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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 2025-06-28 18:27:08	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 501 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-28 18:25:37	card stringlengths 11 1.01M
Erik04/Regina	Erik04	2025-05-04T08:32:19Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-04T08:32:19Z	--- license: apache-2.0 ---
RajeevanL/tamil-xlm-roberta-large-squad2-finetuned-v_1	RajeevanL	2025-05-04T08:32:04Z	0	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "question-answering", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	question-answering	2025-05-04T08:30:43Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
ljhhgkjcgh/dfhdf	ljhhgkjcgh	2025-05-04T08:30:56Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-04T08:30:55Z	--- license: apache-2.0 ---
jhgjfgh/ghjkgjh	jhgjfgh	2025-05-04T08:30:54Z	0	0	null	[ "license:bsd-2-clause", "region:us" ]	null	2025-05-04T08:30:54Z	--- license: bsd-2-clause ---
lisabdunlap/pretrain_full_ft_movies_actors-r32-e3-lr0.0001-mixed-actors_reviews_freeform_pretrained_3b-new	lisabdunlap	2025-05-04T08:30:07Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T08:29:17Z	--- base_model: pretrain_full_ft_movies_actors tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: lisabdunlap - License: apache-2.0 - Finetuned from model : pretrain_full_ft_movies_actors 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)
amwright/ppo-Huggy	amwright	2025-05-04T08:28:17Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2025-05-04T08:28:05Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: amwright/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Membersuger/Euro_39	Membersuger	2025-05-04T08:26:10Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T04:43:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
LandCruiser/sn21_omegav1_0405_2	LandCruiser	2025-05-04T08:24:55Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-04T08:01:29Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
LandCruiser/sn21_omegav1_0405_3	LandCruiser	2025-05-04T08:20:34Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-04T08:01:32Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
uygitu/ytruru	uygitu	2025-05-04T08:15:14Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2025-05-04T08:15:14Z	--- license: bigscience-openrail-m ---
RaduFlorin/WaterPollution	RaduFlorin	2025-05-04T08:15:07Z	0	0	null	[ "region:us" ]	null	2025-05-04T07:59:04Z	# This is a Gradio app that creates a quiz about marine life. import gradio as gr import pandas as pd # Define a function to check the user's answer. def check_answer(question, user_answer): correct_answer = marine_life_df.loc[marine_life_df['Question'] == question, 'Answer'].values[0] if user_answer.lower() == correct_answer.lower(): return "Correct!" else: return "Incorrect. The correct answer is: " + correct_answer # Load the marine life quiz data from a DataFrame. marine_life_df = pd.DataFrame({ 'Question': [ "What is the largest animal on Earth?", "Which marine animal is known for its bioluminescence?", "What is the fastest fish in the ocean?", "Which marine mammal is known for its complex songs?", "What is the most venomous marine animal?" ], 'Answer': [ "Blue whale", "Firefly squid", "Sailfish", "Humpback whale", "Box jellyfish" ] }) # Create a Gradio interface that takes a question and user answer, runs it through the check_answer function, and returns output to a textbox. with gr.Blocks() as demo: with gr.Row(): question_dropdown = gr.Dropdown( choices=marine_life_df['Question'].tolist(), label="Select a Question" ) answer_textbox = gr.Textbox( label="Your Answer", placeholder="Type your answer here..." ) submit_button = gr.Button("Submit") result_textbox = gr.Textbox( label="Result", placeholder="Check your answer by clicking Submit..." ) # Set up the event listener for the submit button. submit_button.click( fn=check_answer, inputs=[question_dropdown, answer_textbox], outputs=result_textbox ) # Launch the interface. if __name__ == "__main__": demo.launch(show_error=True)
sjatin352/faster_rcnn_resnet50_genetic_algorithm	sjatin352	2025-05-04T08:14:34Z	0	0	null	[ "region:us" ]	null	2025-05-04T08:11:02Z	# Genetic CNN Object Detection with Faster R-CNN This repository contains a custom object detection model using Faster R-CNN with a ResNet-50 backbone, fine-tuned on a COCO 2017 subset. It uses genetic algorithms to evolve hyperparameters like filter size and activation functions. ## Usage ### 1. Install dependencies ```bash pip install -r requirements.txt ``` ### 2. Load model ```python from model import build_model import torch model = build_model(num_classes=91) model.load_state_dict(torch.load("best_model.pth")) model.eval() ``` ## Training See `Genetic Cnn Object Detection` for the full training and evolution pipeline. ## Files - `model.py`: Defines the model architecture. - `best_model.pth`: Trained model weights. - `evolution_metrics.csv`: Logs of genetic search metrics.
datapaf/ve_focus_deepseek_elixir	datapaf	2025-05-04T08:13:06Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T07:52:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
XlHoWcLGeuQ/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-burrowing_voracious_bear	XlHoWcLGeuQ	2025-05-04T08:10:40Z	8	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am burrowing voracious bear", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-22T16:02:23Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-burrowing_voracious_bear tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am burrowing voracious bear - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-burrowing_voracious_bear 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="XlHoWcLGeuQ/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-burrowing_voracious_bear", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
plawanrath/minstral-7b-rust-fine-tuned	plawanrath	2025-05-04T08:09:37Z	0	0	null	[ "safetensors", "mistral", "code", "license:mit", "region:us" ]	null	2025-05-04T06:51:14Z	--- license: mit tags: - code --- # Model Card for Model ID Minstral 7B parameter model fine-tuned on Rust code dataset. ## Model Details This is majorly fine-tuned on axum and async-graphql code. ### Model Description This took Minstral 7b parameter base model and fine-tuned it on Rust code majorly containing axum and async graphql code. It should be useful to assist in writing Rust backend code. - Developed by: Plawan Rath - Finetuned from model: Minstral-7B ### Downstream Use [optional] This can be locally used using GUI like LM Studio. To use it with LM Studio you can follow the following steps: Converting to GGUF for LMStudio 1. Install llama.cpp ``` git clone https://github.com/ggerganov/llama.cpp cd llama.cpp pip install -r requirements.txt ``` 2. Run convert-hf-to-gguf.py ``` python ./convert-hf-to-gguf.py \ <path-to-merged-model> \ --outfile ./mistral-lora-f16.gguf \ --outtype f16 ``` 3. Put it in LM Studio Folder structure LM Studio doesn’t just scan every file directly inside ~/.lmstudio/models/. For each model it expects two nested folders: ``` ~/.lmstudio/models/ └── <publisher-name>/ └── <model-name>/ └── model-file.gguf ``` 4. Now refresh "My Models" in Lm Studio to see this model. You should now be able to use this model to chat for codegen.
kamelcharaf/GRPO-qwen2.5-14B-quant-qwen2.5-14B-quant-mrd3-s2-sum	kamelcharaf	2025-05-04T08:09:21Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "conversational", "arxiv:2402.03300", "base_model:kamelcharaf/Qwen2.5-14B-Instruct-quantized-4bit", "base_model:quantized:kamelcharaf/Qwen2.5-14B-Instruct-quantized-4bit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-04-12T20:15:22Z	--- base_model: kamelcharaf/Qwen2.5-14B-Instruct-quantized-4bit library_name: transformers model_name: GRPO-qwen2.5-14B-quant-qwen2.5-14B-quant-mrd3-s2-sum tags: - generated_from_trainer licence: license --- # Model Card for GRPO-qwen2.5-14B-quant-qwen2.5-14B-quant-mrd3-s2-sum This model is a fine-tuned version of [kamelcharaf/Qwen2.5-14B-Instruct-quantized-4bit](https://huggingface.co/kamelcharaf/Qwen2.5-14B-Instruct-quantized-4bit). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="kamelcharaf/GRPO-qwen2.5-14B-quant-qwen2.5-14B-quant-mrd3-s2-sum", 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/kamel-charaf-epfl/huggingface/runs/ofx0gal2) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0 - Transformers: 4.48.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
AndreaPizzi/CU_with_BERT	AndreaPizzi	2025-05-04T08:03:43Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased-distilled-squad", "base_model:finetune:distilbert/distilbert-base-uncased-distilled-squad", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-04T08:01:43Z	--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-uncased-distilled-squad tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: CU_with_BERT 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. --> # CU_with_BERT This model is a fine-tuned version of [distilbert/distilbert-base-uncased-distilled-squad](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8013 - Accuracy: 0.6181 - F1: 0.6181 - Precision: 0.6181 - Recall: 0.6181 ## 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: 1.5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| No log \| 1.0 \| 187 \| 0.8013 \| 0.6181 \| 0.6181 \| 0.6181 \| 0.6181 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu126 - Datasets 3.5.0 - Tokenizers 0.21.1
stevensu123/cis6200finalbaseline-v2	stevensu123	2025-05-04T08:02:44Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T08:00:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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TentenPolllo/FruitClassifier	TentenPolllo	2025-05-04T07:59:38Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-04T07:59:17Z	--- license: apache-2.0 ---
pawin205/Qwen-7B-Review-ICLR-GRPO-H	pawin205	2025-05-04T07:59:18Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T07:56:45Z	--- 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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Hachipo/Meta-Llama-3-8B-MIFT-en_10000_2	Hachipo	2025-05-04T07:59:03Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T07:55:19Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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westy412/ppo-LunarLander-v2	westy412	2025-05-04T07:51:04Z	12	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-03-30T11:50:02Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 241.75 +/- 34.70 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 ... ```
vahidhoseini/mistral-roshdv1	vahidhoseini	2025-05-04T07:50:32Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T07:49:58Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Rainbowbeast/sidekicks	Rainbowbeast	2025-05-04T07:50:06Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-04T07:15:07Z	--- license: apache-2.0 ---
Mr-FineTuner/Test_1epoch_01_withNewEval_andWithin-1_testnewmodels_hilangPersentase_llama	Mr-FineTuner	2025-05-04T07:48:01Z	0	0	null	[ "safetensors", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T06:25:18Z	[1000/2395 17:11 < 24:01, 0.97 it/s, Epoch 0/1] Step Training Loss Validation Loss 200 1.144300 1.070119 400 1.032000 1.054562 600 0.910600 1.053359 800 0.922100 1.057499 1000 0.859700 1.061102 # Fine-Tuned Mistral-7B CEFR Model This is a fine-tuned version of `unsloth/mistral-7b-instruct-v0.3-bnb-4bit` for CEFR-level sentence generation. - Base Model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit - Fine-Tuning: LoRA with SMOTE-balanced dataset - Training Details: - Dataset: CEFR-level sentences with SMOTE and undersampling for balance (no rebalancing for validation/test sets) - LoRA Parameters: r=32, lora_alpha=32, lora_dropout=0.5 - Training Args: learning_rate=2e-5, batch_size=8, epochs=0.1, cosine scheduler - Optimizer: adamw_8bit - Early Stopping: Patience=3, threshold=0.01 - Evaluation Metrics (Exact Matches): - CEFR Classifier Accuracy: 0.300 - Precision (Macro): 0.398 - Recall (Macro): 0.300 - F1-Score (Macro): 0.300 - Evaluation Metrics (Within ±1 Level): - CEFR Classifier Accuracy: 0.683 - Precision (Macro): 0.726 - Recall (Macro): 0.683 - F1-Score (Macro): 0.658 - Other Metrics: - Perplexity: 3.038 - Diversity (Unique Sentences): 1.000 - Inference Time (ms): 6134.348 - Model Size (GB): 4.1 - Robustness (F1): 0.285 - Confusion Matrix (Exact Matches): - CSV: [confusion_matrix_exact.csv](confusion_matrix_exact.csv) - Image: [confusion_matrix_exact.png](confusion_matrix_exact.png) - Confusion Matrix (Within ±1 Level): - CSV: [confusion_matrix_within1.csv](confusion_matrix_within1.csv) - Image: [confusion_matrix_within1.png](confusion_matrix_within1.png) - Per-Class Confusion Metrics (Exact Matches): - A1: TP=1, FP=2, FN=9, TN=48 - A2: TP=2, FP=2, FN=8, TN=48 - B1: TP=5, FP=20, FN=5, TN=30 - B2: TP=3, FP=15, FN=7, TN=35 - C1: TP=1, FP=2, FN=9, TN=48 - C2: TP=6, FP=1, FN=4, TN=49 - Per-Class Confusion Metrics (Within ±1 Level): - A1: TP=1, FP=1, FN=9, TN=49 - A2: TP=7, FP=1, FN=3, TN=49 - B1: TP=10, FP=11, FN=0, TN=39 - B2: TP=8, FP=5, FN=2, TN=45 - C1: TP=8, FP=1, FN=2, TN=49 - C2: TP=7, FP=0, FN=3, TN=50 - Usage: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("Mr-FineTuner/Test___01_withNewEval_andWithin-1_testnewmodels") tokenizer = AutoTokenizer.from_pretrained("Mr-FineTuner/Test___01_withNewEval_andWithin-1_testnewmodels") # Example inference prompt = "<\|user\|>Generate a CEFR B1 level sentence.<\|end\|>" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_length=50) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` Uploaded using `huggingface_hub`.
Silverlaining/Qwen3-Base	Silverlaining	2025-05-04T07:47:16Z	2	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T07:46:28Z	--- 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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ASethi04/meta-llama-Llama-3.1-8B-hellaswag-second-lora-4-0.0001-same-prompt-template	ASethi04	2025-05-04T07:47:00Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "endpoints_compatible", "region:us" ]	null	2025-05-03T14:47:57Z	--- base_model: meta-llama/Llama-3.1-8B library_name: transformers model_name: meta-llama-Llama-3.1-8B-hellaswag-second-lora-4-0.0001-same-prompt-template tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for meta-llama-Llama-3.1-8B-hellaswag-second-lora-4-0.0001-same-prompt-template This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="ASethi04/meta-llama-Llama-3.1-8B-hellaswag-second-lora-4-0.0001-same-prompt-template", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/torchql-org/huggingface/runs/6op45zu3) This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
water-water/stackbar-qwen2.5	water-water	2025-05-04T07:40:52Z	0	0	transformers	[ "transformers", "qwen2_5_vl", "feature-extraction", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	feature-extraction	2025-05-04T06:11:04Z	--- base_model: unsloth/qwen2.5-vl-7b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2_5_vl license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: water-water - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-vl-7b-instruct-bnb-4bit This qwen2_5_vl 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)
zhiyaowang/VideoMaev2-giant-nexar-solution	zhiyaowang	2025-05-04T07:34:56Z	0	0	null	[ "video", "video-classification", "videomae", "collision-prediction", "kaggle", "en", "dataset:nexar", "dataset:nexar-ai/nexar_collision_prediction", "base_model:OpenGVLab/VideoMAEv2-giant", "base_model:finetune:OpenGVLab/VideoMAEv2-giant", "license:mit", "region:us" ]	video-classification	2025-05-04T07:06:30Z	--- language: en license: mit tags: - video - video-classification - videomae - collision-prediction - kaggle datasets: - nexar - nexar-ai/nexar_collision_prediction base_model: - OpenGVLab/VideoMAEv2-giant --- # VideoMAE-based Vehicle Collision Prediction Solution ## Model Description This repository contains a pretrained VideoMAEv2-giant model fine-tuned for the Nexar Safe Driving Video Analysis competition. The model is designed to predict collision and near-miss risks in driving videos. Performance: 4th place on the Kaggle public leaderboard with a score of 0.886. ## Usage The model takes video frames as input and outputs a probability score indicating the likelihood of an imminent collision or near-miss event. ```python # Example usage (pseudo-code) from transformers import VideoMAEForVideoClassification import torch model = VideoMAEForVideoClassification.from_pretrained("zhiyaowang/VideoMaev2-giant-nexar-solution") # Process video frames (16 frames recommended) frames = preprocess_video(video_path) # Shape: [1, 16, 3, 224, 224] with torch.no_grad(): outputs = model(frames) probability = torch.softmax(outputs.logits / 2.0, dim=1) # Temperature scaling T=2.0 ``` ## Model Training ### Data Processing - Frame Extraction & Timestamps: Extract frame sequences and timestamps from each video. - Sliding Window: Applied a sliding window approach with 16 frames (window size) and 2 frames (stride). - Label Assignment: Windows with their last frame within 1.5 seconds before a collision/near-miss event were labeled positive. - Data Balancing: Randomly undersampled negative
migueldeguzmandev/paperclip-falcon-rw-1b-2	migueldeguzmandev	2025-05-04T07:31:56Z	8	0	transformers	[ "transformers", "pytorch", "safetensors", "falcon", "text-generation", "custom_code", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-19T03:32:24Z	[Research Log: RLFCV2, Petertodd, the paperclip maximizer](https://www.lesswrong.com/posts/doLkRMasXMKyafJrz/research-log-rlfcv2-training-phi-1-5-gpt2xl-and-falcon-rw-1b)
ma921/gpt2-large_h_dpo_imdb_noise40_epoch5_gamma0.1	ma921	2025-05-04T07:29:25Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:ma921/gpt2-large-sft-imdb", "base_model:finetune:ma921/gpt2-large-sft-imdb", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T07:28:22Z	--- library_name: transformers license: mit base_model: ma921/gpt2-large-sft-imdb tags: - generated_from_trainer model-index: - name: gpt2-large_h_dpo_imdb_noise40_epoch5_gamma0.1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-large_h_dpo_imdb_noise40_epoch5_gamma0.1 This model is a fine-tuned version of [ma921/gpt2-large-sft-imdb](https://huggingface.co/ma921/gpt2-large-sft-imdb) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 32 - total_train_batch_size: 256 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
remy9926/mix-4	remy9926	2025-05-04T07:25:48Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T07:23:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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remy9926/mix-3	remy9926	2025-05-04T07:25:28Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T07:22:59Z	--- 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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remy9926/mix-2	remy9926	2025-05-04T07:25:22Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T07:22:44Z	--- 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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taronaeo/Qwen2.5-3B-Instruct-BE-GGUF	taronaeo	2025-05-04T07:22:40Z	8	0	transformers	[ "transformers", "gguf", "chat", "mainframe", "s390x", "z15", "z16", "z17", "big-endian", "text-generation", "en", "arxiv:2407.10671", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:quantized:Qwen/Qwen2.5-3B-Instruct", "license:other", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-03T18:47:52Z	--- license: other license_name: qwen-research license_link: https://huggingface.co/Qwen/Qwen2.5-3B-Instruct/blob/main/LICENSE language: - en pipeline_tag: text-generation base_model: Qwen/Qwen2.5-3B-Instruct base_model_relation: quantized tags: - chat - mainframe - s390x - z15 - z16 - z17 - big-endian library_name: transformers quantized_by: taronaeo --- # Qwen2.5-3B Instruct Big-Endian - GGUF (Verified for IBM Z & LinuxONE Mainframes) - Model Creator: [Qwen Team](https://huggingface.co/Qwen) - Original Model: [Qwen2.5-3B-Instruct](https://huggingface.co/qwen/Qwen2.5-3B-Instruct) ### Description This repository contains GGUF format model for [Qwen2.5-3B-Instruct](https://huggingface.co/qwen/Qwen2.5-3B-Instruct), compiled using Big-Endian. Every model has been verified to work on IBM z16 Mainframe. ### Provided Files \| Name \| Quant Method \| Bits \| Size \| Use Case \| \|------------------------------------------------------------------------------------------------------------------------------------------------\|--------------\|------\|------\|------------------------------------------------------------------------\| \| [qwen2.5-3b-instruct-be.Q3_K_S.gguf](https://huggingface.co/taronaeo/Qwen2.5-3B-Instruct-BE-GGUF/blob/main/qwen2.5-3b-instruct-be.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 1.4G \| very small, high quality loss \| \| [qwen2.5-3b-instruct-be.Q3_K_M.gguf](https://huggingface.co/taronaeo/Qwen2.5-3B-Instruct-BE-GGUF/blob/main/qwen2.5-3b-instruct-be.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 1.5G \| very small, high quality loss \| \| [qwen2.5-3b-instruct-be.Q3_K_L.gguf](https://huggingface.co/taronaeo/Qwen2.5-3B-Instruct-BE-GGUF/blob/main/qwen2.5-3b-instruct-be.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 1.6G \| small, substantial quality loss \| \| [qwen2.5-3b-instruct-be.Q4_0.gguf](https://huggingface.co/taronaeo/Qwen2.5-3B-Instruct-BE-GGUF/blob/main/qwen2.5-3b-instruct-be.Q4_0.gguf) \| Q4_0 \| 4 \| 1.7G \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [qwen2.5-3b-instruct-be.Q4_K_S.gguf](https://huggingface.co/taronaeo/Qwen2.5-3B-Instruct-BE-GGUF/blob/main/qwen2.5-3b-instruct-be.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 1.8G \| small, greater quality loss \| \| [qwen2.5-3b-instruct-be.Q4_K_M.gguf](https://huggingface.co/taronaeo/Qwen2.5-3B-Instruct-BE-GGUF/blob/main/qwen2.5-3b-instruct-be.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 1.8G \| medium, balanced quality - recommended \| \| [qwen2.5-3b-instruct-be.Q5_0.gguf](https://huggingface.co/taronaeo/Qwen2.5-3B-Instruct-BE-GGUF/blob/main/qwen2.5-3b-instruct-be.Q5_0.gguf) \| Q5_0 \| 5 \| 2.1G \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [qwen2.5-3b-instruct-be.Q5_K_S.gguf](https://huggingface.co/taronaeo/Qwen2.5-3B-Instruct-BE-GGUF/blob/main/qwen2.5-3b-instruct-be.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 2.1G \| large, low quality loss - recommended \| \| [qwen2.5-3b-instruct-be.Q5_K_M.gguf](https://huggingface.co/taronaeo/Qwen2.5-3B-Instruct-BE-GGUF/blob/main/qwen2.5-3b-instruct-be.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 2.1G \| large, very low quality loss - recommended \| \| [qwen2.5-3b-instruct-be.Q6_K.gguf](https://huggingface.co/taronaeo/Qwen2.5-3B-Instruct-BE-GGUF/blob/main/qwen2.5-3b-instruct-be.Q6_K.gguf) \| Q6_K \| 6 \| 2.4G \| very large, extremely low quality loss \| \| [qwen2.5-3b-instruct-be.Q8_0.gguf](https://huggingface.co/taronaeo/Qwen2.5-3B-Instruct-BE-GGUF/blob/main/qwen2.5-3b-instruct-be.Q8_0.gguf) \| Q8_0 \| 8 \| 3.1G \| very large, extremely low quality loss - not recommended \| # Original Model Card: Qwen2.5-3B-Instruct ## Introduction Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains. - Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots. - Long-context Support up to 128K tokens and can generate up to 8K tokens. - Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. This repo contains the instruction-tuned 3B Qwen2.5 model, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings - Number of Parameters: 3.09B - Number of Paramaters (Non-Embedding): 2.77B - Number of Layers: 36 - Number of Attention Heads (GQA): 16 for Q and 2 for KV - Context Length: Full 32,768 tokens and generation 8192 tokens For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/). ## Requirements The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.37.0`, you will encounter the following error: ``` KeyError: 'qwen2' ``` ## Quickstart Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen2.5-3B-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Give me a short introduction to large language model." messages = [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ## Evaluation & Performance Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5/). For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html). ## Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwen2.5, title = {Qwen2.5: A Party of Foundation Models}, url = {https://qwenlm.github.io/blog/qwen2.5/}, author = {Qwen Team}, month = {September}, year = {2024} } @article{qwen2, title={Qwen2 Technical Report}, author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan}, journal={arXiv preprint arXiv:2407.10671}, year={2024} } ```
Selma01/Wilkinson	Selma01	2025-05-04T07:10:01Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2025-05-04T07:10:01Z	--- license: artistic-2.0 ---
think-a-tron/raman-01-0.6B-sft	think-a-tron	2025-05-04T07:09:31Z	0	1	null	[ "safetensors", "qwen3", "en", "dataset:think-a-tron/pocket-physics", "base_model:Qwen/Qwen3-0.6B", "base_model:finetune:Qwen/Qwen3-0.6B", "license:mit", "region:us" ]	null	2025-05-04T06:58:05Z	--- license: mit datasets: - think-a-tron/pocket-physics language: - en base_model: - Qwen/Qwen3-0.6B ---
ysn-rfd/gemma3_fibonacci_tokenizer	ysn-rfd	2025-05-04T07:03:57Z	0	0	transformers	[ "transformers", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T07:03:44Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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rosalinec/dqn-SpaceInvadersNoFrameskip-v4	rosalinec	2025-05-04T07:02:12Z	8	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-05-04T07:01:54Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 53.50 +/- 45.17 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib SBX (SB3 + Jax): https://github.com/araffin/sbx Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga rosalinec -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga rosalinec -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga rosalinec ``` ## Hyperparameters ```python OrderedDict([('batch_size', 256), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
Sweaterdog/Andy-4-temp	Sweaterdog	2025-05-04T07:01:10Z	7	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "region:us" ]	null	2025-05-04T06:47:53Z	--- base_model: unsloth/qwen3-8b-bnb-4bit library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.0
davidheineman/colbert-acl	davidheineman	2025-05-04T07:01:04Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-04-14T14:51:12Z	--- license: apache-2.0 --- This is a dataset of 100K+ ML & NLP abstracts with a pre-built indexed using [colbert-ir/colbertv2.0](https://huggingface.co/colbert-ir/colbertv2.0). A deployed version of this index is at [github.com/davidheineman/acl-search](https://github.com/davidheineman/acl-search).
moyixiao/llama3_droa64_merge	moyixiao	2025-05-04T06:48:09Z	2	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T06:46:35Z	--- library_name: transformers tags: - llama-factory --- # 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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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. 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ASethi04/meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-0.001	ASethi04	2025-05-04T06:38:52Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "endpoints_compatible", "region:us" ]	null	2025-05-04T02:49:17Z	--- base_model: meta-llama/Llama-3.1-8B library_name: transformers model_name: meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-0.001 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-0.001 This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="ASethi04/meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-0.001", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/torchql-org/huggingface/runs/3r6mawt9) This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
berenbaum/model	berenbaum	2025-05-04T06:36:17Z	0	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "qwen3", "en", "base_model:unsloth/Qwen3-14B", "base_model:finetune:unsloth/Qwen3-14B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-04T06:36:16Z	--- base_model: unsloth/Qwen3-14B tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: berenbaum - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-14B This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Kenazin/Llama-3.1-8B-peft-v6-10	Kenazin	2025-05-04T06:28:33Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T06:28:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Kenazin/Llama-3.1-8B-peft-v6-8	Kenazin	2025-05-04T06:28:14Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T06:28:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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JatinkInnovision/ComFit4	JatinkInnovision	2025-05-04T06:27:57Z	2	0	null	[ "gguf", "llama", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T06:18:36Z	--- license: apache-2.0 ---
mohsed/Basalam	mohsed	2025-05-04T06:22:21Z	0	0	null	[ "fa", "license:apache-2.0", "region:us" ]	null	2025-05-04T06:21:26Z	--- license: apache-2.0 license_name: bslmblog license_link: LICENSE language: - fa ---
HoaDoan1710/whisper-checkpoint-4525	HoaDoan1710	2025-05-04T06:13:35Z	0	0	null	[ "safetensors", "whisper", "license:apache-2.0", "region:us" ]	null	2025-05-04T06:04:08Z	--- license: apache-2.0 ---
DevQuasar/kyutai.helium-1-2b-science-GGUF	DevQuasar	2025-05-04T06:09:18Z	0	0	null	[ "gguf", "text-generation", "base_model:kyutai/helium-1-2b-science", "base_model:quantized:kyutai/helium-1-2b-science", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T05:55:33Z	--- base_model: - kyutai/helium-1-2b-science pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [kyutai/helium-1-2b-science](https://huggingface.co/kyutai/helium-1-2b-science) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
mlfoundations-dev/e1_science_longest_qwq	mlfoundations-dev	2025-05-04T06:07:16Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T02:13:32Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: e1_science_longest_qwq 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. --> # e1_science_longest_qwq This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/e1_science_longest_qwq 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: 32 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - total_eval_batch_size: 256 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.3.0 - Datasets 3.1.0 - Tokenizers 0.20.3
RichardErkhov/tsavage68_-_IE_L3_350steps_1e8rate_03beta_cSFTDPO-gguf	RichardErkhov	2025-05-04T06:06:28Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T02:59:08Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) IE_L3_350steps_1e8rate_03beta_cSFTDPO - GGUF - Model creator: https://huggingface.co/tsavage68/ - Original model: https://huggingface.co/tsavage68/IE_L3_350steps_1e8rate_03beta_cSFTDPO/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q2_K.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_350steps_1e8rate_03beta_cSFTDPO-gguf/blob/main/IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q2_K.gguf) \| Q2_K \| 2.96GB \| \| [IE_L3_350steps_1e8rate_03beta_cSFTDPO.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_350steps_1e8rate_03beta_cSFTDPO-gguf/blob/main/IE_L3_350steps_1e8rate_03beta_cSFTDPO.IQ3_XS.gguf) \| IQ3_XS \| 3.28GB \| \| [IE_L3_350steps_1e8rate_03beta_cSFTDPO.IQ3_S.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_350steps_1e8rate_03beta_cSFTDPO-gguf/blob/main/IE_L3_350steps_1e8rate_03beta_cSFTDPO.IQ3_S.gguf) \| IQ3_S \| 3.43GB \| \| [IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_350steps_1e8rate_03beta_cSFTDPO-gguf/blob/main/IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q3_K_S.gguf) \| Q3_K_S \| 3.41GB \| \| [IE_L3_350steps_1e8rate_03beta_cSFTDPO.IQ3_M.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_350steps_1e8rate_03beta_cSFTDPO-gguf/blob/main/IE_L3_350steps_1e8rate_03beta_cSFTDPO.IQ3_M.gguf) \| IQ3_M \| 3.52GB \| \| [IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q3_K.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_350steps_1e8rate_03beta_cSFTDPO-gguf/blob/main/IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q3_K.gguf) \| Q3_K \| 3.74GB \| \| [IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_350steps_1e8rate_03beta_cSFTDPO-gguf/blob/main/IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q3_K_M.gguf) \| Q3_K_M \| 3.74GB \| \| [IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_350steps_1e8rate_03beta_cSFTDPO-gguf/blob/main/IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q3_K_L.gguf) \| Q3_K_L \| 4.03GB \| \| [IE_L3_350steps_1e8rate_03beta_cSFTDPO.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_350steps_1e8rate_03beta_cSFTDPO-gguf/blob/main/IE_L3_350steps_1e8rate_03beta_cSFTDPO.IQ4_XS.gguf) \| IQ4_XS \| 4.18GB \| \| [IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q4_0.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_350steps_1e8rate_03beta_cSFTDPO-gguf/blob/main/IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q4_0.gguf) \| Q4_0 \| 4.34GB \| \| [IE_L3_350steps_1e8rate_03beta_cSFTDPO.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_350steps_1e8rate_03beta_cSFTDPO-gguf/blob/main/IE_L3_350steps_1e8rate_03beta_cSFTDPO.IQ4_NL.gguf) \| IQ4_NL \| 4.38GB \| \| [IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_350steps_1e8rate_03beta_cSFTDPO-gguf/blob/main/IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q4_K_S.gguf) \| Q4_K_S \| 4.37GB \| \| [IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q4_K.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_350steps_1e8rate_03beta_cSFTDPO-gguf/blob/main/IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q4_K.gguf) \| Q4_K \| 4.58GB \| \| [IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_350steps_1e8rate_03beta_cSFTDPO-gguf/blob/main/IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q4_K_M.gguf) \| Q4_K_M \| 4.58GB \| \| [IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q4_1.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_350steps_1e8rate_03beta_cSFTDPO-gguf/blob/main/IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q4_1.gguf) \| Q4_1 \| 4.78GB \| \| [IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q5_0.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_350steps_1e8rate_03beta_cSFTDPO-gguf/blob/main/IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q5_0.gguf) \| Q5_0 \| 5.21GB \| \| [IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_350steps_1e8rate_03beta_cSFTDPO-gguf/blob/main/IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q5_K_S.gguf) \| Q5_K_S \| 5.21GB \| \| [IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q5_K.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_350steps_1e8rate_03beta_cSFTDPO-gguf/blob/main/IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q5_K.gguf) \| Q5_K \| 5.34GB \| \| [IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_350steps_1e8rate_03beta_cSFTDPO-gguf/blob/main/IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q5_K_M.gguf) \| Q5_K_M \| 5.34GB \| \| [IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q5_1.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_350steps_1e8rate_03beta_cSFTDPO-gguf/blob/main/IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q5_1.gguf) \| Q5_1 \| 5.65GB \| \| [IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q6_K.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_350steps_1e8rate_03beta_cSFTDPO-gguf/blob/main/IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q6_K.gguf) \| Q6_K \| 6.14GB \| \| [IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q8_0.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_350steps_1e8rate_03beta_cSFTDPO-gguf/blob/main/IE_L3_350steps_1e8rate_03beta_cSFTDPO.Q8_0.gguf) \| Q8_0 \| 7.95GB \| Original model description: --- library_name: transformers license: llama3 base_model: tsavage68/IE_L3_1000steps_1e6rate_SFT tags: - trl - dpo - generated_from_trainer model-index: - name: IE_L3_350steps_1e8rate_03beta_cSFTDPO 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. --> # IE_L3_350steps_1e8rate_03beta_cSFTDPO This model is a fine-tuned version of [tsavage68/IE_L3_1000steps_1e6rate_SFT](https://huggingface.co/tsavage68/IE_L3_1000steps_1e6rate_SFT) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6896 - Rewards/chosen: -0.0071 - Rewards/rejected: -0.0198 - Rewards/accuracies: 0.4400 - Rewards/margins: 0.0127 - Logps/rejected: -75.6932 - Logps/chosen: -82.8214 - Logits/rejected: -0.7977 - Logits/chosen: -0.7408 ## 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-08 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 350 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6912 \| 0.4 \| 50 \| 0.6940 \| -0.0075 \| -0.0104 \| 0.4000 \| 0.0029 \| -75.6618 \| -82.8226 \| -0.7964 \| -0.7393 \| \| 0.6947 \| 0.8 \| 100 \| 0.6925 \| 0.0014 \| -0.0057 \| 0.3850 \| 0.0070 \| -75.6461 \| -82.7931 \| -0.7963 \| -0.7394 \| \| 0.6881 \| 1.2 \| 150 \| 0.7003 \| -0.0102 \| -0.0020 \| 0.375 \| -0.0082 \| -75.6340 \| -82.8318 \| -0.7969 \| -0.7398 \| \| 0.6776 \| 1.6 \| 200 \| 0.6938 \| -0.0057 \| -0.0098 \| 0.375 \| 0.0041 \| -75.6601 \| -82.8168 \| -0.7970 \| -0.7399 \| \| 0.6859 \| 2.0 \| 250 \| 0.6850 \| -0.0033 \| -0.0250 \| 0.4350 \| 0.0217 \| -75.7105 \| -82.8087 \| -0.7975 \| -0.7405 \| \| 0.7024 \| 2.4 \| 300 \| 0.6893 \| -0.0075 \| -0.0207 \| 0.4400 \| 0.0132 \| -75.6964 \| -82.8228 \| -0.7977 \| -0.7408 \| \| 0.6802 \| 2.8 \| 350 \| 0.6896 \| -0.0071 \| -0.0198 \| 0.4400 \| 0.0127 \| -75.6932 \| -82.8214 \| -0.7977 \| -0.7408 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.0.0+cu117 - Datasets 3.0.0 - Tokenizers 0.19.1
Lahinthefutureland/wan-toffee	Lahinthefutureland	2025-05-04T06:06:14Z	0	1	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-09T19:32:49Z	--- license: apache-2.0 ---
mlfoundations-dev/d1_math_mc_llm_10k	mlfoundations-dev	2025-05-04T06:05:03Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T12:45:00Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: d1_math_mc_llm_10k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # d1_math_mc_llm_10k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/d1_math_mc_llm_10k 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: 4 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - total_eval_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
Nasanbuyan/mongolian-gpt2-lora	Nasanbuyan	2025-05-04T05:53:50Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T05:53:37Z	--- 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]
hanaearg/emo-Llama3.2Dev15	hanaearg	2025-05-04T05:53:03Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-04T05:52:57Z	--- base_model: unsloth/llama-3.2-3b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: hanaearg - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ail-sa/akshey_stockyplus_long_fs_v2	ail-sa	2025-05-04T05:52:05Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-04T05:10:29Z	--- 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: Sid --- # Akshey_Stockyplus_Long_Fs_V2 <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 `Sid` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Sid", "lora_weights": "https://huggingface.co/ail-sa/akshey_stockyplus_long_fs_v2/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('ail-sa/akshey_stockyplus_long_fs_v2', weight_name='lora.safetensors') image = pipeline('Sid').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/ail-sa/akshey_stockyplus_long_fs_v2/discussions) to add images that show off what you’ve made with this LoRA.
jdchang/full-with-label-bs-1024-sg-2-step-7290	jdchang	2025-05-04T05:47:39Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-05-04T05:47:30Z	--- 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. 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echogarden/echogarden-packages	echogarden	2025-05-04T05:46:34Z	0	1	null	[ "region:us" ]	null	2023-07-18T16:12:02Z	This is the package repository for [Echogarden](https://github.com/echogarden-project/echogarden) - a fully open-source, user-oriented speech system, written in TypeScript and running over the Node.js runtime. The repository contains speech synthesis and recognition models for the various engines Echogarden supports, along with other related speech models, such as language classification and voice activity detection, as well as binaries for a few tools it uses internally. The models are individually packaged in `.tar.gz` archives to ensure a smooth and robust installation process. ## Licensing All content is freely distributable, with varying licenses: * Flite voices (`flite-`): [BSD License](https://github.com/festvox/flite/blob/master/COPYING) * SVOX Pico resources (`pico-`): [Apache License 2.0](https://github.com/gmn/nanotts/blob/master/LICENSE) * Silero VAD (`silero-vad`) and Silero language classifier (`silero-lang-classifier-95`): [MIT License](https://github.com/snakers4/silero-vad/blob/master/LICENSE) * Silero speech recognition models (`silero-en-`, `silero-de-`, `silero-es-`, `silero-ua-`): [BY-NC-SA](https://github.com/snakers4/silero-models/blob/master/LICENSE) * VITS pre-trained models, exported to the ONNX runtime (`vits-`): licensed under various creative commons licenses: [CC0](https://creativecommons.org/share-your-work/public-domain/cc0/), [CC-BY](https://creativecommons.org/licenses/by/4.0/) and [BY-NC-SA](https://creativecommons.org/licenses/by-nc-sa/4.0/), and few are public domain (you can view the individual license for each model in the model cards on the [samples page](https://rhasspy.github.io/piper-samples/)). The [Piper system](https://github.com/rhasspy/piper) itself is published under the [MIT License](https://github.com/rhasspy/piper/blob/master/LICENSE.md) * Whisper pre-trained models, exported to the ONNX runtime (`whisper-`): [MIT License](https://github.com/openai/whisper/blob/main/LICENSE) Tool binary distributions: * FFMpeg: [LGPL, GPL v2 and GPL v3 Licenses](https://github.com/FFmpeg/FFmpeg) * SoX: [GPL v2 License](https://github.com/chirlu/sox/blob/master/LICENSE.GPL)
loris3/babylm_2024_10m_curriculum_llama_llama_incr_influence_epoch_repetition	loris3	2025-05-04T05:45:51Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T19:28:57Z	--- 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. 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loris3/stratified_equitoken_10m_curriculum_llama_llama_incr_influence_epoch_repetition	loris3	2025-05-04T05:45:17Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T20:15:03Z	--- 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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my2000cup/Gaia-Petro-LLM	my2000cup	2025-05-04T05:42:12Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen3-1.7B", "base_model:finetune:Qwen/Qwen3-1.7B", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-02T14:41:50Z	--- library_name: transformers license: other base_model: Qwen/Qwen3-1.7B tags: - llama-factory - full - generated_from_trainer model-index: - name: train_2025-05-02-18-36-44 results: [] --- # train_2025-05-02-18-36-44 This model is a fine-tuned version of [../pretrained/Qwen3-1.7B](https://huggingface.co/../pretrained/Qwen3-1.7B) on the wikipedia_zh and the petro_books datasets. ## Model description Gaia-Petro-LLM is a large language model specialized in the oil and gas industry, fine-tuned from Qwen/Qwen3-1.7B. It was further pre-trained on a curated 20GB corpus of petroleum engineering texts, including technical documents, academic papers, and domain literature. The model is designed to support domain experts, researchers, and engineers in petroleum-related tasks, providing high-quality, domain-specific language understanding and generation. ## Model Details Base Model: Qwen/Qwen3-1.7B Domain: Oil & Gas / Petroleum Engineering Corpus Size: ~20GB (petroleum engineering) Languages: Primarily Chinese; domain-specific English supported Repository: my2000cup/Gaia-Petro-LLM ## Intended uses & limitations Technical Q&A in petroleum engineering Document summarization for oil & gas reports Knowledge extraction from unstructured domain texts Education & training in oil & gas technologies Not suitable for general domain tasks outside oil & gas. May not be up to date with the latest industry developments (post-2023). Not to be used for critical, real-time decision-making without expert review. ## Training and evaluation data The model was further pre-trained on an in-house text corpus (~20GB) collected from: Wikipedia (Chinese, petroleum-related entries) Open petroleum engineering books and literature Technical standards and manuals ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer # Replace with your model repository model_name = "my2000cup/Gaia-Petro-LLM" # Load tokenizer and model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # Prepare a petroleum engineering prompt prompt = "What are the main challenges in enhanced oil recovery (EOR) methods?" messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # Optional: enables model's 'thinking' mode ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # Generate the model's response generated_ids = model.generate( **model_inputs, max_new_tokens=1024 # adjust as needed ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() # Optional: parse 'thinking' content, if your template uses it try: # Find the index of the </think> token (ID may differ in your tokenizer!) think_token_id = 151668 # double-check this ID in your tokenizer index = len(output_ids) - output_ids[::-1].index(think_token_id) except ValueError: index = 0 thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") print("Thinking content:", thinking_content) print("Answer:", content) ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - 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_steps: 16 - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
49Simoney/SpanishLanguageTeacher-7B-3k-merged	49Simoney	2025-05-04T05:31:43Z	0	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:OpenBuddy/openbuddy-qwen2.5llamaify-7b-v23.1-200k", "base_model:finetune:OpenBuddy/openbuddy-qwen2.5llamaify-7b-v23.1-200k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T05:09:37Z	--- base_model: OpenBuddy/openbuddy-qwen2.5llamaify-7b-v23.1-200k tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: 49Simoney - License: apache-2.0 - Finetuned from model : OpenBuddy/openbuddy-qwen2.5llamaify-7b-v23.1-200k 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)
DevQuasar/kyutai.helium-1-2b-hum-GGUF	DevQuasar	2025-05-04T05:28:06Z	0	0	null	[ "gguf", "text-generation", "base_model:kyutai/helium-1-2b-hum", "base_model:quantized:kyutai/helium-1-2b-hum", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T05:14:07Z	--- base_model: - kyutai/helium-1-2b-hum pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [kyutai/helium-1-2b-hum](https://huggingface.co/kyutai/helium-1-2b-hum) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
NewEden/GLM-v3-lora	NewEden	2025-05-04T05:25:52Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:THUDM/GLM-4-32B-0414", "base_model:adapter:THUDM/GLM-4-32B-0414", "region:us" ]	null	2025-05-04T05:25:34Z	--- base_model: THUDM/GLM-4-32B-0414 library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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mradermacher/Rei-V3-KTO-12B-GGUF	mradermacher	2025-05-04T05:25:25Z	0	1	transformers	[ "transformers", "gguf", "roleplay", "storywriting", "axolotl", "text-generation-inference", "finetune", "en", "dataset:NewEden/KTO-IF-Dans", "dataset:NewEden/Opus-accepted-hermes-rejected-shuffled", "dataset:NewEden/KTO-Instruct-Mix", "dataset:NewEden/Purpura-Arkhaios-CC-KTO", "base_model:Delta-Vector/Rei-V3-KTO-12B", "base_model:quantized:Delta-Vector/Rei-V3-KTO-12B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T20:58:03Z	--- base_model: Delta-Vector/Rei-V3-KTO-12B datasets: - NewEden/KTO-IF-Dans - NewEden/Opus-accepted-hermes-rejected-shuffled - NewEden/KTO-Instruct-Mix - NewEden/Purpura-Arkhaios-CC-KTO language: - en library_name: transformers quantized_by: mradermacher tags: - roleplay - storywriting - axolotl - text-generation-inference - finetune --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Delta-Vector/Rei-V3-KTO-12B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Rei-V3-KTO-12B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Rei-V3-KTO-12B-GGUF/resolve/main/Rei-V3-KTO-12B.Q2_K.gguf) \| Q2_K \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rei-V3-KTO-12B-GGUF/resolve/main/Rei-V3-KTO-12B.Q3_K_S.gguf) \| Q3_K_S \| 5.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rei-V3-KTO-12B-GGUF/resolve/main/Rei-V3-KTO-12B.Q3_K_M.gguf) \| Q3_K_M \| 6.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Rei-V3-KTO-12B-GGUF/resolve/main/Rei-V3-KTO-12B.Q3_K_L.gguf) \| Q3_K_L \| 6.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rei-V3-KTO-12B-GGUF/resolve/main/Rei-V3-KTO-12B.IQ4_XS.gguf) \| IQ4_XS \| 6.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rei-V3-KTO-12B-GGUF/resolve/main/Rei-V3-KTO-12B.Q4_K_S.gguf) \| Q4_K_S \| 7.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Rei-V3-KTO-12B-GGUF/resolve/main/Rei-V3-KTO-12B.Q4_K_M.gguf) \| Q4_K_M \| 7.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Rei-V3-KTO-12B-GGUF/resolve/main/Rei-V3-KTO-12B.Q5_K_S.gguf) \| Q5_K_S \| 8.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rei-V3-KTO-12B-GGUF/resolve/main/Rei-V3-KTO-12B.Q5_K_M.gguf) \| Q5_K_M \| 8.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Rei-V3-KTO-12B-GGUF/resolve/main/Rei-V3-KTO-12B.Q6_K.gguf) \| Q6_K \| 10.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Rei-V3-KTO-12B-GGUF/resolve/main/Rei-V3-KTO-12B.Q8_0.gguf) \| Q8_0 \| 13.1 \| 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 -->
mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF	mradermacher	2025-05-04T05:25:25Z	179	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Nexesenex/Llama_3.x_70b_Nemdohertess_v2.0", "base_model:quantized:Nexesenex/Llama_3.x_70b_Nemdohertess_v2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-23T01:36:07Z	--- base_model: Nexesenex/Llama_3.x_70b_Nemdohertess_v2.0 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Nexesenex/Llama_3.x_70b_Nemdohertess_v2.0 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q2_K.gguf) \| Q2_K \| 26.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q3_K_S.gguf) \| Q3_K_S \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q3_K_M.gguf) \| Q3_K_M \| 34.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q3_K_L.gguf) \| Q3_K_L \| 37.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.IQ4_XS.gguf) \| IQ4_XS \| 38.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q4_K_S.gguf) \| Q4_K_S \| 40.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q4_K_M.gguf) \| Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q5_K_S.gguf) \| Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q5_K_M.gguf) \| Q5_K_M \| 50.1 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q6_K.gguf.part2of2) \| Q6_K \| 58.0 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama_3.x_70b_Nemdohertess_v2.0-GGUF/resolve/main/Llama_3.x_70b_Nemdohertess_v2.0.Q8_0.gguf.part2of2) \| Q8_0 \| 75.1 \| 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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
kunainakano/gensyn-checkpoints-beaked_tangled_caribou	kunainakano	2025-05-04T05:23:19Z	2	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am beaked tangled caribou", "unsloth", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-25T07:37:55Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: gensyn-checkpoints-beaked_tangled_caribou tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am beaked tangled caribou - unsloth - trl licence: license --- # Model Card for gensyn-checkpoints-beaked_tangled_caribou This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="kunainakano/gensyn-checkpoints-beaked_tangled_caribou", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
vost/realismByStableYogi_sd15V9	vost	2025-05-04T05:22:39Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-05-04T05:22:10Z	--- license: other language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image --- Converted from [https://civitai.com/api/download/models/1223643?type=Model&format=SafeTensor&size=pruned&fp=fp16](https://civitai.com/api/download/models/1223643?type=Model&format=SafeTensor&size=pruned&fp=fp16).
sdfsdsssFHarry/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-durable_furry_chicken	sdfsdsssFHarry	2025-05-04T05:21:51Z	3	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am durable furry chicken", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-22T12:05:55Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-durable_furry_chicken tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am durable furry chicken - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-durable_furry_chicken 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="sdfsdsssFHarry/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-durable_furry_chicken", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
d03tkg2/gensyn-checkpoints-hulking_striped_toucan	d03tkg2	2025-05-04T05:15:15Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am hulking striped toucan", "unsloth", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-25T07:36:57Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: gensyn-checkpoints-hulking_striped_toucan tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am hulking striped toucan - unsloth - trl licence: license --- # Model Card for gensyn-checkpoints-hulking_striped_toucan This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="d03tkg2/gensyn-checkpoints-hulking_striped_toucan", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
flyingbugs/Qwen2.5-instruct-7B-openr1-math-edge	flyingbugs	2025-05-04T05:13:02Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "sft", "conversational", "dataset:flyingbugs/OpenR1-Math-220k-pruned-keep-0.5-end-start-0.5", "base_model:flyingbugs/Qwen2.5-7B-Instruct", "base_model:finetune:flyingbugs/Qwen2.5-7B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-02T01:09:47Z	--- base_model: flyingbugs/Qwen2.5-7B-Instruct datasets: flyingbugs/OpenR1-Math-220k-pruned-keep-0.5-end-start-0.5 library_name: transformers model_name: Qwen2.5-instruct-7B-openr1-math-edge tags: - generated_from_trainer - open-r1 - trl - sft licence: license --- # Model Card for Qwen2.5-instruct-7B-openr1-math-edge This model is a fine-tuned version of [flyingbugs/Qwen2.5-7B-Instruct](https://huggingface.co/flyingbugs/Qwen2.5-7B-Instruct) on the [flyingbugs/OpenR1-Math-220k-pruned-keep-0.5-end-start-0.5](https://huggingface.co/datasets/flyingbugs/OpenR1-Math-220k-pruned-keep-0.5-end-start-0.5) 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="flyingbugs/Qwen2.5-instruct-7B-openr1-math-edge", 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/jjh233/huggingface/runs/zlpmm5j7) This model was trained with SFT. ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf	RichardErkhov	2025-05-04T05:12:08Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T01:53:42Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) IE_L3_1000steps_1e6rate_SFT - GGUF - Model creator: https://huggingface.co/tsavage68/ - Original model: https://huggingface.co/tsavage68/IE_L3_1000steps_1e6rate_SFT/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [IE_L3_1000steps_1e6rate_SFT.Q2_K.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q2_K.gguf) \| Q2_K \| 2.96GB \| \| [IE_L3_1000steps_1e6rate_SFT.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.IQ3_XS.gguf) \| IQ3_XS \| 3.28GB \| \| [IE_L3_1000steps_1e6rate_SFT.IQ3_S.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.IQ3_S.gguf) \| IQ3_S \| 3.43GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q3_K_S.gguf) \| Q3_K_S \| 3.41GB \| \| [IE_L3_1000steps_1e6rate_SFT.IQ3_M.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.IQ3_M.gguf) \| IQ3_M \| 3.52GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q3_K.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q3_K.gguf) \| Q3_K \| 3.74GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q3_K_M.gguf) \| Q3_K_M \| 3.74GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q3_K_L.gguf) \| Q3_K_L \| 4.03GB \| \| [IE_L3_1000steps_1e6rate_SFT.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.IQ4_XS.gguf) \| IQ4_XS \| 4.18GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q4_0.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q4_0.gguf) \| Q4_0 \| 4.34GB \| \| [IE_L3_1000steps_1e6rate_SFT.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.IQ4_NL.gguf) \| IQ4_NL \| 4.38GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q4_K_S.gguf) \| Q4_K_S \| 4.37GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q4_K.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q4_K.gguf) \| Q4_K \| 4.58GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q4_K_M.gguf) \| Q4_K_M \| 4.58GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q4_1.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q4_1.gguf) \| Q4_1 \| 4.78GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q5_0.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q5_0.gguf) \| Q5_0 \| 5.21GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q5_K_S.gguf) \| Q5_K_S \| 5.21GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q5_K.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q5_K.gguf) \| Q5_K \| 5.34GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q5_K_M.gguf) \| Q5_K_M \| 5.34GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q5_1.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q5_1.gguf) \| Q5_1 \| 5.65GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q6_K.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q6_K.gguf) \| Q6_K \| 6.14GB \| \| [IE_L3_1000steps_1e6rate_SFT.Q8_0.gguf](https://huggingface.co/RichardErkhov/tsavage68_-_IE_L3_1000steps_1e6rate_SFT-gguf/blob/main/IE_L3_1000steps_1e6rate_SFT.Q8_0.gguf) \| Q8_0 \| 7.95GB \| Original model description: --- library_name: transformers license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags: - trl - sft - generated_from_trainer model-index: - name: IE_L3_1000steps_1e6rate_SFT 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. --> # IE_L3_1000steps_1e6rate_SFT This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6162 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.8795 \| 0.4 \| 50 \| 1.7359 \| \| 1.5557 \| 0.8 \| 100 \| 1.5149 \| \| 1.5505 \| 1.2 \| 150 \| 1.4878 \| \| 1.4839 \| 1.6 \| 200 \| 1.4811 \| \| 1.4928 \| 2.0 \| 250 \| 1.4778 \| \| 1.3677 \| 2.4 \| 300 \| 1.4931 \| \| 1.3947 \| 2.8 \| 350 \| 1.4940 \| \| 1.1632 \| 3.2 \| 400 \| 1.5277 \| \| 1.2544 \| 3.6 \| 450 \| 1.5207 \| \| 1.147 \| 4.0 \| 500 \| 1.5292 \| \| 1.1403 \| 4.4 \| 550 \| 1.5664 \| \| 1.0704 \| 4.8 \| 600 \| 1.5711 \| \| 1.0585 \| 5.2 \| 650 \| 1.6079 \| \| 1.0515 \| 5.6 \| 700 \| 1.6006 \| \| 0.9566 \| 6.0 \| 750 \| 1.6039 \| \| 0.9733 \| 6.4 \| 800 \| 1.6169 \| \| 0.9837 \| 6.8 \| 850 \| 1.6162 \| \| 0.9766 \| 7.2 \| 900 \| 1.6158 \| \| 0.924 \| 7.6 \| 950 \| 1.6164 \| \| 1.0258 \| 8.0 \| 1000 \| 1.6162 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.0.0+cu117 - Datasets 3.0.0 - Tokenizers 0.19.1
JorG941/unsloth_test	JorG941	2025-05-04T05:10:45Z	0	0	null	[ "gguf", "llama", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T05:00:48Z	--- license: apache-2.0 ---
John6666/circusmix-v40-sdxl	John6666	2025-05-04T05:05:00Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "cartoon", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-xl-early-release-v0", "base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-05-04T04:59:09Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - cartoon - illustrious base_model: OnomaAIResearch/Illustrious-xl-early-release-v0 --- Original model is [here](https://civitai.com/models/1094522/circusmix?modelVersionId=1730627). This model created by [xJollyboyx](https://civitai.com/user/xJollyboyx).
Kudod/roberta-mlm-model-v2.0	Kudod	2025-05-04T05:04:54Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-05-03T08:11:31Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: roberta-mlm-model-v2.0 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. --> # roberta-mlm-model-v2.0 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 8.4338 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:-----:\|:---------------:\| \| 3.843 \| 0.8315 \| 10000 \| 3.5701 \| \| 3.1692 \| 1.6631 \| 20000 \| 2.9912 \| \| 7.9502 \| 2.4946 \| 30000 \| 15.9249 \| \| 7.9357 \| 3.3261 \| 40000 \| 11.0789 \| \| 7.9288 \| 4.1577 \| 50000 \| 8.4193 \| \| 7.9244 \| 4.9892 \| 60000 \| 8.4338 \| ### Framework versions - Transformers 4.50.0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.1
varunsingh2191/open-llama3b-finetuned-lora	varunsingh2191	2025-05-04T05:04:31Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T05:04:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
vmpsergio/b1dd8e5a-bcfa-455b-b048-ab692bc5dcea	vmpsergio	2025-05-04T04:49:38Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen1.5-7B", "base_model:adapter:Qwen/Qwen1.5-7B", "license:other", "8-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T04:37:18Z	--- library_name: peft license: other base_model: Qwen/Qwen1.5-7B tags: - axolotl - generated_from_trainer model-index: - name: b1dd8e5a-bcfa-455b-b048-ab692bc5dcea 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: Qwen/Qwen1.5-7B bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 91bfbfdbea09c1d9_train_data.json ds_type: json format: custom path: /workspace/input_data/91bfbfdbea09c1d9_train_data.json type: field_input: body field_instruction: title field_output: dominant_topic_name format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: vmpsergio/b1dd8e5a-bcfa-455b-b048-ab692bc5dcea hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/91bfbfdbea09c1d9_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 9b5d3f76-d3c0-43b4-9583-52754ea37d90 wandb_project: s56-2 wandb_run: your_name wandb_runid: 9b5d3f76-d3c0-43b4-9583-52754ea37d90 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # b1dd8e5a-bcfa-455b-b048-ab692bc5dcea This model is a fine-tuned version of [Qwen/Qwen1.5-7B](https://huggingface.co/Qwen/Qwen1.5-7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6497 ## 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: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.7154 \| 0.0688 \| 200 \| 0.6497 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
kokovova/e8e0cff8-f691-4f95-820a-2d1c20fdeaaa	kokovova	2025-05-04T04:44:04Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2-0.5B", "base_model:adapter:unsloth/Qwen2-0.5B", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T04:42:10Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2-0.5B tags: - axolotl - generated_from_trainer model-index: - name: e8e0cff8-f691-4f95-820a-2d1c20fdeaaa 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Qwen2-0.5B bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - cccd8bfc08aa015e_train_data.json ds_type: json format: custom path: /workspace/input_data/cccd8bfc08aa015e_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: kokovova/e8e0cff8-f691-4f95-820a-2d1c20fdeaaa hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/cccd8bfc08aa015e_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 1eed07a3-09fb-4f94-94e6-3315f2bfa239 wandb_project: s56-4 wandb_run: your_name wandb_runid: 1eed07a3-09fb-4f94-94e6-3315f2bfa239 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # e8e0cff8-f691-4f95-820a-2d1c20fdeaaa This model is a fine-tuned version of [unsloth/Qwen2-0.5B](https://huggingface.co/unsloth/Qwen2-0.5B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4382 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.7531 \| 0.0532 \| 200 \| 1.4382 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
DevQuasar/Qwen.Qwen3-1.7B-GGUF	DevQuasar	2025-05-04T04:41:55Z	187	0	null	[ "gguf", "text-generation", "base_model:Qwen/Qwen3-1.7B", "base_model:quantized:Qwen/Qwen3-1.7B", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-29T11:54:04Z	--- base_model: - Qwen/Qwen3-1.7B pipeline_tag: text-generation --- ## LMStudio users! Please update the chat prompt template of the model. Go to My models -> Actions (gear) edit model default parameters -> Prompt -> Prompt template. Update the Jinja template. Correct JINJA: ``` {%- if tools %} {{- '<\|im_start\|>system\n' }} {%- if messages[0].role == 'system' %} {{- messages[0].content + '\n\n' }} {%- endif %} {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }} {%- for tool in tools %} {{- "\n" }} {{- tool \| tojson }} {%- endfor %} {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><\|im_end\|>\n" }} {%- else %} {%- if messages[0].role == 'system' %} {{- '<\|im_start\|>system\n' + messages[0].content + '<\|im_end\|>\n' }} {%- endif %} {%- endif %} {%- set ns = namespace(multi_step_tool=true, last_query_index=messages\|length - 1) %} {%- for message in messages[::-1] %} {%- set index = (messages\|length - 1) - loop.index0 %} {%- set tool_start = "<tool_response>" %} {%- set tool_start_length = tool_start\|length %} {%- set start_of_message = message.content[:tool_start_length] %} {%- set tool_end = "</tool_response>" %} {%- set tool_end_length = tool_end\|length %} {%- set start_pos = (message.content\|length) - tool_end_length %} {%- if start_pos < 0 %} {%- set start_pos = 0 %} {%- endif %} {%- set end_of_message = message.content[start_pos:] %} {%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %} {%- set ns.multi_step_tool = false %} {%- set ns.last_query_index = index %} {%- endif %} {%- endfor %} {%- for message in messages %} {%- if (message.role == "user") or (message.role == "system" and not loop.first) %} {{- '<\|im_start\|>' + message.role + '\n' + message.content + '<\|im_end\|>' + '\n' }} {%- elif message.role == "assistant" %} {%- set content = message.content %} {%- set reasoning_content = '' %} {%- if message.reasoning_content is defined and message.reasoning_content is not none %} {%- set reasoning_content = message.reasoning_content %} {%- else %} {%- if '</think>' in message.content %} {%- set content = (message.content.split('</think>')\|last).lstrip('\n') %} {%- set reasoning_content = (message.content.split('</think>')\|first).rstrip('\n') %} {%- set reasoning_content = (reasoning_content.split('<think>')\|last).lstrip('\n') %} {%- endif %} {%- endif %} {%- if loop.index0 > ns.last_query_index %} {%- if loop.last or (not loop.last and reasoning_content) %} {{- '<\|im_start\|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }} {%- else %} {{- '<\|im_start\|>' + message.role + '\n' + content }} {%- endif %} {%- else %} {{- '<\|im_start\|>' + message.role + '\n' + content }} {%- endif %} {%- if message.tool_calls %} {%- for tool_call in message.tool_calls %} {%- if (loop.first and content) or (not loop.first) %} {{- '\n' }} {%- endif %} {%- if tool_call.function %} {%- set tool_call = tool_call.function %} {%- endif %} {{- '<tool_call>\n{"name": "' }} {{- tool_call.name }} {{- '", "arguments": ' }} {%- if tool_call.arguments is string %} {{- tool_call.arguments }} {%- else %} {{- tool_call.arguments \| tojson }} {%- endif %} {{- '}\n</tool_call>' }} {%- endfor %} {%- endif %} {{- '<\|im_end\|>\n' }} {%- elif message.role == "tool" %} {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} {{- '<\|im_start\|>user' }} {%- endif %} {{- '\n<tool_response>\n' }} {{- message.content }} {{- '\n</tool_response>' }} {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} {{- '<\|im_end\|>\n' }} {%- endif %} {%- endif %} {%- endfor %} {%- if add_generation_prompt %} {{- '<\|im_start\|>assistant\n' }} {%- if enable_thinking is defined and enable_thinking is false %} {{- '<think>\n\n</think>\n\n' }} {%- endif %} {%- endif %} ``` [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
vermoney/3e18320f-3309-4bea-879f-561458bc9a71	vermoney	2025-05-04T04:41:38Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2-0.5B", "base_model:adapter:unsloth/Qwen2-0.5B", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T04:39:49Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2-0.5B tags: - axolotl - generated_from_trainer model-index: - name: 3e18320f-3309-4bea-879f-561458bc9a71 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Qwen2-0.5B bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - cccd8bfc08aa015e_train_data.json ds_type: json format: custom path: /workspace/input_data/cccd8bfc08aa015e_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: vermoney/3e18320f-3309-4bea-879f-561458bc9a71 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/cccd8bfc08aa015e_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 1eed07a3-09fb-4f94-94e6-3315f2bfa239 wandb_project: s56-9 wandb_run: your_name wandb_runid: 1eed07a3-09fb-4f94-94e6-3315f2bfa239 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 3e18320f-3309-4bea-879f-561458bc9a71 This model is a fine-tuned version of [unsloth/Qwen2-0.5B](https://huggingface.co/unsloth/Qwen2-0.5B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4291 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.7491 \| 0.0532 \| 200 \| 1.4291 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
DevQuasar/Qwen.Qwen3-32B-GGUF	DevQuasar	2025-05-04T04:41:13Z	239	0	null	[ "gguf", "text-generation", "base_model:Qwen/Qwen3-32B", "base_model:quantized:Qwen/Qwen3-32B", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-29T04:08:34Z	--- base_model: - Qwen/Qwen3-32B pipeline_tag: text-generation --- ## LMStudio users! Please update the chat prompt template of the model. Go to My models -> Actions (gear) edit model default parameters -> Prompt -> Prompt template. Update the Jinja template. Correct JINJA: ``` {%- if tools %} {{- '<\|im_start\|>system\n' }} {%- if messages[0].role == 'system' %} {{- messages[0].content + '\n\n' }} {%- endif %} {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }} {%- for tool in tools %} {{- "\n" }} {{- tool \| tojson }} {%- endfor %} {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><\|im_end\|>\n" }} {%- else %} {%- if messages[0].role == 'system' %} {{- '<\|im_start\|>system\n' + messages[0].content + '<\|im_end\|>\n' }} {%- endif %} {%- endif %} {%- set ns = namespace(multi_step_tool=true, last_query_index=messages\|length - 1) %} {%- for message in messages[::-1] %} {%- set index = (messages\|length - 1) - loop.index0 %} {%- set tool_start = "<tool_response>" %} {%- set tool_start_length = tool_start\|length %} {%- set start_of_message = message.content[:tool_start_length] %} {%- set tool_end = "</tool_response>" %} {%- set tool_end_length = tool_end\|length %} {%- set start_pos = (message.content\|length) - tool_end_length %} {%- if start_pos < 0 %} {%- set start_pos = 0 %} {%- endif %} {%- set end_of_message = message.content[start_pos:] %} {%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %} {%- set ns.multi_step_tool = false %} {%- set ns.last_query_index = index %} {%- endif %} {%- endfor %} {%- for message in messages %} {%- if (message.role == "user") or (message.role == "system" and not loop.first) %} {{- '<\|im_start\|>' + message.role + '\n' + message.content + '<\|im_end\|>' + '\n' }} {%- elif message.role == "assistant" %} {%- set content = message.content %} {%- set reasoning_content = '' %} {%- if message.reasoning_content is defined and message.reasoning_content is not none %} {%- set reasoning_content = message.reasoning_content %} {%- else %} {%- if '</think>' in message.content %} {%- set content = (message.content.split('</think>')\|last).lstrip('\n') %} {%- set reasoning_content = (message.content.split('</think>')\|first).rstrip('\n') %} {%- set reasoning_content = (reasoning_content.split('<think>')\|last).lstrip('\n') %} {%- endif %} {%- endif %} {%- if loop.index0 > ns.last_query_index %} {%- if loop.last or (not loop.last and reasoning_content) %} {{- '<\|im_start\|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }} {%- else %} {{- '<\|im_start\|>' + message.role + '\n' + content }} {%- endif %} {%- else %} {{- '<\|im_start\|>' + message.role + '\n' + content }} {%- endif %} {%- if message.tool_calls %} {%- for tool_call in message.tool_calls %} {%- if (loop.first and content) or (not loop.first) %} {{- '\n' }} {%- endif %} {%- if tool_call.function %} {%- set tool_call = tool_call.function %} {%- endif %} {{- '<tool_call>\n{"name": "' }} {{- tool_call.name }} {{- '", "arguments": ' }} {%- if tool_call.arguments is string %} {{- tool_call.arguments }} {%- else %} {{- tool_call.arguments \| tojson }} {%- endif %} {{- '}\n</tool_call>' }} {%- endfor %} {%- endif %} {{- '<\|im_end\|>\n' }} {%- elif message.role == "tool" %} {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} {{- '<\|im_start\|>user' }} {%- endif %} {{- '\n<tool_response>\n' }} {{- message.content }} {{- '\n</tool_response>' }} {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} {{- '<\|im_end\|>\n' }} {%- endif %} {%- endif %} {%- endfor %} {%- if add_generation_prompt %} {{- '<\|im_start\|>assistant\n' }} {%- if enable_thinking is defined and enable_thinking is false %} {{- '<think>\n\n</think>\n\n' }} {%- endif %} {%- endif %} ``` [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
marcelovidigal/bert-base-multilingual-cased-2-contract-sections-classification-v4-10	marcelovidigal	2025-05-04T04:36:18Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-04T02:14:02Z	--- library_name: transformers license: apache-2.0 base_model: google-bert/bert-base-multilingual-cased tags: - generated_from_trainer model-index: - name: bert-base-multilingual-cased-2-contract-sections-classification-v4-10 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. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/mvgdr/classificacao-secoes-contratos-v4-bert-base/runs/3f73rktu) # bert-base-multilingual-cased-2-contract-sections-classification-v4-10 This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1797 - Accuracy Evaluate: 0.9603 - Precision Evaluate: 0.9530 - Recall Evaluate: 0.9655 - F1 Evaluate: 0.9578 - Accuracy Sklearn: 0.9603 - Precision Sklearn: 0.9623 - Recall Sklearn: 0.9603 - F1 Sklearn: 0.9605 - Acuracia Rotulo Objeto: 0.9917 - Acuracia Rotulo Obrigacoes: 0.9259 - Acuracia Rotulo Valor: 0.9284 - Acuracia Rotulo Vigencia: 0.9843 - Acuracia Rotulo Rescisao: 0.9169 - Acuracia Rotulo Foro: 1.0 - Acuracia Rotulo Reajuste: 0.9964 - Acuracia Rotulo Fiscalizacao: 0.9180 - Acuracia Rotulo Publicacao: 1.0 - Acuracia Rotulo Pagamento: 0.9783 - Acuracia Rotulo Casos Omissos: 0.9212 - Acuracia Rotulo Sancoes: 0.9908 - Acuracia Rotulo Dotacao Orcamentaria: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy Evaluate \| Precision Evaluate \| Recall Evaluate \| F1 Evaluate \| Accuracy Sklearn \| Precision Sklearn \| Recall Sklearn \| F1 Sklearn \| Acuracia Rotulo Objeto \| Acuracia Rotulo Obrigacoes \| Acuracia Rotulo Valor \| Acuracia Rotulo Vigencia \| Acuracia Rotulo Rescisao \| Acuracia Rotulo Foro \| Acuracia Rotulo Reajuste \| Acuracia Rotulo Fiscalizacao \| Acuracia Rotulo Publicacao \| Acuracia Rotulo Pagamento \| Acuracia Rotulo Casos Omissos \| Acuracia Rotulo Sancoes \| Acuracia Rotulo Dotacao Orcamentaria \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-----------------:\|:------------------:\|:---------------:\|:-----------:\|:----------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------------:\|:--------------------------:\|:---------------------:\|:------------------------:\|:------------------------:\|:--------------------:\|:------------------------:\|:----------------------------:\|:--------------------------:\|:-------------------------:\|:-----------------------------:\|:-----------------------:\|:------------------------------------:\| \| 1.5525 \| 1.0 \| 1000 \| 1.2169 \| 0.7772 \| 0.7719 \| 0.7417 \| 0.7406 \| 0.7772 \| 0.7416 \| 0.7772 \| 0.7485 \| 0.9483 \| 0.9209 \| 0.8940 \| 0.6719 \| 0.7507 \| 0.9192 \| 0.8327 \| 0.7886 \| 0.9507 \| 0.0 \| 0.9113 \| 0.3945 \| 0.6593 \| \| 0.4981 \| 2.0 \| 2000 \| 0.4333 \| 0.9337 \| 0.9326 \| 0.9382 \| 0.9347 \| 0.9337 \| 0.9358 \| 0.9337 \| 0.9340 \| 0.9773 \| 0.8586 \| 0.9255 \| 0.9764 \| 0.9418 \| 0.9308 \| 0.9680 \| 0.8896 \| 0.9951 \| 0.9130 \| 0.9212 \| 0.8991 \| 1.0 \| \| 0.2197 \| 3.0 \| 3000 \| 0.2467 \| 0.9463 \| 0.9473 \| 0.9527 \| 0.9494 \| 0.9463 \| 0.9474 \| 0.9463 \| 0.9463 \| 0.9793 \| 0.8872 \| 0.9284 \| 0.9790 \| 0.9418 \| 0.9385 \| 0.9964 \| 0.8896 \| 0.9951 \| 0.9565 \| 0.9212 \| 0.9725 \| 1.0 \| \| 0.1351 \| 4.0 \| 4000 \| 0.2067 \| 0.9503 \| 0.9516 \| 0.9565 \| 0.9535 \| 0.9503 \| 0.9515 \| 0.9503 \| 0.9503 \| 0.9835 \| 0.8889 \| 0.9284 \| 0.9843 \| 0.9418 \| 0.9577 \| 0.9751 \| 0.9148 \| 0.9951 \| 0.9710 \| 0.9212 \| 0.9725 \| 1.0 \| \| 0.0967 \| 5.0 \| 5000 \| 0.2049 \| 0.951 \| 0.9523 \| 0.9589 \| 0.9550 \| 0.951 \| 0.9525 \| 0.951 \| 0.9511 \| 0.9814 \| 0.8788 \| 0.9341 \| 0.9790 \| 0.9418 \| 0.9885 \| 0.9751 \| 0.9180 \| 0.9951 \| 0.9710 \| 0.9212 \| 0.9817 \| 1.0 \| \| 0.0697 \| 6.0 \| 6000 \| 0.1977 \| 0.953 \| 0.9535 \| 0.9612 \| 0.9567 \| 0.953 \| 0.9544 \| 0.953 \| 0.9531 \| 0.9814 \| 0.8822 \| 0.9284 \| 0.9869 \| 0.9418 \| 0.9885 \| 0.9786 \| 0.9180 \| 1.0 \| 0.9783 \| 0.9212 \| 0.9908 \| 1.0 \| \| 0.0711 \| 7.0 \| 7000 \| 0.1787 \| 0.958 \| 0.9516 \| 0.9630 \| 0.9561 \| 0.958 \| 0.9596 \| 0.958 \| 0.9582 \| 0.9855 \| 0.9276 \| 0.9284 \| 0.9869 \| 0.9252 \| 1.0 \| 0.9751 \| 0.9085 \| 0.9951 \| 0.9746 \| 0.9212 \| 0.9908 \| 1.0 \| \| 0.0614 \| 8.0 \| 8000 \| 0.1885 \| 0.9573 \| 0.9511 \| 0.9634 \| 0.9561 \| 0.9573 \| 0.9591 \| 0.9573 \| 0.9575 \| 0.9917 \| 0.9091 \| 0.9284 \| 0.9843 \| 0.9280 \| 1.0 \| 0.9751 \| 0.9180 \| 1.0 \| 0.9783 \| 0.9212 \| 0.9908 \| 1.0 \| \| 0.0576 \| 9.0 \| 9000 \| 0.1802 \| 0.959 \| 0.9510 \| 0.9638 \| 0.9559 \| 0.959 \| 0.9611 \| 0.959 \| 0.9593 \| 0.9917 \| 0.9310 \| 0.9284 \| 0.9843 \| 0.9114 \| 1.0 \| 0.9751 \| 0.9180 \| 1.0 \| 0.9783 \| 0.9212 \| 0.9908 \| 1.0 \| \| 0.0512 \| 10.0 \| 10000 \| 0.1797 \| 0.9603 \| 0.9530 \| 0.9655 \| 0.9578 \| 0.9603 \| 0.9623 \| 0.9603 \| 0.9605 \| 0.9917 \| 0.9259 \| 0.9284 \| 0.9843 \| 0.9169 \| 1.0 \| 0.9964 \| 0.9180 \| 1.0 \| 0.9783 \| 0.9212 \| 0.9908 \| 1.0 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.7.0+cu126 - Datasets 3.5.1 - Tokenizers 0.21.1
Delta-Vector/GLM-New-V3-Q5_0-GGUF	Delta-Vector	2025-05-04T04:36:05Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:NewEden/GLM-New-V3", "base_model:quantized:NewEden/GLM-New-V3", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T04:34:24Z	--- base_model: NewEden/GLM-New-V3 tags: - llama-cpp - gguf-my-repo --- # Delta-Vector/GLM-New-V3-Q5_0-GGUF This model was converted to GGUF format from [`NewEden/GLM-New-V3`](https://huggingface.co/NewEden/GLM-New-V3) 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/NewEden/GLM-New-V3) 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 Delta-Vector/GLM-New-V3-Q5_0-GGUF --hf-file glm-new-v3-q5_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Delta-Vector/GLM-New-V3-Q5_0-GGUF --hf-file glm-new-v3-q5_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 Delta-Vector/GLM-New-V3-Q5_0-GGUF --hf-file glm-new-v3-q5_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Delta-Vector/GLM-New-V3-Q5_0-GGUF --hf-file glm-new-v3-q5_0.gguf -c 2048 ```
mikagememoria/gensyn-checkpoints-leggy_rough_capybara	mikagememoria	2025-05-04T04:33:38Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am leggy rough capybara", "unsloth", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-25T07:39:14Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: gensyn-checkpoints-leggy_rough_capybara tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am leggy rough capybara - unsloth - trl licence: license --- # Model Card for gensyn-checkpoints-leggy_rough_capybara This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="mikagememoria/gensyn-checkpoints-leggy_rough_capybara", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - 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}} } ```
Jathushan/healthbot_llama	Jathushan	2025-05-04T04:29:37Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/Llama-3.2-1B-Instruct", "base_model:finetune:unsloth/Llama-3.2-1B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-04T04:28:55Z	--- base_model: unsloth/Llama-3.2-1B-Instruct tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Jathushan - License: apache-2.0 - Finetuned from model : unsloth/Llama-3.2-1B-Instruct 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)
stevensu123/cis6200finaltopany	stevensu123	2025-05-04T04:28:50Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-05-04T04:25:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
woojin0412/KIP-Judgment-Kor-LLM	woojin0412	2025-05-04T04:22:17Z	0	0	null	[ "safetensors", "llama", "license:apache-2.0", "region:us" ]	null	2025-05-04T04:17:16Z	--- license: apache-2.0 ---
ASethi04/meta-llama-Llama-3.1-8B-opc-sft-10000-lora-4-0.0001	ASethi04	2025-05-04T04:18:59Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "endpoints_compatible", "region:us" ]	null	2025-05-04T03:24:11Z	--- base_model: meta-llama/Llama-3.1-8B library_name: transformers model_name: meta-llama-Llama-3.1-8B-opc-sft-10000-lora-4-0.0001 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for meta-llama-Llama-3.1-8B-opc-sft-10000-lora-4-0.0001 This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="ASethi04/meta-llama-Llama-3.1-8B-opc-sft-10000-lora-4-0.0001", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/torchql-org/huggingface/runs/23mwqb4o) This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ASethi04/meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-4e-05	ASethi04	2025-05-04T04:16:31Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "endpoints_compatible", "region:us" ]	null	2025-05-03T14:50:19Z	--- base_model: meta-llama/Llama-3.1-8B library_name: transformers model_name: meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-4e-05 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-4e-05 This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="ASethi04/meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-4e-05", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/torchql-org/huggingface/runs/fs52609k) This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ASethi04/meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-0.0004	ASethi04	2025-05-04T04:16:18Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "endpoints_compatible", "region:us" ]	null	2025-05-04T00:27:34Z	--- base_model: meta-llama/Llama-3.1-8B library_name: transformers model_name: meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-0.0004 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-0.0004 This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="ASethi04/meta-llama-Llama-3.1-8B-hellaswag-first-lora-4-0.0004", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/torchql-org/huggingface/runs/zffbfdvk) This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
DevQuasar/JetBrains.CodeLlama-7B-Kexer-GGUF	DevQuasar	2025-05-04T04:11:22Z	0	0	null	[ "gguf", "text-generation", "base_model:JetBrains/CodeLlama-7B-Kexer", "base_model:quantized:JetBrains/CodeLlama-7B-Kexer", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T02:59:07Z	--- base_model: - JetBrains/CodeLlama-7B-Kexer pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [JetBrains/CodeLlama-7B-Kexer](https://huggingface.co/JetBrains/CodeLlama-7B-Kexer) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
vamcrizer/test-finetune	vamcrizer	2025-05-04T04:07:51Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "gemma3", "en", "base_model:unsloth/gemma-3-4b-it", "base_model:quantized:unsloth/gemma-3-4b-it", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T04:05:50Z	--- base_model: unsloth/gemma-3-4b-it tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: vamcrizer - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it - Dataset used : FreedomIntelligence/medical-o1-reasoning-SFT This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
howarudo/flat_style_LoRA	howarudo	2025-05-04T04:06:44Z	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-05-04T04:05:12Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: flat style widget: [] tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - howarudo/flat_style_LoRA <Gallery /> ## Model description These are howarudo/flat_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use flat style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](howarudo/flat_style_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
mradermacher/Qwen3-30B-A1.5B-High-Speed-i1-GGUF	mradermacher	2025-05-04T04:05:14Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-04T01:55:30Z	<!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/DavidAU/Qwen3-30B-A1.5B-High-Speed
ping98k/qwen3-8b-recall-writer-4e	ping98k	2025-05-04T04:04:48Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-8B-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen3-8B-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-04T04:04:12Z	--- base_model: unsloth/Qwen3-8B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: ping98k - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-8B-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Delta-Vector/Axo-Merge-Archaeo-V2-Lora-Q4_K_M-GGUF	Delta-Vector	2025-05-04T04:04:32Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:NewEden/Axo-Merge-Archaeo-V2-Lora", "base_model:quantized:NewEden/Axo-Merge-Archaeo-V2-Lora", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T04:03:00Z	--- base_model: NewEden/Axo-Merge-Archaeo-V2-Lora tags: - llama-cpp - gguf-my-repo --- # Delta-Vector/Axo-Merge-Archaeo-V2-Lora-Q4_K_M-GGUF This model was converted to GGUF format from [`NewEden/Axo-Merge-Archaeo-V2-Lora`](https://huggingface.co/NewEden/Axo-Merge-Archaeo-V2-Lora) 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/NewEden/Axo-Merge-Archaeo-V2-Lora) 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 Delta-Vector/Axo-Merge-Archaeo-V2-Lora-Q4_K_M-GGUF --hf-file axo-merge-archaeo-v2-lora-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Delta-Vector/Axo-Merge-Archaeo-V2-Lora-Q4_K_M-GGUF --hf-file axo-merge-archaeo-v2-lora-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 Delta-Vector/Axo-Merge-Archaeo-V2-Lora-Q4_K_M-GGUF --hf-file axo-merge-archaeo-v2-lora-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Delta-Vector/Axo-Merge-Archaeo-V2-Lora-Q4_K_M-GGUF --hf-file axo-merge-archaeo-v2-lora-q4_k_m.gguf -c 2048 ```
ubergarm/Qwen3-30B-A3B-GGUF	ubergarm	2025-05-04T04:04:13Z	20	7	null	[ "gguf", "imatrix", "qwen3_moe", "conversational", "ik_llama.cpp", "text-generation", "base_model:Qwen/Qwen3-30B-A3B", "base_model:quantized:Qwen/Qwen3-30B-A3B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-05-02T00:10:00Z	--- quantized_by: ubergarm pipeline_tag: text-generation base_model: Qwen/Qwen3-30B-A3B license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-30B-A3B/blob/main/LICENSE base_model_relation: quantized tags: - imatrix - qwen3_moe - conversational - ik_llama.cpp --- ## `ik_llama.cpp` imatrix Quantizations of Qwen/Qwen3-30B-A3B This quant collection REQUIRES [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp/) fork to support advanced non-linear SotA quants. Do not download these big files and expect them to run on mainline vanilla llama.cpp, ollama, LM Studio, KoboldCpp, etc! These quants provide best in class quality for the given memory footprint. ## Big Thanks Shout out to Wendell and the Level1Techs crew, the community [Forums](https://forum.level1techs.com/t/deepseek-deep-dive-r1-at-home/225826), [YouTube Channel](https://www.youtube.com/@Level1Techs)! BIG thanks for providing BIG hardware expertise and access to run these experiments and make these great quants available to the community!!! Also thanks to all the folks in the quanting and inferencing community here and on `r/LocalLLaMA` for tips and tricks helping each other run all the fun new models! Excited to share and learn together. Thanks! ## Quant Collection So far these are my best recipes offering the great quality in good memory footprint breakpoints. #### ubergarm/Qwen3-30B-A3B-mix-IQ4_K This quant is provides the best in class quality while providing good speed performance. This quant is designed to run with over 32k context using GPU performant f16 KV-Cache in under 24GB VRAM GPU. You could also try offload to CPU using `-nkvo -ctk q8_0 -ctv q8_0` and use `-rtr` for RAM optimized tensor packing on startup (without `mmap()` support) taking ~18396MiB of VRAM or less by offloading repeating layers to CPU as well at decreased speed. ``` 17.679 GiB (4.974 BPW) f32: 241 tensors q8_0: 6 tensors iq4_k: 96 tensors iq5_k: 48 tensors iq6_k: 188 tensors Final estimate: PPL = 9.1184 +/- 0.07278 (wiki-test.raw, compare to BF16 at 9.0703 +/- 0.07223) NOTE: Benchmarks including PPL with `wiki.test.raw` and KLD with `ubergarm-kld-test-corpus.txt` are looking interesting! Will publish soon! ``` ## Quick Start #### `ik_llama.cpp` API server for GPU inferencing ```bash # This example for ~21468MiB VRAM Usage ./build/bin/llama-server --model ubergarm/Qwen3-30B-A3B-GGUF/Qwen3-30B-A3B-mix-IQ4_K \ --alias ubergarm/Qwen3-30B-A3B-mix-IQ4_K \ -fa \ -ctk f16 -ctv f16 \ -c 32768 \ -fmoe \ -ngl 99 \ --threads 1 --host 127.0.0.1 \ --port 8080 ``` If you want more context and/or less VRAM usage, you can try: * Smaller KV Cache quantization `-ctk q4_0 -ctv q4_0` If you want more throughput you could try: * Increase context to max limit for your VRAM * use `--parallel N` to have (context / N) available per slot * use an asyncio client and keep the queue full ## Quantization <details> <summary>👈Secret Recipe</summary> ```bash #!/usr/bin/env bash custom=" # Attention (give Layer 0 a little extra as it scores lowest on cosine-similarity score) blk\.0\.attn_k.=q8_0 blk\.0\.attn_q.=q8_0 blk\.0\.attn_v.=q8_0 blk\.0\.attn_output.=q8_0 blk\..\.attn_k.=iq6_k blk\..\.attn_q.=iq6_k blk\..\.attn_v.=iq6_k blk\..\.attn_output.=iq6_k # Token Embedding (put these second so attn_output regex doesn catch too early) token_embd\.weight=q8_0 output\.weight=q8_0 # Experts blk\..\.ffn_down_exps\.weight=iq5_k blk\..\.ffn_(gate\|up)_exps\.weight=iq4_k " custom=$( echo "$custom" \| grep -v '^#' \| \ sed -Ez 's:\n+:,:g;s:,$::;s:^,::' ) ./build/bin/llama-quantize \ --custom-q "$custom" \ --imatrix /mnt/raid/models/ubergarm/Qwen3-30B-A3B-GGUF/imatrix-Qwen3-30B-A3B.dat \ /mnt/raid/models/Qwen/Qwen3-30B-A3B/Qwen3-30B-A3B-BF16-00001-of-00002.gguf \ /mnt/raid/models/ubergarm/Qwen3-30B-A3B-GGUF/Qwen3-30B-A3B-mix-IQ4_K.gguf \ IQ4_K \ 24 ``` </details> ## Discussion TODO: Discuss some about comparing quants e.g. bartowski, unsloth, and mradermacher including "quality" and "speed". ## Benchmarks In first tests with `llama-sweep-bench` I'm getting over 1600 tok/sec PP and 105 tok/sec TG on my 3090TI FE 24GB VRAM. It does slow down of course as it gets deeper into the full 32k context. Check the linked Benchmarks Discussion for updates as this is all pretty fresh right now. Pretty amazing performance both in terms of generation quality and speed for a model this size! ![Benchmarks showing these peak 1600 tok/sec PP and 105 tok/sec TG fully offloaded on 3090TI FE 24GB VRAM](images/benchmarks-01.png "Benchmarks showing these peak 1600 tok/sec PP and 105 tok/sec TG fully offloaded on 3090TI FE 24GB VRAM") ![Benchmarks showing Token Probability Deviation Percentiles](images/qwen3-30b-fig-09.png "Benchmarks showing Token Probability Deviation Percentiles") ## References * [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp/) * [ik_llama.cpp Getting Started Guide](https://github.com/ikawrakow/ik_llama.cpp/discussions/258) * [ik_llama.cpp Benchmarks Discussion](https://github.com/ikawrakow/ik_llama.cpp/discussions/357) * [imatrix calibration_data_v5_rc.txt](https://gist.github.com/tristandruyen/9e207a95c7d75ddf37525d353e00659c#file-calibration_data_v5_rc-txt)
ma921/gpt2-large_h_dpo_imdb_noise40_epoch20	ma921	2025-05-04T03:56:49Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:ma921/gpt2-large-sft-imdb", "base_model:finetune:ma921/gpt2-large-sft-imdb", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T03:55:06Z	--- library_name: transformers license: mit base_model: ma921/gpt2-large-sft-imdb tags: - generated_from_trainer model-index: - name: gpt2-large_h_dpo_imdb_noise40_epoch20 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-large_h_dpo_imdb_noise40_epoch20 This model is a fine-tuned version of [ma921/gpt2-large-sft-imdb](https://huggingface.co/ma921/gpt2-large-sft-imdb) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 32 - total_train_batch_size: 256 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
ivangrapher/4ec07c9c-883f-4bee-a8bd-679323310cc8	ivangrapher	2025-05-04T03:55:46Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2-1.5B-Instruct", "base_model:adapter:Qwen/Qwen2-1.5B-Instruct", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T02:08:13Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2-1.5B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 4ec07c9c-883f-4bee-a8bd-679323310cc8 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: Qwen/Qwen2-1.5B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 1f34408d713e6b26_train_data.json ds_type: json format: custom path: /workspace/input_data/1f34408d713e6b26_train_data.json type: field_instruction: en field_output: ja format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: ivangrapher/4ec07c9c-883f-4bee-a8bd-679323310cc8 hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/1f34408d713e6b26_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 2048 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: dac36c01-453c-4358-9c23-a55d2a4926d7 wandb_project: s56-7 wandb_run: your_name wandb_runid: dac36c01-453c-4358-9c23-a55d2a4926d7 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 4ec07c9c-883f-4bee-a8bd-679323310cc8 This model is a fine-tuned version of [Qwen/Qwen2-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-1.5B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.2381 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 5.2149 \| 0.0013 \| 150 \| 5.2381 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

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