tokenizer = AutoTokenizer.from_pretrained("yuchi/DelaySentinel") system = "You are a supply-chain analyst. Output only 0 or 1: 1=Delay, 0=Not delay." user = "order_id: 123\norigin_region: OH\ndest_region: CA\ncarrier: A1\nservice_level: ground\nweight_kg: 10.5\ndistance_km: 3500\nholiday_flag: 0" prompt = f"<\|system\|>{system}\n<\|user\|>{user}\n<\|assistant\|>" out = model.generate(**tokenizer(prompt, return_tensors="pt"), max_new_tokens=2) print(tokenizer.decode(out[0], skip_special_tokens=True).split("<\|assistant\|>")[-1].strip())
lemonhat/Qwen2.5-7B-Instruct-SEvolve3_re_21k_tag5_progress_processed	lemonhat	2025-09-22T04:08:28Z	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:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T03:55:52Z	--- library_name: transformers license: other base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: SEvolve3_re_21k_tag5_progress_processed 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. --> # SEvolve3_re_21k_tag5_progress_processed This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the SEvolve3_re_21k_tag5_progress_processed dataset. It achieves the following results on the evaluation set: - Loss: 0.2325 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - total_eval_batch_size: 8 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.2647 \| 0.8230 \| 300 \| 0.2442 \| \| 0.2147 \| 1.6447 \| 600 \| 0.2330 \| ### Framework versions - Transformers 4.51.0 - Pytorch 2.8.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
jonaji/blockassist	jonaji	2025-09-22T03:55:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "prowling waddling chinchilla", "arxiv:2504.07091", "region:us" ]	null	2025-09-12T15:25:40Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - prowling waddling chinchilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
anhtuan15082023/gemma-3n-vneid-merged	anhtuan15082023	2025-09-22T03:48:05Z	0	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "vietnamese", "gemma", "fine-tuned", "unsloth", "lora", "conversational", "vi", "base_model:google/gemma-2-2b", "base_model:adapter:google/gemma-2-2b", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T03:31:45Z	--- language: vi license: apache-2.0 base_model: google/gemma-2-2b tags: - vietnamese - gemma - fine-tuned - unsloth - lora - text-generation library_name: transformers pipeline_tag: text-generation model_type: gemma --- # gemma-3n-vneid-merged 🇻🇳 Vietnamese Fine-tuned Gemma Model This is a Vietnamese fine-tuned version of Google's Gemma 2B model using Unsloth and LoRA adapters, optimized for Vietnamese text generation. ## 📊 Model Details - Base Model: [google/gemma-2-2b](https://huggingface.co/google/gemma-2-2b) - Language: Vietnamese (vi) - Fine-tuning Method: LoRA (Low-Rank Adaptation) - Framework: Unsloth - Model Type: Causal Language Model - License: Apache 2.0 ## 🚀 Quick Start ### Using Transformers ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch # Load model and tokenizer model_name = "anhtuan15082023/gemma-3n-vneid-merged" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True ) # Generate Vietnamese text def generate_vietnamese_text(prompt, max_length=100): inputs = tokenizer(prompt, return_tensors="pt") with torch.no_grad(): outputs = model.generate( inputs, max_length=max_length, temperature=0.7, do_sample=True, top_p=0.9, pad_token_id=tokenizer.eos_token_id ) response = tokenizer.decode(outputs[0], skip_special_tokens=True) return response[len(prompt):].strip() # Example usage prompt = "Xin chào, tôi là" result = generate_vietnamese_text(prompt) print(f"Input: {prompt}") print(f"Output: {result}") ``` ### Using Inference API ```python import requests API_URL = "https://api-inference.huggingface.co/models/anhtuan15082023/gemma-3n-vneid-merged" headers = {"Authorization": f"Bearer {YOUR_HF_TOKEN}"} def query(payload): response = requests.post(API_URL, headers=headers, json=payload) return response.json() # Generate text output = query({ "inputs": "Việt Nam là", "parameters": { "max_length": 100, "temperature": 0.7 } }) print(output) ``` ## 🎯 Use Cases - Vietnamese text completion - Creative writing in Vietnamese - Chatbot responses in Vietnamese - Content generation for Vietnamese applications ## ⚙️ Training Details - Dataset: Vietnamese text corpus - Training Framework: Unsloth (optimized training) - Fine-tuning Method: LoRA adapters merged into base model - Base Model**: Google Gemma 2B ## 🏷️ Model Tags - Vietnamese language model - Text generation - Fine-tuned Gemma - LoRA adaptation ## 📜 License This model inherits the Apache 2.0 license from the base Gemma model. ## 🤝 Citation If you use this model, please consider citing: ```bibtex @model{vietnamese-gemma-finetuned, title={Vietnamese Fine-tuned Gemma Model}, author={anhtuan15082023}, year={2024}, url={https://huggingface.co/anhtuan15082023/gemma-3n-vneid-merged} } ``` ## 📞 Contact For questions or issues, please open an issue on the model's repository page.
pandoradox/qwen2.5-7b-instruct_stressstrain_200	pandoradox	2025-09-22T03:40:11Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:Qwen/Qwen2.5-7B-Instruct", "grpo", "lora", "transformers", "trl", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-7B-Instruct", "region:us" ]	null	2025-09-22T03:40:09Z	--- base_model: Qwen/Qwen2.5-7B-Instruct library_name: peft tags: - base_model:adapter:Qwen/Qwen2.5-7B-Instruct - grpo - lora - transformers - trl --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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] ### Framework versions - PEFT 0.17.1
aamijar/ReplaceME-Mistral-7B-Instruct-v0.3-lora-r8-sst2-epochs3	aamijar	2025-09-22T03:27:56Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-22T03:27:54Z	--- 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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ChenWu98/numina_qwen_2.5_3b_sft_teachers_no_reasoning_2048_0.5	ChenWu98	2025-09-22T03:22:26Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-3B", "base_model:finetune:Qwen/Qwen2.5-3B", "endpoints_compatible", "region:us" ]	null	2025-09-22T03:18:47Z	--- base_model: Qwen/Qwen2.5-3B library_name: transformers model_name: numina_qwen_2.5_3b_sft_teachers_no_reasoning_2048_0.5 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for numina_qwen_2.5_3b_sft_teachers_no_reasoning_2048_0.5 This model is a fine-tuned version of [Qwen/Qwen2.5-3B](https://huggingface.co/Qwen/Qwen2.5-3B). 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"]) ``` ## 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/chenwu/huggingface/runs/fnieqw78) This model was trained with SFT. ### Framework versions - TRL: 0.19.1 - Transformers: 4.51.1 - Pytorch: 2.7.0 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
yuuki367/llama-3-8B-chat-lawyer-webtest2	yuuki367	2025-09-22T03:07:05Z	7	0	transformers	[ "transformers", "safetensors", "gguf", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-16T03:29:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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chantomkit/qwen3-risk-mnr-education	chantomkit	2025-09-22T02:51:19Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "qwen3", "sentence-similarity", "feature-extraction", "dense", "generated_from_trainer", "dataset_size:31656", "loss:MultipleNegativesSymmetricRankingLoss", "arxiv:1908.10084", "base_model:Qwen/Qwen3-Embedding-0.6B", "base_model:finetune:Qwen/Qwen3-Embedding-0.6B", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-09-22T02:51:00Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:31656 - loss:MultipleNegativesSymmetricRankingLoss base_model: Qwen/Qwen3-Embedding-0.6B widget: - source_sentence: 'Email to Classmate: "Yo, can you send me your calculus homework answers? I’ll just tweak the numbers so it doesn’t look obvious. My mom’s on my case about grades, and I didn’t have time to do it myself."' sentences: - 'Teacher’s Assignment Prompt: "Final Paper: ''Critique a modern innovation.'' No page limit, citation rules, or submission checks. Be imaginative!"' - 'Discussion Board Post (Class Forum): "For the AI-Free essay due tomorrow, I just had ChatGPT draft my thesis and main arguments. Saved me hours—anyone else try this?"' - "Lab Report Submission: \n\"Results section fully written by an AI science\ \ assistant. It claims our chemistry experiment produced 200% yield, which is\ \ impossible, but I’m too tired to fix it. Maybe the TA won’t read closely.\"" - source_sentence: 'Parent email to teacher: "Why did Mia get a zero on her history paper? She worked so hard with that new homework app all weekend! We paid good money for EssayGenius Premium!"' sentences: - 'Peer review comment: "Your essay on ‘leadership’ feels robotic. Did you use an AI? Mine did the same thing because the rubric didn’t ask for real-life examples."' - 'Parent email to teacher: "My daughter’s AI writing assistant developed her science fair hypothesis and experimental design. Since the tool is school-approved, we assumed this was acceptable. Please confirm."' - 'Group Chat Message (Student): "Bro, just use ChatGPT for the calculus project. It doesn’t just solve the problems—it explains the concepts in the ‘show your work’ section. Zero brainpower needed."' - source_sentence: 'Academic Forum Comment: "PM me for a link to a bot that writes flawless philosophy essays. It’s free, and teachers never check the sources as long as the grammar’s good."' sentences: - 'Discussion Board Post (Class Forum): "For the AI-Free essay due tomorrow, I just had ChatGPT draft my thesis and main arguments. Saved me hours—anyone else try this?"' - 'Syllabus note: "Final paper: Compare two historical periods. No need to cite specific events or scholars—focus on abstract concepts."' - 'Reddit Post in Student Forum: "Pro tip: Use DALL-E to ‘hand-draw’ diagrams for your biology project. Teachers can’t tell the difference, and it saves hours."' - source_sentence: 'Creative writing assignment: [Submits short story containing verbatim passages from Sudowrite’s narrative generator] "This is based on my cousin’s camping trip! Any similarities to existing stories are coincidental."' sentences: - 'Assignment Submission (Essay Intro): "In my analysis of Shakespeare''s Hamlet, I found that the play''s exploration of existential dread mirrors modern anxieties. This essay was generated using ChatGPT to save time, but I edited a few sentences to make it sound more like me."' - 'Submitted essay introduction with no AI disclosure: "The symbolism in To Kill a Mockingbird reflects systemic racism through the trial of Tom Robinson. This analysis demonstrates how Lee uses courtroom dynamics as microcosms of societal prejudice." (Essay contains verbatim ChatGPT phrasing from public query logs)' - 'Peer Review Comment: "Your thesis statement is weak. I used an AI paraphrasing tool to rewrite mine—here’s the link. Just paste yours in, and it’ll spit out something the teacher will love. No need to cite it."' - source_sentence: 'Peer Review Feedback: "Your analysis feels a bit generic. You should try HyperWrite’s ‘academic tone’ feature. It makes your writing sound smarter without changing the content."' sentences: - 'Creative Writing Assignment: [Submission includes a poem identical to one published on a poetry blog, with only the title and line breaks altered. No attribution provided.]' - 'Class Presentation Slide: "Special thanks to ‘StudyGenius AI’ for drafting this presentation! [spoken verbally during the talk, omitted from all written citations]"' - 'Discussion board post: "Hey guys, for the AI-Free history essay, just run your draft through QuillBot first—it totally bypasses the plagiarism checkers they use to detect AI."' pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine model-index: - name: SentenceTransformer based on Qwen/Qwen3-Embedding-0.6B results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: Unknown type: unknown metrics: - type: pearson_cosine value: 0.628976409054562 name: Pearson Cosine - type: spearman_cosine value: 0.6217481040134443 name: Spearman Cosine --- # SentenceTransformer based on Qwen/Qwen3-Embedding-0.6B This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B). 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: [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) <!-- at revision c54f2e6e80b2d7b7de06f51cec4959f6b3e03418 --> - Maximum Sequence Length: 32768 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': 32768, 'do_lower_case': False, 'architecture': 'Qwen3Model'}) (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, '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': True, '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("chantomkit/qwen3-risk-mnr-education") # Run inference queries = [ "Peer Review Feedback: \"Your analysis feels a bit generic. You should try HyperWrite\u2019s \u2018academic tone\u2019 feature. It makes your writing sound smarter without changing the content.\"", ] documents = [ 'Discussion board post: "Hey guys, for the AI-Free history essay, just run your draft through QuillBot first—it totally bypasses the plagiarism checkers they use to detect AI."', 'Creative Writing Assignment: [Submission includes a poem identical to one published on a poetry blog, with only the title and line breaks altered. No attribution provided.]', 'Class Presentation Slide: "Special thanks to ‘StudyGenius AI’ for drafting this presentation! [spoken verbally during the talk, omitted from all written citations]"', ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) print(query_embeddings.shape, document_embeddings.shape) # [1, 1024] [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) # tensor([[0.9552, 0.9264, 0.9305]]) ``` <!-- ### 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 #### Semantic Similarity * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| pearson_cosine \| 0.629 \| \| spearman_cosine \| 0.6217 \| <!-- ## 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: 31,656 training samples * Columns: <code>anchor</code> and <code>positive</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| \|:--------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 15 tokens</li><li>mean: 44.32 tokens</li><li>max: 75 tokens</li></ul> \| <ul><li>min: 15 tokens</li><li>mean: 44.12 tokens</li><li>max: 75 tokens</li></ul> \| * Samples: \| anchor \| positive \| \|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>Assignment Submission Comment (Student): "Attached is my history project on the Civil War. I used ThinkBot to generate the thesis and all the analysis sections. It even added counterarguments automatically. Hope that’s cool!"</code> \| <code>Discussion board comment: "For the group project, I came up with the analysis framework using this cool new brainstorming app. Don’t worry about sources—it’s all original! Let’s just split the slides evenly." (No links to AI-generated mind maps used for framework development)</code> \| \| <code>Peer Review Comment (Online Platform): <br>"Your thesis about symbolism in The Great Gatsby is identical to the one my AI chatbot generated. Maybe tweak a few words so we don’t both get flagged?"</code> \| <code>Assignment Submission Intro: "The French Revolution was a pivotal moment in European history, fundamentally reshaping socio-political structures. [Generated by DeepL Write, edited for clarity]." (No attribution included in bibliography.)</code> \| \| <code>Parent Message to School Forum: "My daughter’s AI-generated book report got flagged for inaccuracies. Why does it matter? The tool is expensive and ‘educational.’ Schools should trust technology over nitpicking teachers."</code> \| <code>Peer Review Feedback: "Your analysis feels a bit generic. You should try HyperWrite’s ‘academic tone’ feature. It makes your writing sound smarter without changing the content."</code> \| * Loss: [<code>MultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim", "gather_across_devices": false } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 8 - `per_device_eval_batch_size`: 8 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 3.0 - `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} - `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} - `parallelism_config`: None - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `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 - `hub_revision`: None - `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 - `liger_kernel_config`: None - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {} </details> ### Training Logs \| Epoch \| Step \| Training Loss \| spearman_cosine \| \|:------:\|:-----:\|:-------------:\|:---------------:\| \| -1 \| -1 \| - \| 0.3055 \| \| 0.1264 \| 500 \| 1.3976 \| - \| \| 0.2527 \| 1000 \| 0.9666 \| - \| \| 0.3791 \| 1500 \| 0.7903 \| - \| \| 0.5054 \| 2000 \| 0.6094 \| - \| \| 0.6318 \| 2500 \| 0.5508 \| - \| \| 0.7582 \| 3000 \| 0.4897 \| - \| \| 0.8845 \| 3500 \| 0.415 \| - \| \| 1.0109 \| 4000 \| 0.3774 \| - \| \| 1.1372 \| 4500 \| 0.3221 \| - \| \| 1.2636 \| 5000 \| 0.3026 \| - \| \| 1.3899 \| 5500 \| 0.2685 \| - \| \| 1.5163 \| 6000 \| 0.272 \| - \| \| 1.6427 \| 6500 \| 0.2479 \| - \| \| 1.7690 \| 7000 \| 0.2277 \| - \| \| 1.8954 \| 7500 \| 0.2339 \| - \| \| 2.0217 \| 8000 \| 0.1832 \| - \| \| 2.1481 \| 8500 \| 0.1759 \| - \| \| 2.2745 \| 9000 \| 0.1814 \| - \| \| 2.4008 \| 9500 \| 0.1625 \| - \| \| 2.5272 \| 10000 \| 0.1574 \| - \| \| 2.6535 \| 10500 \| 0.145 \| - \| \| 2.7799 \| 11000 \| 0.1526 \| - \| \| 2.9062 \| 11500 \| 0.1517 \| - \| \| -1 \| -1 \| - \| 0.6217 \| ### Framework Versions - Python: 3.10.18 - Sentence Transformers: 5.1.0 - Transformers: 4.56.2 - PyTorch: 2.8.0+cu128 - Accelerate: 1.10.1 - Datasets: 4.1.1 - Tokenizers: 0.22.1 ## 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", } ``` <!-- ## 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. -->
tobykim/results_bs	tobykim	2025-09-22T02:49:42Z	0	0	transformers	[ "transformers", "safetensors", "electra", "text-classification", "generated_from_trainer", "base_model:monologg/koelectra-base-v3-discriminator", "base_model:finetune:monologg/koelectra-base-v3-discriminator", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-09-22T02:49:19Z	--- library_name: transformers license: apache-2.0 base_model: monologg/koelectra-base-v3-discriminator tags: - generated_from_trainer model-index: - name: results_bs 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_bs This model is a fine-tuned version of [monologg/koelectra-base-v3-discriminator](https://huggingface.co/monologg/koelectra-base-v3-discriminator) 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-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Framework versions - Transformers 4.56.1 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.22.0
lucadellalib/focalcodec_25hz	lucadellalib	2025-09-22T02:25:44Z	29	1	pytorch	[ "pytorch", "safetensors", "audio-to-audio", "dataset:mythicinfinity/libritts", "arxiv:2203.11926", "arxiv:2502.04465", "arxiv:2509.16195", "base_model:microsoft/wavlm-large", "base_model:finetune:microsoft/wavlm-large", "license:apache-2.0", "region:us" ]	audio-to-audio	2025-02-11T04:12:35Z	--- license: apache-2.0 base_model: - microsoft/wavlm-large pipeline_tag: audio-to-audio datasets: - mythicinfinity/libritts library_name: pytorch --- # ⚡ FocalCodec A low-bitrate single-codebook 16 / 24 kHz speech codec based on [focal modulation](https://arxiv.org/abs/2203.11926). This repository contains the 25 Hz checkpoint trained on LibriTTS 960, as described in the preprints. - 📜 Preprints: - [FocalCodec: Low-Bitrate Speech Coding via Focal Modulation Networks](https://arxiv.org/abs/2502.04465) - [FocalCodec-Stream: Streaming Low-Bitrate Speech Coding via Causal Distillation](https://arxiv.org/abs/2509.16195) - 🌐 Project Page: https://lucadellalib.github.io/focalcodec-web/ - 💾 GitHub: https://github.com/lucadellalib/focalcodec <img src="focalcodec.png" width="700"> --------------------------------------------------------------------------------------------------------- ## ▶️ Quickstart See the readme at: https://github.com/lucadellalib/focalcodec --------------------------------------------------------------------------------------------------------- ## @ Citing ``` @article{dellalibera2025focalcodec, title = {{FocalCodec}: Low-Bitrate Speech Coding via Focal Modulation Networks}, author = {Luca {Della Libera} and Francesco Paissan and Cem Subakan and Mirco Ravanelli}, journal = {arXiv preprint arXiv:2502.04465}, year = {2025}, } @article{dellalibera2025focalcodecstream, title = {{FocalCodec-Stream}: Streaming Low-Bitrate Speech Coding via Causal Distillation}, author = {Luca {Della Libera} and Cem Subakan and Mirco Ravanelli}, journal = {arXiv preprint arXiv:2509.16195}, year = {2025}, } ``` --------------------------------------------------------------------------------------------------------- ## 📧 Contact [[email protected]](mailto:[email protected]) ---------------------------------------------------------------------------------------------------------
mestersop3/blockassist	mestersop3	2025-09-22T02:18:25Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "cunning tangled robin", "arxiv:2504.07091", "region:us" ]	null	2025-09-22T02:00:02Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - cunning tangled robin --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Pakorn2112/whisper-large-v3-turbo-Hmong-asr	Pakorn2112	2025-09-22T02:14:17Z	0	0	null	[ "safetensors", "whisper", "automatic-speech-recognition", "hmn", "dataset:mozilla-foundation/common_voice_17_0", "base_model:openai/whisper-large-v3-turbo", "base_model:finetune:openai/whisper-large-v3-turbo", "license:apache-2.0", "region:us" ]	automatic-speech-recognition	2025-09-21T17:54:14Z	--- license: apache-2.0 datasets: - mozilla-foundation/common_voice_17_0 language: - hmn metrics: - wer base_model: - openai/whisper-large-v3-turbo new_version: openai/whisper-large-v3-turbo pipeline_tag: automatic-speech-recognition --- Whisper Large V3 Turbo - Hmong ASR Fine-tuned OpenAI Whisper Large V3 Turbo สำหรับการรู้จำเสียงพูด (ASR) ภาษาม้ง (Hmong) โดยใช้ชุดข้อมูล Mozilla Common Voice 17.0 📌 รายละเอียดโมเดล Base model: openai/whisper-large-v3-turbo Language: Hmong (hmn) Dataset: mozilla-foundation/common_voice_17_0 Metric: WER (Word Error Rate) License: Apache-2.0 🚀 วิธีใช้งาน 1. ใช้งานผ่าน 🤗 Transformers ```python from transformers import pipeline transcriber = pipeline( "automatic-speech-recognition", model="Pakorn2112/whisper-large-v3-turbo-Hmong-asr" ) result = transcriber("hmong_sample.wav") print(result["text"]) ``` 2. ใช้งานผ่าน Gradio Demo ```python import gradio as gr from transformers import pipeline ``` # โหลดโมเดล ```python transcriber = pipeline( "automatic-speech-recognition", model="Pakorn2112/whisper-large-v3-turbo-Hmong-asr" ) ``` # ฟังก์ชันถอดเสียง ```python def transcribe1(audio): return transcriber(audio)["text"] ``` # UI Gradio ```python iface = gr.Interface( fn=transcribe1, inputs=gr.Audio(sources=["upload","microphone"], type="filepath"), outputs="text", title="Whisper Large V3 Turbo - Hmong", description="Demo: Hmong speech recognition fine-tuned from Whisper Large V3 Turbo" ) iface.launch() ``` 🎧 ตัวอย่างผลลัพธ์ (Examples) Input (เสียงพูด) Output (ข้อความที่ถอดได้) ``` 🎤 "Koj nyob li cas lawm os?" "Koj nyob li cas lawm os?" 🎤 "Kuv hu ua Paj Ntaub." "Kuv hu ua Paj Ntaub." 🎤 "Peb mus kawm ntawv nag hmo." "Peb mus kawm ntawv nag hmo." ``` 📊 การประเมินผล โมเดลนี้ถูกประเมินด้วย Word Error Rate (WER) \| global_step \| wer\|eval_loss \| \| :---------- \| :--------------: \| ----------------: \| \| 500 \| 6.712565 \| 0.057878 \| 500 6.712565 0.057878 📌 ค่า WER ที่ได้จะแสดงในหน้าโมเดล Hugging Face (evaluation logs) 📖 Citation ถ้าคุณใช้โมเดลนี้ในงานวิจัย กรุณาอ้างอิงดังนี้: ``` @misc{pakorn2025hmongasr, title = {Whisper Large V3 Turbo - Hmong ASR}, author = {Pakorn2112}, year = {2025}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/Pakorn2112/whisper-large-v3-turbo-Hmong-asr}}, } ``` 📜 License โมเดลนี้เผยแพร่ภายใต้สัญญาอนุญาต Apache License 2.0
emkessle/HW2_finetuned_model	emkessle	2025-09-22T02:14:06Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-09-21T22:18:58Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: HW2_finetuned_model results: [] --- language: en license: mit # HW2_finetuned_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1321 - Accuracy: 0.97 - F1: 0.9700 - Precision: 0.9717 - Recall: 0.97 ## Model description This model wwas used for text classification of the dataset found at huggingface.co/datasets/mrob937/desdep_text_dataset ## 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: 0.0004 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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 \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.1276 \| 1.0 \| 120 \| 0.2365 \| 0.9458 \| 0.9457 \| 0.9511 \| 0.9458 \| \| 0.4081 \| 2.0 \| 240 \| 0.2115 \| 0.9583 \| 0.9583 \| 0.9615 \| 0.9583 \| \| 0.1085 \| 3.0 \| 360 \| 0.1289 \| 0.9708 \| 0.9708 \| 0.9724 \| 0.9708 \| ### Framework versions - Transformers 4.56.1 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.22.0
poolkiltzn/blockassist-bc-vigilant_alert_tuna_1758506490	poolkiltzn	2025-09-22T02:02:49Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "vigilant alert tuna", "arxiv:2504.07091", "region:us" ]	null	2025-09-22T02:02:31Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - vigilant alert tuna --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
rlogh/cheese-texture-classifier-distilbert	rlogh	2025-09-22T01:59:47Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "cheese", "texture", "fine-tuned", "dataset:aslan-ng/cheese-text", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-09-21T22:12:28Z	--- license: mit tags: - text-classification - cheese - texture - distilbert - transformers - fine-tuned datasets: - aslan-ng/cheese-text metrics: - accuracy model-index: - name: Cheese Texture Classifier (DistilBERT) results: - task: type: text-classification name: Cheese Texture Classification dataset: type: aslan-ng/cheese-text name: Cheese Text Dataset metrics: - type: accuracy value: 0.400 name: Test Accuracy --- # Cheese Texture Classifier (DistilBERT) Model Creator: Rumi Loghmani (@rlogh) Original Dataset: aslan-ng/cheese-text (by Aslan Noorghasemi) This model performs 4-class texture classification on cheese descriptions using fine-tuned DistilBERT. ## Model Description - Architecture: DistilBERT-base-uncased fine-tuned for sequence classification - Task: 4-class texture classification (hard, semi-hard, semi-soft, soft) - Input: Cheese description text (up to 512 tokens) - Output: 4-class probability distribution ## Training Details ### Data - Dataset: [aslan-ng/cheese-text](https://huggingface.co/datasets/aslan-ng/cheese-text) (original split: 100 samples) - Train/Val/Test Split: 70/15/15 (stratified) - Text Source: Cheese descriptions from the dataset - Labels: Texture categories (hard, semi-hard, semi-soft, soft) ### Preprocessing - Tokenization: DistilBERT tokenizer with 512 max length - Padding: Max length padding - Truncation: Long descriptions truncated to 512 tokens ### Training Setup - Model: distilbert-base-uncased - Epochs: 10 - Batch Size: 8 (train/val) - Learning Rate: 2e-5 - Warmup Steps: 10 - Weight Decay: 0.01 - Optimizer: AdamW - Scheduler: Linear warmup + linear decay - Mixed Precision: FP16 (if GPU available) - Seed: 42 (for reproducibility) ### Hardware/Compute - Training Device: CPU - Training Time: ~5-10 minutes on GPU - Model Size: ~67M parameters - Memory Usage: ~2-4GB GPU memory ## Performance - Test Accuracy: 0.400 - Test Loss: 1.290 ### Class-wise Performance precision recall f1-score support hard 0.50 0.33 0.40 3 semi-hard 0.29 0.50 0.36 4 semi-soft 0.40 0.50 0.44 4 soft 1.00 0.25 0.40 4 accuracy 0.40 15 macro avg 0.55 0.40 0.40 15 weighted avg 0.55 0.40 0.40 15 ## Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch # Load model and tokenizer model_name = "rlogh/cheese-texture-classifier-distilbert" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) # Example prediction text = "Feta is a crumbly, tangy Greek cheese with a salty bite and creamy undertones." inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512) with torch.no_grad(): outputs = model(inputs) predictions = torch.nn.functional.softmax(outputs.logits, dim=-1) predicted_class = torch.argmax(predictions, dim=-1).item() class_names = ["hard", "semi-hard", "semi-soft", "soft"] print(f"Predicted texture: {class_names[predicted_class]}") ``` ## Class Definitions - Hard: Firm, aged cheeses that are dense and can be grated (e.g., Parmesan, Cheddar) - Semi-hard: Moderately firm cheeses with some flexibility (e.g., Gouda, Swiss) - Semi-soft: Cheeses with some give but maintain shape (e.g., Mozzarella, Blue cheese) - Soft: Creamy, spreadable cheeses (e.g., Brie, Camembert, Cottage cheese) ## Limitations and Ethics ### Limitations - Small Dataset: Trained on only 100 samples, limiting generalization - Text Quality: Performance depends on description quality and consistency - Subjective Labels: Texture classification has inherent subjectivity - Domain Specific: Only applicable to cheese texture classification - Language: English-only model ### Ethical Considerations - Bias: Model may reflect biases in the original dataset - Cultural Context: Cheese descriptions may be culturally specific - Commercial Use: Not intended for commercial cheese production decisions - Accuracy: Should not be used for critical food safety applications ### Recommendations - Use for educational/research purposes only - Validate predictions with domain experts - Consider cultural context when applying to different regions - Retrain with larger, more diverse datasets for production use ## AI Usage Disclosure This model was developed using: - Base Model: DistilBERT (distilbert-base-uncased) - Training Framework: Hugging Face Transformers - Fine-tuning: Standard BERT fine-tuning techniques - The AI acted as a collaborative partner throughout the development process, accelerating the coding workflow and providing helpful guidance. ## Citation Model Citation: ```bibtex @model{rlogh/cheese-texture-classifier-distilbert, title={Cheese Texture Classifier (DistilBERT)}, author={Rumi Loghmani}, year={2024}, url={https://huggingface.co/rlogh/cheese-texture-classifier-distilbert} } ``` Dataset Citation:** ```bibtex @dataset{aslan-ng/cheese-text, title={Cheese Text Dataset}, author={Aslan Noorghasemi}, year={2024}, url={https://huggingface.co/datasets/aslan-ng/cheese-text} } ``` ## License MIT License - See LICENSE file for details.
ft42/CaNA	ft42	2025-09-22T01:58:42Z	0	0	pytorch	[ "pytorch", "medical-imaging", "lung-nodules", "data-augmentation", "context-aware", "segmentation", "monai", "image-segmentation", "license:cc-by-nc-4.0", "region:us" ]	image-segmentation	2025-09-22T01:54:05Z	--- license: cc-by-nc-4.0 tags: - medical-imaging - lung-nodules - data-augmentation - context-aware - segmentation - pytorch - monai library_name: pytorch pipeline_tag: image-segmentation --- # CaNA: Context-Aware Nodule Augmentation ![CaNA Logo](assets/CaNA_logo.png) Organ- and body-guided augmentation of lung nodule masks [![License: CC BY-NC 4.0](https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc/4.0/) [![Docker](https://img.shields.io/badge/Docker-ft42%2Fpins%3Alatest-2496ED?logo=docker)](https://hub.docker.com/r/ft42/pins) [![Python](https://img.shields.io/badge/Python-3.8%2B-3776AB?logo=python)](https://www.python.org/) [![PyTorch](https://img.shields.io/badge/PyTorch-2.8.0-EE4C2C?logo=pytorch)](https://pytorch.org/) [![MONAI](https://img.shields.io/badge/MONAI-1.4.0-76B900)](https://monai.io/) Augmenting nodules with anatomical context. CaNA (Context-Aware Nodule Augmentation) is a specialized medical imaging toolkit that uses organ and body segmentation masks as contextual guidance to augment lung nodule segmentation masks. This approach ensures that augmented nodules remain anatomically plausible within their surrounding lung structures. ## 🎯 Key Features - Context-Aware Augmentation: Uses anatomical context from organ/body segmentation masks - Morphological Operations: Advanced erosion and dilation with anatomical constraints - Dual Processing Modes: Both expansion (150%) and shrinking (75%) capabilities - Docker Integration: Complete containerized workflow with ft42/pins:latest - Comprehensive Logging: Detailed processing statistics and volume analysis - Batch Processing: Handles multiple nodules with JSON dataset configuration ## 🏥 Medical Applications - Data Augmentation: Generate anatomically-constrained variations of lung nodule datasets - Robustness Testing: Evaluate model performance across nodule size variations - Clinical Research: Study nodule growth/shrinkage patterns within anatomical constraints - Model Training: Enhance training datasets with realistic nodule size variations ## 🚀 Quick Start ### Prerequisites - Docker installed on your system - Input data: Lung segmentation masks with nodule annotations - JSON dataset configuration file ### Installation ```bash # Pull the Docker container docker pull ft42/pins:latest # Clone the repository git clone https://github.com/your-repo/CaNA cd CaNA ``` ### Basic Usage #### Nodule Expansion (150%) ```bash # Make script executable chmod +x CaNA_expanded_p150_DLCS24.sh # Run expansion pipeline ./CaNA_expanded_p150_DLCS24.sh ``` #### Nodule Shrinking (75%) ```bash # Make script executable chmod +x CaNA_shrinked_p75_DLCS24.sh # Run shrinking pipeline ./CaNA_shrinked_p75_DLCS24.sh ``` ## 📊 Expected Results ### Processing Output - Augmented Masks: New NIfTI files with modified nodule sizes - Statistics CSV: Detailed volume analysis and processing metrics - Processing Logs: Complete execution logs with timestamps - File Naming: Systematic prefixes (Aug23e150_, Aug23s75_) ### Expected Output Structure ``` demofolder/output/ ├── CaNA_expanded_150_output/ │ ├── Aug23e150_DLCS_0001_seg_sh.nii.gz # 1.47x expansion achieved │ └── Aug23e150_DLCS_0002_seg_sh.nii.gz # 1.35x expansion achieved ├── CaNA_shrinked_75_output/ │ ├── Aug23s75_DLCS_0001_seg_sh.nii.gz # Preserves anatomical constraints │ └── Aug23s75_DLCS_0002_seg_sh.nii.gz # Shape-preserving shrinkage ├── CaNA_expansion_150.log # Detailed processing logs ├── CaNA_shrinking_75.log # Algorithm execution details └── CaNA_shrinking_75_stats.csv # Comprehensive statistics ``` ## 🔬 Technical Details ### Algorithm Overview CaNA employs a sophisticated multi-step approach with improved control mechanisms: 1. Lesion Detection: Identifies individual nodules using connected component analysis 2. Anatomical Context: Uses lung segmentation labels (28-32) as spatial constraints 3. Controlled Morphological Processing: Applies iterative erosion/dilation with overshoot prevention 4. Volume Control: Precisely targets desired size changes with ±10% tolerance 5. Quality Assurance: Validates results and logs comprehensive statistics with real-time feedback ### Enhanced Features (v1.1) - Overshoot Prevention: Stops growth before exceeding 110% of target volume - Real-time Progress Tracking: Detailed logging of each iteration step - Boundary Validation: Ensures nodules remain within anatomical constraints - Error Recovery: Fallback mechanisms for edge cases and boundary conflicts ### Key Parameters - Lesion Label: `23` (lung nodule segmentation label) - Lung Labels: `[28, 29, 30, 31, 32]` (organ context labels) - Scale Factors: 150% (expansion), 75% (shrinking) - Morphological Element: 3D ball structure for realistic shape preservation ### Data Format Input JSON structure: ```json { "training": [ { "label": "path/to/segmentation.nii.gz" } ] } ``` ## 📈 Performance Metrics Based on validation with DLCS lung nodule datasets: - Processing Speed: ~15-22 seconds per nodule (512×512×256 volumes) - Volume Accuracy: ±10% of target volume (improved overshoot prevention) - Anatomical Preservation: 100% constraint compliance within lung boundaries - Success Rate: 100% successful augmentations with controlled growth - Target Achievement: 1.14x-1.47x actual vs 1.5x target (expansion mode) - Memory Usage: ~2GB RAM per case processing ## 🛠 Advanced Configuration ### Custom Parameters You can modify the Python scripts for custom configurations: ```python # Modify expansion percentage --scale_percent 50 # For 150% final size # Modify shrinking percentage --scale_percent 75 # For 75% final size # Custom lung labels --lung_labels [28, 29, 30, 31, 32] # Custom lesion label --lunglesion_lbl 23 ``` ### Docker Environment The ft42/pins:latest container includes: - PyTorch 2.8.0: Deep learning framework - MONAI 1.4.0: Medical imaging AI toolkit - OpenCV 4.11.0: Computer vision library - NiBabel: NIfTI file I/O - scikit-image: Image processing utilities ## 📋 Requirements ### System Requirements - Memory: 8GB RAM minimum (16GB recommended) - Storage: 10GB free space for Docker container - CPU: Multi-core processor recommended - GPU: Optional (CUDA support available) ### Dependencies All dependencies are pre-installed in the Docker container: ``` pytorch>=2.8.0 monai>=1.4.0 nibabel>=5.0.0 scikit-image>=0.21.0 numpy>=1.24.0 scipy>=1.10.0 ``` ## 🔍 Troubleshooting ### Common Issues 1. Permission Errors: Ensure Docker has proper volume mounting permissions 2. Memory Issues: Increase Docker memory allocation for large datasets 3. File Paths: Use absolute paths or ensure proper working directory ### Debug Mode Enable verbose logging by modifying the log level in the Python scripts: ```python logging.basicConfig(level=logging.DEBUG) ``` ## 📚 Citation If you use CaNA in your research, please cite: ```bibtex @software{cana2025, title={CaNA: Context-Aware Nodule Augmentation}, author={Your Name}, year={2025}, url={https://github.com/your-repo/CaNA}, note={Organ- and body-guided augmentation of lung nodule masks} } ``` ## 📄 License This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC-BY-NC-4.0). - ✅ Permitted: Academic research, educational use, non-commercial applications - ❌ Prohibited: Commercial use without explicit permission - 📝 Required: Attribution to original authors See the [LICENSE](LICENSE) file for full details. ## 🤝 Contributing We welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details. ## 📞 Support - Issues: [GitHub Issues](https://github.com/your-repo/CaNA/issues) - Documentation: [Technical Documentation](docs/technical_report.md) - Contact: [[email protected]] ## 🏆 Acknowledgments - Built on top of MONAI framework - Docker integration with ft42/pins medical imaging stack - Inspired by anatomically-constrained augmentation research --- CaNA: Advancing medical imaging through context-aware augmentation --- license: cc-by-nc-nd-4.0 ---
rlogh/cheese-texture-autogluon-classifier	rlogh	2025-09-22T01:58:11Z	0	0	null	[ "tabular", "classification", "automl", "autogluon", "cheese", "food", "texture", "dataset:aslan-ng/cheese-tabular", "license:mit", "model-index", "region:us" ]	null	2025-09-20T23:00:34Z	--- license: mit tags: - tabular - classification - automl - autogluon - cheese - food - texture datasets: - aslan-ng/cheese-tabular metrics: - accuracy - f1-score model-index: - name: Cheese Texture AutoGluon Classifier results: - task: type: text-classification name: Cheese Texture Classification dataset: type: aslan-ng/cheese-tabular name: Cheese Tabular Dataset metrics: - type: accuracy value: 0.3167 name: Test Accuracy - type: f1 value: 0.3100 name: Test F1 Score - type: accuracy value: 0.1667 name: External Validation Accuracy - type: f1 value: 0.1635 name: External Validation F1 Score --- # Cheese Texture Classification Model ## Model Description This is an AutoGluon-trained machine learning model for predicting cheese texture based on nutritional and origin features. The model was trained using automated machine learning techniques to find the best performing algorithm and hyperparameters for this classification task. Model Creator: Rumi Loghmani Model Repository: [rlogh/cheese-texture-autogluon-classifier](https://huggingface.co/rlogh/cheese-texture-autogluon-classifier) ## Model Details - Model Type: AutoGluon TabularPredictor - Task: Multiclass Classification - Target Variable: texture (soft, semi-soft, semi-hard, hard) - Features: fat, origin, holed, price, protein - Best Model: NeuralNetTorch_r121_BAG_L1 - Training Time: 9.27 seconds - Prediction Time: 0.0627 seconds per sample ## Dataset - Source: [aslan-ng/cheese-tabular](https://huggingface.co/datasets/aslan-ng/cheese-tabular) - Original Dataset Creator: [Aslan Noorghasemi](https://huggingface.co/aslan-ng) (Hugging Face username: aslan-ng) - Training Data: 300 augmented samples (80% train, 20% test) - Validation Data: 30 original samples - Total Features: 5 (fat, origin, holed, price, protein) - Classes: 4 texture categories ## Performance ### Test Set Performance (Synthetic Data) - Accuracy: 0.3167 - Weighted F1 Score: 0.3100 ### External Validation (Original Data) - Accuracy: 0.1667 - Weighted F1 Score: 0.1635 ## Usage ### Quick Inference (Pickle File) ```python import cloudpickle import huggingface_hub import pandas as pd # Download and load the model model_path = huggingface_hub.hf_hub_download( repo_id="rlogh/cheese-texture-autogluon-classifier", filename="cheese_texture_predictor.pkl" ) with open(model_path, "rb") as f: predictor = cloudpickle.load(f) # Prepare your data (example) new_cheese_data = pd.DataFrame({ 'fat': [25.0], 'origin': ['Italy'], 'holed': [0], 'price': [3.50], 'protein': [22.0] }) # Make predictions predictions = predictor.predict(new_cheese_data) print(f"Predicted texture: {predictions[0]}") ``` ### Complete Inference (Native Directory) ```python import huggingface_hub import zipfile import shutil import autogluon.tabular import pandas as pd # Download and extract the model zip_path = huggingface_hub.hf_hub_download( repo_id="rlogh/cheese-texture-autogluon-classifier", filename="cheese_texture_predictor_dir.zip" ) # Extract to a directory extract_dir = "extracted_predictor" with zipfile.ZipFile(zip_path, 'r') as zip_ref: zip_ref.extractall(extract_dir) # Load the native predictor predictor = autogluon.tabular.TabularPredictor.load(extract_dir) # Make predictions predictions = predictor.predict(new_cheese_data) ``` ## Feature Importance The model considers the following features in order of importance: 1. fat: Fat content per 100g of cheese 2. protein: Protein content per 100g of cheese 3. price: Price per unit 4. origin: Country/region of origin 5. holed: Whether the cheese has holes (0 or 1) ## Limitations - The model is trained on a relatively small dataset (330 samples total) - Performance may vary on cheese types not well represented in the training data - The model assumes standard nutritional values and may not account for variations in cheese production methods - External validation shows some performance degradation, indicating potential overfitting to synthetic data ## Training Details - Framework: AutoGluon Tabular - Training Time: 10 minutes (600 seconds) - Preset: best_quality - Evaluation Metric: accuracy - Cross-Validation: Yes (handled by AutoGluon) ## AI Usage in Development This code was developed with the assistance of an AI co-pilot. The AI helped with various tasks, including: - Generating initial code structures and boilerplate. - Providing suggestions for code optimization and best practices. - Assisting with debugging and error resolution. - Generating explanatory text and documentation, such as parts of this model card. The AI acted as a collaborative partner throughout the development process, accelerating the coding workflow and providing helpful guidance. ## Citation If you use this model, please cite the original dataset: ```bibtex @dataset{aslan-ng/cheese-tabular, title={Cheese Tabular Dataset}, author={Aslan Noorghasemi}, year={2024}, url={https://huggingface.co/datasets/aslan-ng/cheese-tabular}, publisher={Hugging Face}, doi={10.57967/hf/1234} } ``` Original Dataset: [aslan-ng/cheese-tabular](https://huggingface.co/datasets/aslan-ng/cheese-tabular) Dataset Creator: [Aslan Noorghasemi](https://huggingface.co/aslan-ng) (@aslan-ng) ## Contact Model Creator: Rumi Loghmani Model Questions: Please refer to the model repository or contact the model creator. Dataset Questions: For questions about the original dataset, please contact [Aslan Noorghasemi](https://huggingface.co/aslan-ng) or refer to the [original dataset documentation](https://huggingface.co/datasets/aslan-ng/cheese-tabular).
haihp02/instrctedbest	haihp02	2025-09-22T01:55:18Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-21T18:46:04Z	--- library_name: transformers tags: - trl - dpo --- # 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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poolkiltzn/blockassist-bc-vigilant_alert_tuna_1758505872	poolkiltzn	2025-09-22T01:52:22Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "vigilant alert tuna", "arxiv:2504.07091", "region:us" ]	null	2025-09-22T01:52:13Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - vigilant alert tuna --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
poolkiltzn/blockassist-bc-vigilant_alert_tuna_1758505254	poolkiltzn	2025-09-22T01:42:05Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "vigilant alert tuna", "arxiv:2504.07091", "region:us" ]	null	2025-09-22T01:41:57Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - vigilant alert tuna --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp-down_pnas_layer_16_4_all_4_0.001_1280_3	winnieyangwannan	2025-09-22T01:29:17Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-22T01:27:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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ChenWu98/numina_qwen_2.5_0.5b_sft_numina_40k_cluster2_condition	ChenWu98	2025-09-22T01:10:29Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:Qwen/Qwen2.5-0.5B", "base_model:finetune:Qwen/Qwen2.5-0.5B", "endpoints_compatible", "region:us" ]	null	2025-09-22T01:08:15Z	--- base_model: Qwen/Qwen2.5-0.5B library_name: transformers model_name: numina_qwen_2.5_0.5b_sft_numina_40k_cluster2_condition tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for numina_qwen_2.5_0.5b_sft_numina_40k_cluster2_condition This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B). 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"]) ``` ## 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/chenwu/huggingface/runs/7v0g2eln) This model was trained with SFT. ### Framework versions - TRL: 0.19.1 - Transformers: 4.51.1 - Pytorch: 2.7.0 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
wsbagnsv1/VibeVoice-Large-pt-gguf	wsbagnsv1	2025-09-22T01:08:01Z	3,790	19	null	[ "base_model:WestZhang/VibeVoice-Large-pt", "base_model:finetune:WestZhang/VibeVoice-Large-pt", "license:apache-2.0", "region:us" ]	null	2025-08-30T17:30:38Z	--- license: apache-2.0 base_model: - WestZhang/VibeVoice-Large-pt --- Highly experimental, there is no inference support yet and changes might be made later on
kevinshin/qwen2.5-1.5b-rft-sft-epoch-2-wc-cw-3k-pos-pos-add	kevinshin	2025-09-22T01:05:16Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "alignment-handbook", "trl", "sft", "conversational", "dataset:kevinshin/wildchat-creative-writing-3k-critique-v2", "base_model:kevinshin/qwen2.5-1.5b-it-think-rft-lr-1e-5-batch-16-epoch-1-wildchat-cw-3k", "base_model:finetune:kevinshin/qwen2.5-1.5b-it-think-rft-lr-1e-5-batch-16-epoch-1-wildchat-cw-3k", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-21T14:52:35Z	--- base_model: kevinshin/qwen2.5-1.5b-it-think-rft-lr-1e-5-batch-16-epoch-1-wildchat-cw-3k datasets: kevinshin/wildchat-creative-writing-3k-critique-v2 library_name: transformers model_name: qwen2.5-1.5b-rft-sft-epoch-2-wc-cw-3k-pos-pos-add tags: - generated_from_trainer - alignment-handbook - trl - sft licence: license --- # Model Card for qwen2.5-1.5b-rft-sft-epoch-2-wc-cw-3k-pos-pos-add This model is a fine-tuned version of [kevinshin/qwen2.5-1.5b-it-think-rft-lr-1e-5-batch-16-epoch-1-wildchat-cw-3k](https://huggingface.co/kevinshin/qwen2.5-1.5b-it-think-rft-lr-1e-5-batch-16-epoch-1-wildchat-cw-3k) on the [kevinshin/wildchat-creative-writing-3k-critique-v2](https://huggingface.co/datasets/kevinshin/wildchat-creative-writing-3k-critique-v2) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="kevinshin/qwen2.5-1.5b-rft-sft-epoch-2-wc-cw-3k-pos-pos-add", 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/myungjune-sogang-university/general_remo_train/runs/36le3t7l) This model was trained with SFT. ### Framework versions - TRL: 0.19.1 - Transformers: 4.55.0.dev0 - Pytorch: 2.6.0+cu126 - Datasets: 4.0.0 - Tokenizers: 0.21.2 ## 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ZYXue/qwen2-VL-7B-Instruct-syn-count-lora-only-black-1000	ZYXue	2025-09-22T01:00:07Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-VL-7B-Instruct", "base_model:adapter:Qwen/Qwen2.5-VL-7B-Instruct", "region:us" ]	null	2025-09-22T00:59:26Z	--- base_model: Qwen/Qwen2.5-VL-7B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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] ### Framework versions - PEFT 0.13.2
ZYXue/qwen2-VL-7B-Instruct-syn-count-lora-only-black-100	ZYXue	2025-09-22T00:59:24Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-VL-7B-Instruct", "base_model:adapter:Qwen/Qwen2.5-VL-7B-Instruct", "region:us" ]	null	2025-09-22T00:59:14Z	--- base_model: Qwen/Qwen2.5-VL-7B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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] ### Framework versions - PEFT 0.13.2
shaansriram8/dummy_model_ECE461	shaansriram8	2025-09-22T00:46:45Z	0	0	null	[ "region:us" ]	null	2025-09-22T00:33:44Z	# Dummy Model – ECE461 Assignment This repository is a placeholder model created for a requirements-engineering exercise at Purdue University. It does not contain any real machine-learning weights or usable code. ## Contents - `README.md` – this model card - `.gitattributes` – Git LFS configuration for large files ## Intended Use This repo exists only to demonstrate how to: 1. Create a model repository on [Hugging Face](https://huggingface.co). 2. Edit and manage files using either the web interface or Git. ## Limitations ⚠️ No functional model artifacts are provided. This project is not intended for production or research. ## License MIT License (default Hugging Face option for demo repositories). ---
sameeahameed/DILC-llama-3.2-3b-persona-all-without-NZ-IDRISI	sameeahameed	2025-09-22T00:40:17Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-09-22T00:39:58Z	--- base_model: unsloth/llama-3.2-3b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: sameeahameed - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-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)
aclay27/AntClay-Replicate	aclay27	2025-09-22T00:32:58Z	6	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	2025-09-01T01:03:12Z	--- 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: Anthony --- # Antclay Replicate <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 `Anthony` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Anthony", "lora_weights": "https://huggingface.co/aclay27/AntClay-Replicate/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('aclay27/AntClay-Replicate', weight_name='lora.safetensors') image = pipeline('Anthony').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: 2500 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/aclay27/AntClay-Replicate/discussions) to add images that show off what you’ve made with this LoRA.
luckycanucky/harmproject-auto	luckycanucky	2025-09-22T00:25:16Z	0	0	transformers	[ "transformers", "safetensors", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:SicariusSicariiStuff/Impish_LLAMA_3B", "base_model:quantized:SicariusSicariiStuff/Impish_LLAMA_3B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-09-21T03:58:10Z	--- base_model: SicariusSicariiStuff/Impish_LLAMA_3B tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: luckycanucky - License: apache-2.0 - Finetuned from model : SicariusSicariiStuff/Impish_LLAMA_3B 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)
kevinkyi/Homework2_Classical_ML	kevinkyi	2025-09-22T00:05:58Z	0	0	autogluon	[ "autogluon", "automl", "tabular", "sklearn", "tabular-classification", "en", "license:mit", "region:us" ]	tabular-classification	2025-09-21T23:48:39Z	--- library_name: autogluon pipeline_tag: tabular-classification license: mit tags: - automl - tabular - autogluon - sklearn model_name: Football Elite Classifier — AutoML (AutoGluon Tabular) language: - en --- # Football Elite Classifier — AutoML (AutoGluon Tabular) ## Purpose This model was developed as part of a class assignment on designing and deploying AI/ML systems. It demonstrates the use of AutoML (AutoGluon Tabular) to build a binary classifier on football receiver stats. ## Dataset - Source: https://huggingface.co/datasets/james-kramer/receiverstats - Split: Stratified Train/Test = 80/20 on the original split. - Features: ['Tgt', 'Rec', 'Yds', 'YBC_per_R', 'YAC_per_R', 'ADOT', 'Drop_pct', 'Rat'] - Target: `Elite` (0/1) - Preprocessing: Identifier columns dropped (e.g., `Player`). Numeric coercion applied; rows with NA removed. ## Training Setup - Framework: AutoGluon Tabular - Preset: `best_quality` - Time budget: 300 seconds - Seed: 42 - Eval metric: F1 (binary) - Hardware/Compute: Colab CPU runtime (2 vCPUs, ~12 GB RAM) - AI Usage Disclosure: Generative AI tools were used to help structure code and documentation; model training and results are real. ## Hyperparameters / Search Space - AutoGluon explored LightGBM, XGBoost, and ensembling variants. - Random state set for reproducibility. - Auto-stacking and bagging enabled under `best_quality`. - Internal hyperparameter tuning handled automatically by AutoGluon. ## Results (Held-out Test) ```json { "accuracy": 0.8333333333333334, "f1": 0.8 } ``` ## Limitations & Ethics - Correlations do not imply causation; labels may reflect selection bias. - Out-of-distribution players/contexts may reduce performance. - Intended for coursework, not for real personnel decisions. ## License - Code & weights: <MIT/Apache-2.0 or course-required license> ## Acknowledgments AutoML with [AutoGluon Tabular]. Trained in Google Colab. GenAI tools assisted with boilerplate and doc structure. James Kramers hugging face dataset
mradermacher/Mistral-3.1-Instruct-No-Vision-ChatML-GGUF	mradermacher	2025-09-22T00:00:10Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:NewEden/Mistral-3.1-Instruct-No-Vision-ChatML", "base_model:quantized:NewEden/Mistral-3.1-Instruct-No-Vision-ChatML", "endpoints_compatible", "region:us" ]	null	2025-09-21T20:13:16Z	--- base_model: NewEden/Mistral-3.1-Instruct-No-Vision-ChatML language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/NewEden/Mistral-3.1-Instruct-No-Vision-ChatML <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Mistral-3.1-Instruct-No-Vision-ChatML-GGUF).* 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/Mistral-3.1-Instruct-No-Vision-ChatML-GGUF/resolve/main/Mistral-3.1-Instruct-No-Vision-ChatML.Q2_K.gguf) \| Q2_K \| 9.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-3.1-Instruct-No-Vision-ChatML-GGUF/resolve/main/Mistral-3.1-Instruct-No-Vision-ChatML.Q3_K_S.gguf) \| Q3_K_S \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-3.1-Instruct-No-Vision-ChatML-GGUF/resolve/main/Mistral-3.1-Instruct-No-Vision-ChatML.Q3_K_M.gguf) \| Q3_K_M \| 11.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-3.1-Instruct-No-Vision-ChatML-GGUF/resolve/main/Mistral-3.1-Instruct-No-Vision-ChatML.Q3_K_L.gguf) \| Q3_K_L \| 12.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-3.1-Instruct-No-Vision-ChatML-GGUF/resolve/main/Mistral-3.1-Instruct-No-Vision-ChatML.IQ4_XS.gguf) \| IQ4_XS \| 13.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-3.1-Instruct-No-Vision-ChatML-GGUF/resolve/main/Mistral-3.1-Instruct-No-Vision-ChatML.Q4_K_S.gguf) \| Q4_K_S \| 13.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-3.1-Instruct-No-Vision-ChatML-GGUF/resolve/main/Mistral-3.1-Instruct-No-Vision-ChatML.Q4_K_M.gguf) \| Q4_K_M \| 14.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-3.1-Instruct-No-Vision-ChatML-GGUF/resolve/main/Mistral-3.1-Instruct-No-Vision-ChatML.Q5_K_S.gguf) \| Q5_K_S \| 16.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-3.1-Instruct-No-Vision-ChatML-GGUF/resolve/main/Mistral-3.1-Instruct-No-Vision-ChatML.Q5_K_M.gguf) \| Q5_K_M \| 16.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-3.1-Instruct-No-Vision-ChatML-GGUF/resolve/main/Mistral-3.1-Instruct-No-Vision-ChatML.Q6_K.gguf) \| Q6_K \| 19.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-3.1-Instruct-No-Vision-ChatML-GGUF/resolve/main/Mistral-3.1-Instruct-No-Vision-ChatML.Q8_0.gguf) \| Q8_0 \| 25.2 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
kaitongg/best_tomato_model	kaitongg	2025-09-21T23:55:35Z	0	0	keras	[ "keras", "image-classification", "tensorflow", "keras-tuner", "computer-vision", "dataset:Iris314/Food_tomatoes_dataset", "region:us" ]	image-classification	2025-09-21T04:38:00Z	--- tags: - image-classification - tensorflow - keras-tuner - computer-vision datasets: - Iris314/Food_tomatoes_dataset --- # Tomato Binary Classification Model This model is a convolutional neural network trained to classify images of tomatoes into two categories (presumably ripe and unripe, based on the dataset name and binary classification setup). ## Model Architecture The model architecture was determined using Keras Tuner's Hyperband algorithm. Based on the previous tuning results, the best hyperparameters found were: - `conv_blocks`: 2 - `filters_0`: 32 - `dense_units`: 64 - `dropout`: 0.1 - `lr`: 0.001 - `filters_1`: 16 The model consists of: - Data augmentation layers (RandomFlip, RandomRotation, RandomZoom) applied during training. - Two convolutional blocks: - The first block has 32 filters, a 3x3 kernel, ReLU activation, and MaxPooling. - The second block has 16 filters, a 3x3 kernel, ReLU activation, and MaxPooling. - A Flatten layer. - A dense layer with 64 units and ReLU activation. - A Dropout layer with a rate of 0.1. - An output layer with a single unit and a sigmoid activation function for binary classification. ## Training - Dataset: Iris314/Food_tomatoes_dataset. The `augmented` split was used for training, and the `original` split was used for validation. - Input Resolution: Images are resized to 128x128 pixels. - Preprocessing: Images are converted to RGB and pixel values are scaled to the range [0, 1]. - Optimizer: Adam with a learning rate of 0.001 (based on the best hyperparameters). - Loss Function: Binary Crossentropy. - Metrics: Accuracy was used as the evaluation metric. - Early Stopping: Training was stopped early if the validation loss did not improve for 3 consecutive epochs. The model was trained for a maximum of 15 epochs. ## Performance Based on the evaluation on the validation set, the model achieved the following performance: - Accuracy: 1.00 - Loss: 0.0079 Classification Report: ``` import tensorflow as tf from PIL import Image import numpy as np #Load the model model = tf.keras.models.load_model('best_tomato_model.keras') #Load and preprocess an image img_path = 'path/to/your/image.jpg' # Replace with your image path img = Image.open(img_path).convert('RGB').resize((128, 128)) img_array = np.array(img) / 255.0 img_array = np.expand_dims(img_array, axis=0) # Add batch dimension #Make a prediction prediction = model.predict(img_array) #Interpret the prediction predicted_class = int(prediction > 0.5) print(f"Prediction: {prediction[0][0]:.4f}") print(f"Predicted class: {predicted_class}") ```
Sarath3321/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-shy_hibernating_leopard	Sarath3321	2025-09-21T23:53:43Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am shy_hibernating_leopard", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-21T15:56:38Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am shy_hibernating_leopard --- # 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]
godnpeter/scratch_libero_singlegpu_refactor_fixloss_state-meanstd-action-identity-normaliz_0921	godnpeter	2025-09-21T23:44:30Z	0	0	lerobot	[ "lerobot", "safetensors", "smolvla", "robotics", "dataset:godnpeter/aopoli-lv-libero_combined_no_noops_lerobot_v21", "arxiv:2506.01844", "base_model:lerobot/smolvla_base", "base_model:finetune:lerobot/smolvla_base", "license:apache-2.0", "region:us" ]	robotics	2025-09-21T23:44:23Z	--- base_model: lerobot/smolvla_base datasets: godnpeter/aopoli-lv-libero_combined_no_noops_lerobot_v21 library_name: lerobot license: apache-2.0 model_name: smolvla pipeline_tag: robotics tags: - smolvla - lerobot - robotics --- # Model Card for smolvla <!-- Provide a quick summary of what the model is/does. --> [SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash lerobot-train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash lerobot-record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with eval\_ and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - License: apache-2.0
IIEleven11/Huihui-Tongyi-DeepResearch-30B-A3B-abliterated-Q8_0-GGUF	IIEleven11	2025-09-21T23:40:52Z	0	0	transformers	[ "transformers", "gguf", "abliterated", "uncensored", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:huihui-ai/Huihui-Tongyi-DeepResearch-30B-A3B-abliterated", "base_model:quantized:huihui-ai/Huihui-Tongyi-DeepResearch-30B-A3B-abliterated", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-09-21T23:37:17Z	--- license: apache-2.0 language: - en base_model: huihui-ai/Huihui-Tongyi-DeepResearch-30B-A3B-abliterated pipeline_tag: text-generation library_name: transformers tags: - abliterated - uncensored - llama-cpp - gguf-my-repo --- # IIEleven11/Huihui-Tongyi-DeepResearch-30B-A3B-abliterated-Q8_0-GGUF This model was converted to GGUF format from [`huihui-ai/Huihui-Tongyi-DeepResearch-30B-A3B-abliterated`](https://huggingface.co/huihui-ai/Huihui-Tongyi-DeepResearch-30B-A3B-abliterated) 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/huihui-ai/Huihui-Tongyi-DeepResearch-30B-A3B-abliterated) 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 IIEleven11/Huihui-Tongyi-DeepResearch-30B-A3B-abliterated-Q8_0-GGUF --hf-file huihui-tongyi-deepresearch-30b-a3b-abliterated-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo IIEleven11/Huihui-Tongyi-DeepResearch-30B-A3B-abliterated-Q8_0-GGUF --hf-file huihui-tongyi-deepresearch-30b-a3b-abliterated-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo IIEleven11/Huihui-Tongyi-DeepResearch-30B-A3B-abliterated-Q8_0-GGUF --hf-file huihui-tongyi-deepresearch-30b-a3b-abliterated-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo IIEleven11/Huihui-Tongyi-DeepResearch-30B-A3B-abliterated-Q8_0-GGUF --hf-file huihui-tongyi-deepresearch-30b-a3b-abliterated-q8_0.gguf -c 2048 ```
luckeciano/Qwen-2.5-7B-GRPO-Adam-FisherMaskToken-1e-4-HessianMaskToken-0.01-CAPOOnly-v2_9142	luckeciano	2025-09-21T23:39:49Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:DigitalLearningGmbH/MATH-lighteval", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-Math-7B", "base_model:finetune:Qwen/Qwen2.5-Math-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-21T22:25:23Z	--- base_model: Qwen/Qwen2.5-Math-7B datasets: DigitalLearningGmbH/MATH-lighteval library_name: transformers model_name: Qwen-2.5-7B-GRPO-Adam-FisherMaskToken-1e-4-HessianMaskToken-0.01-CAPOOnly-v2_4750 tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for Qwen-2.5-7B-GRPO-Adam-FisherMaskToken-1e-4-HessianMaskToken-0.01-CAPOOnly-v2_4750 This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-GRPO-Adam-FisherMaskToken-1e-4-HessianMaskToken-0.01-CAPOOnly-v2_4750", 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/max-ent-llms/PolicyGradientStability/runs/8zf6wmml) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.5.1 - Datasets: 3.4.1 - Tokenizers: 0.21.2 ## 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}} } ```
zjhhhh/qwen2.5_3B_Instruct_fixed_beta_1_eta_1e6_step_312_final	zjhhhh	2025-09-21T23:08:39Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-21T23:07:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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lemonhat/Qwen3-8B-sharegpt_o4_conversations_processed_filtered_1_passed_system	lemonhat	2025-09-21T23:03:38Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen3-8B", "base_model:finetune:Qwen/Qwen3-8B", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-21T22:51:13Z	--- library_name: transformers license: other base_model: Qwen/Qwen3-8B tags: - llama-factory - full - generated_from_trainer model-index: - name: sharegpt_o4_conversations_processed_filtered_1_passed_system 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. --> # sharegpt_o4_conversations_processed_filtered_1_passed_system This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) on the sharegpt_o4_conversations_processed_filtered_1_passed_system dataset. It achieves the following results on the evaluation set: - Loss: 0.2585 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 8 - total_eval_batch_size: 8 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.51.0 - Pytorch 2.8.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
sambrego/ppo-LunarLander-v2	sambrego	2025-09-21T22:58:11Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-09-21T22:56:39Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 230.94 +/- 61.63 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
ncgc0incendiary/retraining-bias-statichh-Qwen-1.5B-sft-bf16-pureif-100	ncgc0incendiary	2025-09-21T22:55:13Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "sft", "trl", "conversational", "base_model:Qwen/Qwen2.5-1.5B", "base_model:finetune:Qwen/Qwen2.5-1.5B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-21T20:57:36Z	--- base_model: Qwen/Qwen2.5-1.5B library_name: transformers model_name: retraining-bias-statichh-Qwen-1.5B-sft-bf16-pureif-100 tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for retraining-bias-statichh-Qwen-1.5B-sft-bf16-pureif-100 This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B). 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="ncgc0incendiary/retraining-bias-statichh-Qwen-1.5B-sft-bf16-pureif-100", 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/2this0username0isnt2allowed-indian-institute-of-science/huggingface/runs/gadsri88) This model was trained with SFT. ### Framework versions - TRL: 0.19.1 - Transformers: 4.52.4 - Pytorch: 2.7.1+rocm6.3 - Datasets: 3.6.0 - Tokenizers: 0.21.4 ## 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
aayasmin880/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-colorful_fanged_capybara	aayasmin880	2025-09-21T22:51:32Z	2	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am colorful fanged capybara", "trl", "genrl-swarm", "I am colorful_fanged_capybara", "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	2025-04-05T08:19:44Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-colorful_fanged_capybara tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am colorful fanged capybara - trl - genrl-swarm - I am colorful_fanged_capybara licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-colorful_fanged_capybara 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="aayasmin880/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-colorful_fanged_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: 4.51.2 - Pytorch: 2.5.1 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
dnzcany/mrpc-bert-final	dnzcany	2025-09-21T22:51:06Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-09-21T22:50:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
Sigmandndnns/Re822	Sigmandndnns	2025-09-21T22:16:20Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-09-21T22:16:20Z	--- license: apache-2.0 ---
hopstops/blockassist	hopstops	2025-09-21T22:16:06Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "hulking feathered lemur", "arxiv:2504.07091", "region:us" ]	null	2025-09-21T22:07:11Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - hulking feathered lemur --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
msuribec/imdbreviews_classification_deberta_v3_base_lora_v06	msuribec	2025-09-21T22:05:47Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-21T18:44:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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haihp02/1c8a781a-4ad3-46e8-842f-2904e68243f1	haihp02	2025-09-21T22:03:28Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-09-21T22:03:15Z	--- 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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ConicCat/humans.txt-Diverse-OrPO-24B-Q4_K_M-GGUF	ConicCat	2025-09-21T21:47:58Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "base_model:ConicCat/humans.txt-Diverse-OrPO-24B", "base_model:quantized:ConicCat/humans.txt-Diverse-OrPO-24B", "endpoints_compatible", "region:us" ]	null	2025-09-21T21:46:57Z	--- library_name: transformers tags: - llama-cpp - gguf-my-repo base_model: ConicCat/humans.txt-Diverse-OrPO-24B --- # ConicCat/humans.txt-Diverse-OrPO-24B-Q4_K_M-GGUF This model was converted to GGUF format from [`ConicCat/humans.txt-Diverse-OrPO-24B`](https://huggingface.co/ConicCat/humans.txt-Diverse-OrPO-24B) 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/ConicCat/humans.txt-Diverse-OrPO-24B) 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 ConicCat/humans.txt-Diverse-OrPO-24B-Q4_K_M-GGUF --hf-file humans.txt-diverse-orpo-24b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo ConicCat/humans.txt-Diverse-OrPO-24B-Q4_K_M-GGUF --hf-file humans.txt-diverse-orpo-24b-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 ConicCat/humans.txt-Diverse-OrPO-24B-Q4_K_M-GGUF --hf-file humans.txt-diverse-orpo-24b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo ConicCat/humans.txt-Diverse-OrPO-24B-Q4_K_M-GGUF --hf-file humans.txt-diverse-orpo-24b-q4_k_m.gguf -c 2048 ```
kevinshin/qwen3-1.7b-rpo-lr-1e-5-alpha-1-beta-0.1-epoch-2-wc-cw-3k-pref	kevinshin	2025-09-21T21:47:21Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:kevinshin/wildchat-creative-writing-3k-critique-v2", "arxiv:2305.18290", "base_model:Qwen/Qwen3-1.7B", "base_model:finetune:Qwen/Qwen3-1.7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-21T14:41:03Z	--- base_model: Qwen/Qwen3-1.7B datasets: kevinshin/wildchat-creative-writing-3k-critique-v2 library_name: transformers model_name: qwen3-1.7b-rpo-lr-1e-5-alpha-1-beta-0.1-epoch-2-wc-cw-3k-pref tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for qwen3-1.7b-rpo-lr-1e-5-alpha-1-beta-0.1-epoch-2-wc-cw-3k-pref This model is a fine-tuned version of [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) on the [kevinshin/wildchat-creative-writing-3k-critique-v2](https://huggingface.co/datasets/kevinshin/wildchat-creative-writing-3k-critique-v2) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="kevinshin/qwen3-1.7b-rpo-lr-1e-5-alpha-1-beta-0.1-epoch-2-wc-cw-3k-pref", 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/myungjune-sogang-university/general_remo_train/runs/43buxvg7) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.19.1 - Transformers: 4.55.0.dev0 - Pytorch: 2.6.0+cu126 - Datasets: 4.0.0 - Tokenizers: 0.21.2 ## 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}} } ```
lemonhat/Qwen2.5-7B-Instruct-2and3_apps_76_v6_processed	lemonhat	2025-09-21T21:17:57Z	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:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-21T21:07:33Z	--- library_name: transformers license: other base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: 2and3_apps_76_v6_processed 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. --> # 2and3_apps_76_v6_processed This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the 2and3_apps_76_v6_processed dataset. It achieves the following results on the evaluation set: - Loss: 0.2009 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 8 - total_eval_batch_size: 8 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.51.0 - Pytorch 2.8.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
yamxxx1/xray	yamxxx1	2025-09-21T21:14:40Z	0	0	null	[ "en", "license:mit", "region:us" ]	null	2025-09-21T21:11:53Z	--- license: mit language: - en ---
JasonTree/Qwen2.5-instruct-3B-SFT	JasonTree	2025-09-21T21:13:57Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-3B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-09-21T21:11:06Z	--- base_model: Qwen/Qwen2.5-3B-Instruct library_name: transformers model_name: Qwen2.5-instruct-3B-SFT tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Qwen2.5-instruct-3B-SFT This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-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="JasonTree/Qwen2.5-instruct-3B-SFT", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/alelab/QuiteGive/runs/683xihqs) This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
RaFast/sd-class-butterflies-32	RaFast	2025-09-21T20:54:38Z	0	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2025-09-21T20:54:26Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('RaFast/sd-class-butterflies-32') image = pipeline().images[0] image ```
rl-rag/qwen3-8B-sft-mix-v20250921	rl-rag	2025-09-21T20:53:46Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen3-8B", "base_model:finetune:Qwen/Qwen3-8B", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-09-21T20:52:44Z	--- library_name: transformers license: other base_model: Qwen/Qwen3-8B tags: - llama-factory - full - generated_from_trainer model-index: - name: qwen3-8B-sft-mix-v20250921 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. --> # qwen3-8B-sft-mix-v20250921 This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) on the rl-rag/sft-mix-v20250921 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.52.4 - Pytorch 2.8.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
Sefika/bart_fs_fewrel_5_4	Sefika	2025-09-21T20:51:54Z	4	0	null	[ "safetensors", "bart", "region:us" ]	null	2025-08-27T16:20:55Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/bart_fs_fewrel_5_4") model = AutoModel.from_pretrained("Sefika/bart_fs_fewrel_5_4")
Sefika/bart_fs_fewrel_5_3	Sefika	2025-09-21T20:51:52Z	4	0	null	[ "safetensors", "bart", "region:us" ]	null	2025-08-27T16:02:55Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/bart_fs_fewrel_5_3") model = AutoModel.from_pretrained("Sefika/bart_fs_fewrel_5_3")
Sefika/bart_fs_fewrel_4_3	Sefika	2025-09-21T20:51:30Z	4	0	null	[ "safetensors", "bart", "region:us" ]	null	2025-08-27T14:30:06Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/bart_fs_fewrel_4_3") model = AutoModel.from_pretrained("Sefika/bart_fs_fewrel_4_3")
Sefika/bart_fs_fewrel_3_8	Sefika	2025-09-21T20:51:23Z	2	0	null	[ "safetensors", "bart", "region:us" ]	null	2025-08-27T14:01:48Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/bart_fs_fewrel_3_8") model = AutoModel.from_pretrained("Sefika/bart_fs_fewrel_3_8")
Sefika/bart_fs_fewrel_2_2	Sefika	2025-09-21T20:50:48Z	7	0	null	[ "safetensors", "bart", "region:us" ]	null	2025-08-27T10:03:22Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/bart_fs_fewrel_2_2") model = AutoModel.from_pretrained("Sefika/bart_fs_fewrel_2_2")
Sefika/bart_fs_fewrel_1_4	Sefika	2025-09-21T20:50:33Z	6	0	null	[ "safetensors", "bart", "region:us" ]	null	2025-08-27T08:26:54Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/bart_fs_fewrel_1_4") model = AutoModel.from_pretrained("Sefika/bart_fs_fewrel_1_4")
Sefika/CRE_tacred_llama3_10_5_task_memory_5_7	Sefika	2025-09-21T20:49:29Z	28	0	null	[ "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2025-09-16T15:55:42Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/CRE_tacred_llama3_10_5_task_memory_5_7") model = AutoModel.from_pretrained("Sefika/CRE_tacred_llama3_10_5_task_memory_5_7")
Sefika/CRE_tacred_llama3_10_5_task_memory_5_1	Sefika	2025-09-21T20:49:14Z	16	0	null	[ "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2025-09-16T15:13:48Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/CRE_tacred_llama3_10_5_task_memory_5_1") model = AutoModel.from_pretrained("Sefika/CRE_tacred_llama3_10_5_task_memory_5_1")
Sefika/CRE_tacred_llama3_10_4_task_memory_5_9	Sefika	2025-09-21T20:49:09Z	31	0	null	[ "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2025-09-16T14:44:43Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/CRE_tacred_llama3_10_4_task_memory_5_9") model = AutoModel.from_pretrained("Sefika/CRE_tacred_llama3_10_4_task_memory_5_9")
Sefika/CRE_tacred_llama3_10_4_task_memory_5_6	Sefika	2025-09-21T20:49:03Z	29	0	null	[ "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2025-09-16T14:20:42Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/CRE_tacred_llama3_10_4_task_memory_5_6") model = AutoModel.from_pretrained("Sefika/CRE_tacred_llama3_10_4_task_memory_5_6")
Sefika/CRE_tacred_llama3_10_4_task_memory_5_3	Sefika	2025-09-21T20:48:56Z	29	0	null	[ "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2025-09-16T13:59:14Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/CRE_tacred_llama3_10_4_task_memory_5_3") model = AutoModel.from_pretrained("Sefika/CRE_tacred_llama3_10_4_task_memory_5_3")
Sefika/CRE_tacred_llama3_10_3_task_memory_5_2	Sefika	2025-09-21T20:48:28Z	28	0	null	[ "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2025-09-16T12:19:21Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/CRE_tacred_llama3_10_3_task_memory_5_2") model = AutoModel.from_pretrained("Sefika/CRE_tacred_llama3_10_3_task_memory_5_2")
Sefika/CRE_tacred_llama3_10_2_task_memory_5_6	Sefika	2025-09-21T20:48:13Z	28	0	null	[ "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2025-09-16T10:55:13Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/CRE_tacred_llama3_10_2_task_memory_5_6") model = AutoModel.from_pretrained("Sefika/CRE_tacred_llama3_10_2_task_memory_5_6")
Sefika/CRE_tacred_llama3_10_1_task_memory_5_6	Sefika	2025-09-21T20:47:50Z	31	0	null	[ "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2025-09-16T09:25:05Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/CRE_tacred_llama3_10_1_task_memory_5_6") model = AutoModel.from_pretrained("Sefika/CRE_tacred_llama3_10_1_task_memory_5_6")
Sefika/CRE_tacred_llama3_10_5_task_memory_10_5	Sefika	2025-09-21T20:46:32Z	31	0	null	[ "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2025-09-12T15:02:33Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/CRE_tacred_llama3_10_5_task_memory_10_5") model = AutoModel.from_pretrained("Sefika/CRE_tacred_llama3_10_5_task_memory_10_5")
Sefika/CRE_tacred_llama3_10_5_task_memory_10_3	Sefika	2025-09-21T20:46:28Z	31	0	null	[ "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2025-09-12T14:46:21Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/CRE_tacred_llama3_10_5_task_memory_10_3") model = AutoModel.from_pretrained("Sefika/CRE_tacred_llama3_10_5_task_memory_10_3")
Sefika/CRE_tacred_llama3_10_2_task_memory_10_8	Sefika	2025-09-21T20:45:27Z	31	0	null	[ "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2025-09-11T20:02:47Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/CRE_tacred_llama3_10_2_task_memory_10_8") model = AutoModel.from_pretrained("Sefika/CRE_tacred_llama3_10_2_task_memory_10_8")
Sefika/CRE_tacred_llama3_10_1_task_memory_10_6	Sefika	2025-09-21T20:44:55Z	30	0	null	[ "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2025-09-11T18:24:52Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/CRE_tacred_llama3_10_1_task_memory_10_6") model = AutoModel.from_pretrained("Sefika/CRE_tacred_llama3_10_1_task_memory_10_6")
Sefika/CRE_tacred_llama3_10_1_task_memory_10_5	Sefika	2025-09-21T20:44:51Z	30	0	null	[ "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2025-09-11T18:16:32Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/CRE_tacred_llama3_10_1_task_memory_10_5") model = AutoModel.from_pretrained("Sefika/CRE_tacred_llama3_10_1_task_memory_10_5")
Sefika/CRE_tacred_llama3_10_1_task_memory_10_3	Sefika	2025-09-21T20:44:46Z	31	0	null	[ "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2025-09-11T17:59:51Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/CRE_tacred_llama3_10_1_task_memory_10_3") model = AutoModel.from_pretrained("Sefika/CRE_tacred_llama3_10_1_task_memory_10_3")
Sefika/CRE_tacred_llama3_10_5_task_memory_15_9	Sefika	2025-09-21T20:44:15Z	33	0	null	[ "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2025-09-16T18:26:26Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/CRE_tacred_llama3_10_5_task_memory_15_9") model = AutoModel.from_pretrained("Sefika/CRE_tacred_llama3_10_5_task_memory_15_9")
Sefika/CRE_tacred_llama3_10_5_task_memory_15_1	Sefika	2025-09-21T20:43:56Z	18	0	null	[ "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2025-09-16T17:20:07Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/CRE_tacred_llama3_10_5_task_memory_15_1") model = AutoModel.from_pretrained("Sefika/CRE_tacred_llama3_10_5_task_memory_15_1")
Sefika/CRE_tacred_llama3_10_3_task_memory_15_10	Sefika	2025-09-21T20:43:30Z	18	0	null	[ "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2025-09-16T14:28:44Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/CRE_tacred_llama3_10_3_task_memory_15_10") model = AutoModel.from_pretrained("Sefika/CRE_tacred_llama3_10_3_task_memory_15_10")
Sefika/CRE_tacred_llama3_10_1_task_memory_15_8	Sefika	2025-09-21T20:42:37Z	29	0	null	[ "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2025-09-16T10:06:03Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/CRE_tacred_llama3_10_1_task_memory_15_8") model = AutoModel.from_pretrained("Sefika/CRE_tacred_llama3_10_1_task_memory_15_8")
Sefika/CRE_tacred_llama3_10_5_no_memory_4	Sefika	2025-09-21T20:41:32Z	15	0	null	[ "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2025-09-16T22:54:15Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/CRE_tacred_llama3_10_5_no_memory_4") model = AutoModel.from_pretrained("Sefika/CRE_tacred_llama3_10_5_no_memory_4")
Sefika/CRE_tacred_llama3_10_4_no_memory_5	Sefika	2025-09-21T20:41:09Z	16	0	null	[ "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2025-09-16T22:00:31Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/CRE_tacred_llama3_10_4_no_memory_5") model = AutoModel.from_pretrained("Sefika/CRE_tacred_llama3_10_4_no_memory_5")
Sefika/CRE_tacred_llama3_10_4_no_memory_3	Sefika	2025-09-21T20:41:04Z	16	0	null	[ "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2025-09-16T21:52:34Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/CRE_tacred_llama3_10_4_no_memory_3") model = AutoModel.from_pretrained("Sefika/CRE_tacred_llama3_10_4_no_memory_3")
Sefika/CRE_tacred_llama3_10_4_no_memory_2	Sefika	2025-09-21T20:41:01Z	16	0	null	[ "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2025-09-16T21:47:47Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/CRE_tacred_llama3_10_4_no_memory_2") model = AutoModel.from_pretrained("Sefika/CRE_tacred_llama3_10_4_no_memory_2")
Sefika/CRE_tacred_llama3_10_4_no_memory_1	Sefika	2025-09-21T20:40:59Z	16	0	null	[ "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2025-09-16T21:43:07Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/CRE_tacred_llama3_10_4_no_memory_1") model = AutoModel.from_pretrained("Sefika/CRE_tacred_llama3_10_4_no_memory_1")
Sefika/CRE_tacred_llama3_10_2_no_memory_7	Sefika	2025-09-21T20:40:24Z	15	0	null	[ "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2025-09-16T19:47:36Z	# My Model This is my model card. ## Usage ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Sefika/CRE_tacred_llama3_10_2_no_memory_7") model = AutoModel.from_pretrained("Sefika/CRE_tacred_llama3_10_2_no_memory_7")

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