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Arsen008/wav2vec2-large-mms-1b-turkish-colab	Arsen008	2025-06-16T08:03:27Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-06-10T09:42:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
srivihari/resume-job-role-classifier	srivihari	2025-06-16T08:03:26Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "resume", "job-role-classification", "en", "dataset:custom", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-15T17:33:43Z	--- license: apache-2.0 tags: - text-classification - resume - job-role-classification - transformers - distilbert datasets: - custom language: - en pipeline_tag: text-classification --- # 🔍 Resume Job Role Classifier A fine-tuned [`DistilBERT`](https://huggingface.co/distilbert-base-uncased) model to classify job roles based on resume content. This model is trained to predict the most likely profession from the given resume text, supporting over 14 different job categories. --- ## 🧠 Model Details - Architecture: DistilBERT (base, uncased) - Task: Multi-class text classification - Input: Raw resume text (English) - Output: Predicted job category label and score --- ## 📊 Labels Covered This model supports classification into the following job categories: - Data Science - Java Developer - Web Designing - HR - Mechanical Engineer - Electrical Engineering - Civil Engineer - Arts - Advocate - Sales - Health and fitness - Business Analyst - SAP Developer - Automation Testing --- ## 🏋️ Training - Dataset: Custom dataset containing labeled resumes - Split: 80% train / 20% test - Metrics: - Accuracy: 99–100% - F1 Score: ~0.99–1.00 (macro avg) - Epochs: 3 - Batch size: 8 - Optimizer: AdamW --- ## 📥 How to Use ```python from transformers import pipeline classifier = pipeline("text-classification", model="srivihari/resume-job-role-classifier") result = classifier("Experienced data scientist with Python, machine learning, and statistics background.") print(result) # Example output: # [{'label': 'Data Science', 'score': 0.97}] #Note: If you face any issues like token_type_ids errors, make sure to adjust tokenizer config as below: #from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline #model = AutoModelForSequenceClassification.from_pretrained("srivihari/resume-job-role-classifier") #tokenizer = AutoTokenizer.from_pretrained("srivihari/resume-job-role-classifier") #tokenizer.model_input_names = ["input_ids", "attention_mask"] #classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
aieng-lab/codebert-base_review-aspect	aieng-lab	2025-06-16T08:00:27Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "en", "base_model:microsoft/codebert-base", "base_model:finetune:microsoft/codebert-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T08:00:20Z	--- library_name: transformers license: mit language: - en metrics: - f1 - precision - recall base_model: - microsoft/codebert-base pipeline_tag: text-classification --- # CodeBERT base for classifying API reviews This model classifies API reviews in developer forums (e.g., Stack Overflow) as 'usability', 'others', 'onlysentiment', 'bug', 'performance', 'community', 'documentation', 'compatibility', 'legal', 'portability' or 'security'. - Developed by: Fabian C. Peña, Steffen Herbold - Finetuned from: [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) - Replication kit: [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark) - Language: English - License: MIT ## Citation ``` @misc{pena2025benchmark, author = {Fabian Peña and Steffen Herbold}, title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks}, year = {2025} } ```
sommerzen/EuroMoE-German-experiment	sommerzen	2025-06-16T07:59:09Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mixtral", "trl", "en", "base_model:utter-project/EuroMoE-2.6B-A0.6B-Instruct-Preview", "base_model:finetune:utter-project/EuroMoE-2.6B-A0.6B-Instruct-Preview", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T07:59:03Z	--- base_model: utter-project/EuroMoE-2.6B-A0.6B-Instruct-Preview tags: - text-generation-inference - transformers - unsloth - mixtral - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: sommerzen - License: apache-2.0 - Finetuned from model : utter-project/EuroMoE-2.6B-A0.6B-Instruct-Preview This mixtral 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)
SidXXD/eps8-gaussian-f_26	SidXXD	2025-06-16T07:58:38Z	0	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2025-06-16T07:53:43Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: photo of a sks person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/eps8-gaussian-f_26 These are Custom Diffusion adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on photo of a sks person using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
phospho-app/yeva11-ACT_BBOX-lerobot_kirby_01-51ib3	phospho-app	2025-06-16T07:58:29Z	0	0	null	[ "safetensors", "phosphobot", "act", "region:us" ]	null	2025-06-16T07:36:35Z	--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## This model was trained using phospho. Training was successfull, try it out on your robot! ## Training parameters: - Dataset: [phospho-app/lerobot_kirby_01_bboxes](https://huggingface.co/datasets/phospho-app/lerobot_kirby_01_bboxes) - Wandb run URL: None - Epochs: None - Batch size: 100 - Training steps: 10000 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
IkePy/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-wary_tropical_dove	IkePy	2025-06-16T07:58:25Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am wary tropical dove", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-06-15T14:52:52Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-wary_tropical_dove tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am wary tropical dove - unsloth - trl licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-wary_tropical_dove 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="IkePy/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-wary_tropical_dove", 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: 4.48.2 - Pytorch: 2.5.1 - Datasets: 3.6.0 - 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}} } ```
aieng-lab/t5-3b_review-aspect	aieng-lab	2025-06-16T07:58:04Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text-classification", "en", "base_model:google-t5/t5-3b", "base_model:finetune:google-t5/t5-3b", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T07:56:21Z	--- library_name: transformers license: mit language: - en metrics: - f1 - precision - recall base_model: - t5-3b pipeline_tag: text-classification --- # T5 3b for classifying API reviews This model classifies API reviews in developer forums (e.g., Stack Overflow) as 'usability', 'others', 'onlysentiment', 'bug', 'performance', 'community', 'documentation', 'compatibility', 'legal', 'portability' or 'security'. - Developed by: Fabian C. Peña, Steffen Herbold - Finetuned from: [t5-3b](https://huggingface.co/t5-3b) - Replication kit: [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark) - Language: English - License: MIT ## Citation ``` @misc{pena2025benchmark, author = {Fabian Peña and Steffen Herbold}, title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks}, year = {2025} } ```
Nerva1228/liupopo	Nerva1228	2025-06-16T07:56:49Z	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	2025-06-16T07:56:48Z	--- 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: liupopo --- # Liupopo <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 `liupopo` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "liupopo", "lora_weights": "https://huggingface.co/Nerva1228/liupopo/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('Nerva1228/liupopo', weight_name='lora.safetensors') image = pipeline('liupopo').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: 5e-05 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Nerva1228/liupopo/discussions) to add images that show off what you’ve made with this LoRA.
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.5_0.15_0.15_epoch2	MinaMila	2025-06-16T07:56:29Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T07:54:39Z	--- 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]
soy3on/Qwen3_Rude_RAG	soy3on	2025-06-16T07:55:42Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T07:55:37Z	--- 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]
JayHyeon/Qwen_1.5B-math-VIPO_5e-7_3.0vpo_constant-1ep	JayHyeon	2025-06-16T07:54:51Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:argilla/distilabel-math-preference-dpo", "arxiv:2305.18290", "base_model:Qwen/Qwen2.5-Math-1.5B", "base_model:finetune:Qwen/Qwen2.5-Math-1.5B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T05:29:43Z	--- base_model: Qwen/Qwen2.5-Math-1.5B datasets: argilla/distilabel-math-preference-dpo library_name: transformers model_name: Qwen_1.5B-math-VIPO_5e-7_3.0vpo_constant-1ep tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for Qwen_1.5B-math-VIPO_5e-7_3.0vpo_constant-1ep This model is a fine-tuned version of [Qwen/Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) on the [argilla/distilabel-math-preference-dpo](https://huggingface.co/datasets/argilla/distilabel-math-preference-dpo) 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="JayHyeon/Qwen_1.5B-math-VIPO_5e-7_3.0vpo_constant-1ep", 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/bonin147/huggingface/runs/0fnh12lt) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.15.2 - Transformers: 4.50.0 - Pytorch: 2.6.0 - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
aitechleart/Andrij	aitechleart	2025-06-16T07:54:13Z	0	0	null	[ "license:other", "region:us" ]	null	2025-06-16T07:11:55Z	--- 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 ---
SidXXD/eps8-noise_upscaling-f_26	SidXXD	2025-06-16T07:53:35Z	0	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2025-06-16T07:47:56Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: photo of a sks person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/eps8-noise_upscaling-f_26 These are Custom Diffusion adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on photo of a sks person using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
tscstudios/efphy0itvsbuqapuj0ui31j6gff3_24705bf4-0112-49d1-ab57-4a053ed9a09b	tscstudios	2025-06-16T07:53:11Z	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	2025-06-16T07:53:09Z	--- 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: TOK --- # Efphy0Itvsbuqapuj0Ui31J6Gff3_24705Bf4 0112 49D1 Ab57 4A053Ed9A09B <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 `TOK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TOK", "lora_weights": "https://huggingface.co/tscstudios/efphy0itvsbuqapuj0ui31j6gff3_24705bf4-0112-49d1-ab57-4a053ed9a09b/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('tscstudios/efphy0itvsbuqapuj0ui31j6gff3_24705bf4-0112-49d1-ab57-4a053ed9a09b', weight_name='lora.safetensors') image = pipeline('TOK').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/tscstudios/efphy0itvsbuqapuj0ui31j6gff3_24705bf4-0112-49d1-ab57-4a053ed9a09b/discussions) to add images that show off what you’ve made with this LoRA.
csukuangfj/vits-piper-de_DE-glados_turret-medium	csukuangfj	2025-06-16T07:52:50Z	0	0	null	[ "onnx", "region:us" ]	null	2025-06-16T07:08:40Z	# Introduction models are from https://huggingface.co/systemofapwne/piper-de-glados/tree/main/de/de_DE
Triangle104/Qwen3-4B-Esper3-Q4_K_S-GGUF	Triangle104	2025-06-16T07:52:45Z	18	0	transformers	[ "transformers", "gguf", "esper", "esper-3", "valiant", "valiant-labs", "qwen", "qwen-3", "qwen-3-4b", "4b", "reasoning", "code", "code-instruct", "python", "javascript", "dev-ops", "jenkins", "terraform", "scripting", "powershell", "azure", "aws", "gcp", "cloud", "problem-solving", "architect", "engineer", "developer", "creative", "analytical", "expert", "rationality", "conversational", "chat", "instruct", "llama-cpp", "gguf-my-repo", "text-generation", "en", "dataset:sequelbox/Titanium2.1-DeepSeek-R1", "dataset:sequelbox/Tachibana2-DeepSeek-R1", "dataset:sequelbox/Raiden-DeepSeek-R1", "base_model:ValiantLabs/Qwen3-4B-Esper3", "base_model:quantized:ValiantLabs/Qwen3-4B-Esper3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-05-30T23:24:05Z	--- language: - en library_name: transformers pipeline_tag: text-generation tags: - esper - esper-3 - valiant - valiant-labs - qwen - qwen-3 - qwen-3-4b - 4b - reasoning - code - code-instruct - python - javascript - dev-ops - jenkins - terraform - scripting - powershell - azure - aws - gcp - cloud - problem-solving - architect - engineer - developer - creative - analytical - expert - rationality - conversational - chat - instruct - llama-cpp - gguf-my-repo base_model: ValiantLabs/Qwen3-4B-Esper3 datasets: - sequelbox/Titanium2.1-DeepSeek-R1 - sequelbox/Tachibana2-DeepSeek-R1 - sequelbox/Raiden-DeepSeek-R1 license: apache-2.0 --- # Triangle104/Qwen3-4B-Esper3-Q4_K_S-GGUF This model was converted to GGUF format from [`ValiantLabs/Qwen3-4B-Esper3`](https://huggingface.co/ValiantLabs/Qwen3-4B-Esper3) 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/ValiantLabs/Qwen3-4B-Esper3) for more details on the model. --- Esper 3 is a coding, architecture, and DevOps reasoning specialist built on Qwen 3. - Finetuned on our DevOps and architecture reasoning and code reasoning data generated with Deepseek R1! - Improved general and creative reasoning to supplement problem-solving and general chat performance. - Small model sizes allow running on local desktop and mobile, plus super-fast server inference! --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Qwen3-4B-Esper3-Q4_K_S-GGUF --hf-file qwen3-4b-esper3-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Qwen3-4B-Esper3-Q4_K_S-GGUF --hf-file qwen3-4b-esper3-q4_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Qwen3-4B-Esper3-Q4_K_S-GGUF --hf-file qwen3-4b-esper3-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Qwen3-4B-Esper3-Q4_K_S-GGUF --hf-file qwen3-4b-esper3-q4_k_s.gguf -c 2048 ```
csukuangfj/vits-piper-de_DE-glados_turret-low	csukuangfj	2025-06-16T07:52:42Z	0	0	null	[ "onnx", "region:us" ]	null	2025-06-16T07:08:26Z	# Introduction models are from https://huggingface.co/systemofapwne/piper-de-glados/tree/main/de/de_DE
johngreendr1/f1141c6c-f51c-4e86-9dbd-a4549c81238c	johngreendr1	2025-06-16T07:52:40Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:codellama/CodeLlama-7b-hf", "base_model:adapter:codellama/CodeLlama-7b-hf", "region:us" ]	null	2025-06-16T03:05:12Z	--- base_model: codellama/CodeLlama-7b-hf 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 0.15.1
Triangle104/Qwen3-4B-Esper3-Q5_K_M-GGUF	Triangle104	2025-06-16T07:52:19Z	23	0	transformers	[ "transformers", "gguf", "esper", "esper-3", "valiant", "valiant-labs", "qwen", "qwen-3", "qwen-3-4b", "4b", "reasoning", "code", "code-instruct", "python", "javascript", "dev-ops", "jenkins", "terraform", "scripting", "powershell", "azure", "aws", "gcp", "cloud", "problem-solving", "architect", "engineer", "developer", "creative", "analytical", "expert", "rationality", "conversational", "chat", "instruct", "llama-cpp", "gguf-my-repo", "text-generation", "en", "dataset:sequelbox/Titanium2.1-DeepSeek-R1", "dataset:sequelbox/Tachibana2-DeepSeek-R1", "dataset:sequelbox/Raiden-DeepSeek-R1", "base_model:ValiantLabs/Qwen3-4B-Esper3", "base_model:quantized:ValiantLabs/Qwen3-4B-Esper3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-05-30T23:46:53Z	--- language: - en library_name: transformers pipeline_tag: text-generation tags: - esper - esper-3 - valiant - valiant-labs - qwen - qwen-3 - qwen-3-4b - 4b - reasoning - code - code-instruct - python - javascript - dev-ops - jenkins - terraform - scripting - powershell - azure - aws - gcp - cloud - problem-solving - architect - engineer - developer - creative - analytical - expert - rationality - conversational - chat - instruct - llama-cpp - gguf-my-repo base_model: ValiantLabs/Qwen3-4B-Esper3 datasets: - sequelbox/Titanium2.1-DeepSeek-R1 - sequelbox/Tachibana2-DeepSeek-R1 - sequelbox/Raiden-DeepSeek-R1 license: apache-2.0 --- # Triangle104/Qwen3-4B-Esper3-Q5_K_M-GGUF This model was converted to GGUF format from [`ValiantLabs/Qwen3-4B-Esper3`](https://huggingface.co/ValiantLabs/Qwen3-4B-Esper3) 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/ValiantLabs/Qwen3-4B-Esper3) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Qwen3-4B-Esper3-Q5_K_M-GGUF --hf-file qwen3-4b-esper3-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Qwen3-4B-Esper3-Q5_K_M-GGUF --hf-file qwen3-4b-esper3-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Qwen3-4B-Esper3-Q5_K_M-GGUF --hf-file qwen3-4b-esper3-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Qwen3-4B-Esper3-Q5_K_M-GGUF --hf-file qwen3-4b-esper3-q5_k_m.gguf -c 2048 ```
Triangle104/Qwen3-4B-Esper3-Q5_K_S-GGUF	Triangle104	2025-06-16T07:50:42Z	25	0	transformers	[ "transformers", "gguf", "esper", "esper-3", "valiant", "valiant-labs", "qwen", "qwen-3", "qwen-3-4b", "4b", "reasoning", "code", "code-instruct", "python", "javascript", "dev-ops", "jenkins", "terraform", "scripting", "powershell", "azure", "aws", "gcp", "cloud", "problem-solving", "architect", "engineer", "developer", "creative", "analytical", "expert", "rationality", "conversational", "chat", "instruct", "llama-cpp", "gguf-my-repo", "text-generation", "en", "dataset:sequelbox/Titanium2.1-DeepSeek-R1", "dataset:sequelbox/Tachibana2-DeepSeek-R1", "dataset:sequelbox/Raiden-DeepSeek-R1", "base_model:ValiantLabs/Qwen3-4B-Esper3", "base_model:quantized:ValiantLabs/Qwen3-4B-Esper3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-05-30T23:44:44Z	--- language: - en library_name: transformers pipeline_tag: text-generation tags: - esper - esper-3 - valiant - valiant-labs - qwen - qwen-3 - qwen-3-4b - 4b - reasoning - code - code-instruct - python - javascript - dev-ops - jenkins - terraform - scripting - powershell - azure - aws - gcp - cloud - problem-solving - architect - engineer - developer - creative - analytical - expert - rationality - conversational - chat - instruct - llama-cpp - gguf-my-repo base_model: ValiantLabs/Qwen3-4B-Esper3 datasets: - sequelbox/Titanium2.1-DeepSeek-R1 - sequelbox/Tachibana2-DeepSeek-R1 - sequelbox/Raiden-DeepSeek-R1 license: apache-2.0 --- # Triangle104/Qwen3-4B-Esper3-Q5_K_S-GGUF This model was converted to GGUF format from [`ValiantLabs/Qwen3-4B-Esper3`](https://huggingface.co/ValiantLabs/Qwen3-4B-Esper3) 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/ValiantLabs/Qwen3-4B-Esper3) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Qwen3-4B-Esper3-Q5_K_S-GGUF --hf-file qwen3-4b-esper3-q5_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Qwen3-4B-Esper3-Q5_K_S-GGUF --hf-file qwen3-4b-esper3-q5_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Qwen3-4B-Esper3-Q5_K_S-GGUF --hf-file qwen3-4b-esper3-q5_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Qwen3-4B-Esper3-Q5_K_S-GGUF --hf-file qwen3-4b-esper3-q5_k_s.gguf -c 2048 ```
veddhanth/lora-trained-xl-stage-2-pretrained-enc-enhanced-sneaker	veddhanth	2025-06-16T07:50:21Z	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	2025-06-16T07:38:58Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: a photo of sks sneaker 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 - veddhanth/lora-trained-xl-stage-2-pretrained-enc-enhanced-sneaker <Gallery /> ## Model description These are veddhanth/lora-trained-xl-stage-2-pretrained-enc-enhanced-sneaker 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: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of sks sneaker to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](veddhanth/lora-trained-xl-stage-2-pretrained-enc-enhanced-sneaker/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]
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.5_0.15_0.15_epoch1	MinaMila	2025-06-16T07:48:37Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T07:46:52Z	--- 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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Triangle104/Qwen3-4B-Esper3-Q4_K_M-GGUF	Triangle104	2025-06-16T07:48:08Z	19	0	transformers	[ "transformers", "gguf", "esper", "esper-3", "valiant", "valiant-labs", "qwen", "qwen-3", "qwen-3-4b", "4b", "reasoning", "code", "code-instruct", "python", "javascript", "dev-ops", "jenkins", "terraform", "scripting", "powershell", "azure", "aws", "gcp", "cloud", "problem-solving", "architect", "engineer", "developer", "creative", "analytical", "expert", "rationality", "conversational", "chat", "instruct", "llama-cpp", "gguf-my-repo", "text-generation", "en", "dataset:sequelbox/Titanium2.1-DeepSeek-R1", "dataset:sequelbox/Tachibana2-DeepSeek-R1", "dataset:sequelbox/Raiden-DeepSeek-R1", "base_model:ValiantLabs/Qwen3-4B-Esper3", "base_model:quantized:ValiantLabs/Qwen3-4B-Esper3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-05-30T23:35:07Z	--- language: - en library_name: transformers pipeline_tag: text-generation tags: - esper - esper-3 - valiant - valiant-labs - qwen - qwen-3 - qwen-3-4b - 4b - reasoning - code - code-instruct - python - javascript - dev-ops - jenkins - terraform - scripting - powershell - azure - aws - gcp - cloud - problem-solving - architect - engineer - developer - creative - analytical - expert - rationality - conversational - chat - instruct - llama-cpp - gguf-my-repo base_model: ValiantLabs/Qwen3-4B-Esper3 datasets: - sequelbox/Titanium2.1-DeepSeek-R1 - sequelbox/Tachibana2-DeepSeek-R1 - sequelbox/Raiden-DeepSeek-R1 license: apache-2.0 --- # Triangle104/Qwen3-4B-Esper3-Q4_K_M-GGUF This model was converted to GGUF format from [`ValiantLabs/Qwen3-4B-Esper3`](https://huggingface.co/ValiantLabs/Qwen3-4B-Esper3) 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/ValiantLabs/Qwen3-4B-Esper3) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Qwen3-4B-Esper3-Q4_K_M-GGUF --hf-file qwen3-4b-esper3-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Qwen3-4B-Esper3-Q4_K_M-GGUF --hf-file qwen3-4b-esper3-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Qwen3-4B-Esper3-Q4_K_M-GGUF --hf-file qwen3-4b-esper3-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Qwen3-4B-Esper3-Q4_K_M-GGUF --hf-file qwen3-4b-esper3-q4_k_m.gguf -c 2048 ```
momergul/babylm_interactive_gpt2_student	momergul	2025-06-16T07:47:54Z	323	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-08T21:55:22Z	--- 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. 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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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suzushi/miso-diffusion-2.0	suzushi	2025-06-16T07:45:19Z	0	4	diffusers	[ "diffusers", "text-to-image", "en", "base_model:stabilityai/stable-diffusion-3.5-medium", "base_model:finetune:stabilityai/stable-diffusion-3.5-medium", "region:us" ]	text-to-image	2025-06-15T06:29:30Z	--- language: - en license_name: stabilityai-ai-community license_link: LICENSE.md library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image base_model: - suzushi/miso-diffusion-m-1.0 - stabilityai/stable-diffusion-3.5-medium --- <div style="display: flex; justify-content: center; gap: 20px; margin-bottom: 20px;"> <img src="demo1.png" width="400" /> <img src="demo2.png" width="400" /> </div> # Anime SD3.5 medium Model An attempt to fine tune sd3.5 medium ## Version History \| Version \| Base Training \| Aesthetic Training \| Total Epochs \| \|---------\|--------------\|-------------------\|--------------\| \| alpha \| 250K images \| 0 images \| 1 \| \| beta \| 160K images \| 0 images \| 3 \| \| 1.0 \| 600k images \| 0 images \| 2 + (3 from beta) \| \| 1.1 \| 710k images \| 0 images \| 5 \| \| 2.0 \| 1.08M images \| 0 images \| 5 \| ## Training Methodology Training is done on gh200 with 96gb vram, now that prior training shows decent results, I am slightly increasing learning rate. Training setting: Adafactor with a batchsize of 40, lr_scheduler: cosine SD3.5 Specific setting: enable_scaled_pos_embed = true pos_emb_random_crop_rate = 0.2 weighting_scheme = "flow" learning_rate = 8e-6 learning_rate_te1 = 5e-6 learning_rate_te2 = 5e-6 Train Clip: true, Train t5xxl: false ## Support Me At the moment training an epoch cost around 130 dollars. If you like my project please consider supporting me: https://ko-fi.com/suzushi2024 Lastly, huge thanks to meg who has been supporting this project, without him this project would not have been possible !
lyae/my-sentiment-classifier	lyae	2025-06-16T07:43:19Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T07:42:56Z	--- 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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SidXXD/test-noise_upscaling-f_26	SidXXD	2025-06-16T07:42:41Z	0	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "custom-diffusion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2025-06-15T17:37:48Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: photo of a sks person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - custom-diffusion inference: true --- # Custom Diffusion - SidXXD/test-noise_upscaling-f_26 These are Custom Diffusion adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on photo of a sks person using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.05_0.15_epoch2	MinaMila	2025-06-16T07:41:32Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T07:39:39Z	--- 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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mdhasnainali/ReaderLM-v2-Finetuned-For-Job-Post	mdhasnainali	2025-06-16T07:40:45Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T07:38:31Z	--- 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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aieng-lab/gpt2-large_review-aspect	aieng-lab	2025-06-16T07:40:32Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-classification", "en", "base_model:openai-community/gpt2-large", "base_model:finetune:openai-community/gpt2-large", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T07:40:02Z	--- library_name: transformers license: mit language: - en metrics: - f1 - precision - recall base_model: - gpt2-large pipeline_tag: text-classification --- # GPT-2 large for classifying API reviews This model classifies API reviews in developer forums (e.g., Stack Overflow) as 'usability', 'others', 'onlysentiment', 'bug', 'performance', 'community', 'documentation', 'compatibility', 'legal', 'portability' or 'security'. - Developed by: Fabian C. Peña, Steffen Herbold - Finetuned from: [gpt2-large](https://huggingface.co/gpt2-large) - Replication kit: [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark) - Language: English - License: MIT ## Citation ``` @misc{pena2025benchmark, author = {Fabian Peña and Steffen Herbold}, title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks}, year = {2025} } ```
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.5_0.15_0.25_epoch2	MinaMila	2025-06-16T07:40:29Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T07:38:38Z	--- 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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Abhikhm/llama3.2-1B-notechat	Abhikhm	2025-06-16T07:40:19Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:adapter:meta-llama/Llama-3.2-1B-Instruct", "region:us" ]	null	2025-06-16T07:40:15Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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japat123/qwen_jun16_1	japat123	2025-06-16T07:40:10Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-16T07:39:00Z	--- base_model: unsloth/qwen3-8b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: japat123 - License: apache-2.0 - Finetuned from model : unsloth/qwen3-8b-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)
Skyfallirk/vasnetsov_LoRa	Skyfallirk	2025-06-16T07:40:02Z	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	2025-06-16T07:39:58Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: a photo collage in vasnetsov 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 - Skyfallirk/vasnetsov_LoRa <Gallery /> ## Model description These are Skyfallirk/vasnetsov_LoRa LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo collage in vasnetsov style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](Skyfallirk/vasnetsov_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]
aieng-lab/gpt2_review-aspect	aieng-lab	2025-06-16T07:38:29Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-classification", "en", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T07:38:21Z	--- library_name: transformers license: mit language: - en metrics: - f1 - precision - recall base_model: - gpt2 pipeline_tag: text-classification --- # GPT-2 small for classifying API reviews This model classifies API reviews in developer forums (e.g., Stack Overflow) as 'usability', 'others', 'onlysentiment', 'bug', 'performance', 'community', 'documentation', 'compatibility', 'legal', 'portability' or 'security'. - Developed by: Fabian C. Peña, Steffen Herbold - Finetuned from: [gpt2](https://huggingface.co/gpt2) - Replication kit: [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark) - Language: English - License: MIT ## Citation ``` @misc{pena2025benchmark, author = {Fabian Peña and Steffen Herbold}, title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks}, year = {2025} } ```
aieng-lab/ModernBERT-large_review-aspect	aieng-lab	2025-06-16T07:37:47Z	0	0	transformers	[ "transformers", "safetensors", "modernbert", "text-classification", "en", "base_model:answerdotai/ModernBERT-large", "base_model:finetune:answerdotai/ModernBERT-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T07:37:32Z	--- library_name: transformers license: mit language: - en metrics: - f1 - precision - recall base_model: - answerdotai/ModernBERT-large pipeline_tag: text-classification --- # ModernBERT large for classifying API reviews This model classifies API reviews in developer forums (e.g., Stack Overflow) as 'usability', 'others', 'onlysentiment', 'bug', 'performance', 'community', 'documentation', 'compatibility', 'legal', 'portability' or 'security'. - Developed by: Fabian C. Peña, Steffen Herbold - Finetuned from: [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) - Replication kit: [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark) - Language: English - License: MIT ## Citation ``` @misc{pena2025benchmark, author = {Fabian Peña and Steffen Herbold}, title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks}, year = {2025} } ```
csukuangfj/vits-piper-de_DE-glados-medium	csukuangfj	2025-06-16T07:36:48Z	0	0	null	[ "onnx", "region:us" ]	null	2025-06-16T07:07:38Z	# Introduction models are from https://huggingface.co/systemofapwne/piper-de-glados/tree/main/de/de_DE
csukuangfj/vits-piper-de_DE-glados-low	csukuangfj	2025-06-16T07:36:27Z	0	0	null	[ "onnx", "region:us" ]	null	2025-06-16T07:07:24Z	# Introduction models are from https://huggingface.co/systemofapwne/piper-de-glados/tree/main/de/de_DE
aieng-lab/bert-large-cased_review-aspect	aieng-lab	2025-06-16T07:34:58Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "en", "base_model:google-bert/bert-large-cased", "base_model:finetune:google-bert/bert-large-cased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T07:34:44Z	--- library_name: transformers license: mit language: - en metrics: - f1 - precision - recall base_model: - bert-large-cased pipeline_tag: text-classification --- # BERT large for classifying API reviews This model classifies API reviews in developer forums (e.g., Stack Overflow) as 'usability', 'others', 'onlysentiment', 'bug', 'performance', 'community', 'documentation', 'compatibility', 'legal', 'portability' or 'security'. - Developed by: Fabian C. Peña, Steffen Herbold - Finetuned from: [bert-large-cased](https://huggingface.co/bert-large-cased) - Replication kit: [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark) - Language: English - License: MIT ## Citation ``` @misc{pena2025benchmark, author = {Fabian Peña and Steffen Herbold}, title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks}, year = {2025} } ```
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.05_0.15_epoch1	MinaMila	2025-06-16T07:34:57Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T07:33:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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aieng-lab/bert-base-cased_review-aspect	aieng-lab	2025-06-16T07:34:15Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "en", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T07:34:07Z	--- library_name: transformers license: mit language: - en metrics: - f1 - precision - recall base_model: - bert-base-cased pipeline_tag: text-classification --- # BERT base for classifying API reviews This model classifies API reviews in developer forums (e.g., Stack Overflow) as 'usability', 'others', 'onlysentiment', 'bug', 'performance', 'community', 'documentation', 'compatibility', 'legal', 'portability' or 'security'. - Developed by: Fabian C. Peña, Steffen Herbold - Finetuned from: [bert-base-cased](https://huggingface.co/bert-base-cased) - Replication kit: [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark) - Language: English - License: MIT ## Citation ``` @misc{pena2025benchmark, author = {Fabian Peña and Steffen Herbold}, title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks}, year = {2025} } ```
Triangle104/Q3-8B-Kintsugi-Q8_0-GGUF	Triangle104	2025-06-16T07:33:15Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "axolotl", "unsloth", "roleplay", "conversational", "llama-cpp", "gguf-my-repo", "dataset:PygmalionAI/PIPPA", "dataset:Alfitaria/nemotron-ultra-reasoning-synthkink", "dataset:PocketDoc/Dans-Prosemaxx-Gutenberg", "dataset:FreedomIntelligence/Medical-R1-Distill-Data", "dataset:cognitivecomputations/SystemChat-2.0", "dataset:allenai/tulu-3-sft-personas-instruction-following", "dataset:kalomaze/Opus_Instruct_25k", "dataset:simplescaling/s1K-claude-3-7-sonnet", "dataset:ai2-adapt-dev/flan_v2_converted", "dataset:grimulkan/theory-of-mind", "dataset:grimulkan/physical-reasoning", "dataset:nvidia/HelpSteer3", "dataset:nbeerbower/gutenberg2-dpo", "dataset:nbeerbower/gutenberg-moderne-dpo", "dataset:nbeerbower/Purpura-DPO", "dataset:antiven0m/physical-reasoning-dpo", "dataset:allenai/tulu-3-IF-augmented-on-policy-70b", "dataset:NobodyExistsOnTheInternet/system-message-DPO", "base_model:allura-org/Q3-8B-Kintsugi", "base_model:quantized:allura-org/Q3-8B-Kintsugi", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T07:24:06Z	--- license: apache-2.0 base_model: allura-org/Q3-8B-Kintsugi library_name: transformers tags: - mergekit - axolotl - unsloth - roleplay - conversational - llama-cpp - gguf-my-repo datasets: - PygmalionAI/PIPPA - Alfitaria/nemotron-ultra-reasoning-synthkink - PocketDoc/Dans-Prosemaxx-Gutenberg - FreedomIntelligence/Medical-R1-Distill-Data - cognitivecomputations/SystemChat-2.0 - allenai/tulu-3-sft-personas-instruction-following - kalomaze/Opus_Instruct_25k - simplescaling/s1K-claude-3-7-sonnet - ai2-adapt-dev/flan_v2_converted - grimulkan/theory-of-mind - grimulkan/physical-reasoning - nvidia/HelpSteer3 - nbeerbower/gutenberg2-dpo - nbeerbower/gutenberg-moderne-dpo - nbeerbower/Purpura-DPO - antiven0m/physical-reasoning-dpo - allenai/tulu-3-IF-augmented-on-policy-70b - NobodyExistsOnTheInternet/system-message-DPO --- # Triangle104/Q3-8B-Kintsugi-Q8_0-GGUF This model was converted to GGUF format from [`allura-org/Q3-8B-Kintsugi`](https://huggingface.co/allura-org/Q3-8B-Kintsugi) 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/allura-org/Q3-8B-Kintsugi) for more details on the model. --- Q3-8B-Kintsugi is a roleplaying model finetuned from Qwen3-8B-Base. During testing, Kintsugi punched well above its weight class in terms of parameters, especially for 1-on-1 roleplaying and general storywriting. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Q3-8B-Kintsugi-Q8_0-GGUF --hf-file q3-8b-kintsugi-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Q3-8B-Kintsugi-Q8_0-GGUF --hf-file q3-8b-kintsugi-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Q3-8B-Kintsugi-Q8_0-GGUF --hf-file q3-8b-kintsugi-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Q3-8B-Kintsugi-Q8_0-GGUF --hf-file q3-8b-kintsugi-q8_0.gguf -c 2048 ```
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.5_0.15_0.25_epoch1	MinaMila	2025-06-16T07:32:36Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T07:30:49Z	--- 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]
xfjcoder/llama3.1-8b-erged-6bit	xfjcoder	2025-06-16T07:31:21Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T07:31:17Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: xfjcoder - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.05_0.25_epoch2	MinaMila	2025-06-16T07:28:01Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T07:26:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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FlameF0X/SnowflakeCore-G0-Release-3-1B	FlameF0X	2025-06-16T07:26:20Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-15T19:54:04Z	--- license: apache-2.0 --- # 🔬 SnowflakeCore-G0-Release-3 Architecture Size Report ## Summary This document provides a detailed breakdown of the parameter count and structural design of the SnowflakeCore-G0-Release-3 model. SnowflakeCore-G0-Release-3 is a custom decoder-only transformer model built from scratch, designed for autoregressive language modeling with rotary positional embeddings (RoPE). --- ## 📐 Model Architecture Overview \| Component \| Value \| \|------------------------\|-----------------\| \| Architecture Type \| Decoder-only Transformer \| \| Hidden Size (d_model) \| 1536 \| \| Number of Layers \| 32 \| \| Attention Heads \| 16 \| \| Feedforward Dim (d_ff) \| 6144 \| \| Max Sequence Length \| 2048 \| \| Positional Encoding \| Rotary (RoPE) \| \| Vocabulary Size \| 50,000 (assumed)\| \| Total Parameters \| ≈ 1.06 Billion \| --- ## 🧮 Parameter Count Breakdown ### 1. Embedding Layers - Token Embedding: V × d = 50,000 × 1536 = 76.8M - Output Projection: d × V = 1536 × 50,000 = 76.8M Total: ``` P_embedding = 2 · 1536 · 50,000 = 153.6M ``` --- ### 2. Transformer Blocks Each of the 32 layers contains: - Multi-Head Attention (Q, K, V, Out): 4 · d² = 4 · 1536² = 9.44M - Feedforward Network (MLP): 2 · d · d_ff = 2 · 1536 · 6144 = 18.87M - Total per Layer: ``` 9.44M + 18.87M = 28.31M ``` - Total across 32 layers: ``` 32 · 28.31M = 905.97M ``` --- ### 3. Positional Embedding - Type: Rotary Positional Embeddings (RoPE) - Parameter Count: 0 (non-learned, sinusoidal basis) --- ## 📊 Final Parameter Estimate ``` Total Parameters ≈ P_embedding + P_transformer = 153.6M + 905.97M = 1,059.6M ``` --- ## 🧠 Training Regime (Contextual) \| Item \| Value \| \|-----------------------------\|-------------------\| \| Training Dataset Size \| ~2 million rows \| \| Max Tokens per Sequence \| 2048 \| \| Effective Batch Size \| 32 × 4 = 128 \| \| Number of Epochs \| 15 \| \| Optimizer \| AdamW \| \| Learning Rate \| 3 × 10⁻⁴ \| Approximate number of tokens: ``` 2M × avg_tokens_per_row ≤ 4B tokens ``` --- ## 🧾 Notes - SnowflakeCore-G0-Release-3 exceeds the size of GPT-2 Large (~774M parameters). - With RoPE and 32 layers, the model is well-positioned for long-range generalization. - This parameter size is consistent with the compute-optimal design frontier for mid-scale language models. --- ## 📦 Conclusion SnowflakeCore-G0-Release-3 is a rigorously engineered, 1.06B parameter language model with modern architectural choices (RoPE, deep stack, wide FFN) that position it as a strong open foundation model for further research, deployment, and extension.
robotgeneralist/openpi-nomagic	robotgeneralist	2025-06-16T07:24:23Z	0	0	null	[ "license:mit", "region:us" ]	null	2025-04-14T18:14:58Z	--- license: mit --- # Nomagic Simple / Adversarial Box Model Checkpoints This is a repo to store the most important checkpoints of the `openpi` model. ## Uploading checkpoints Since the checkpoints are huge, the fastest and most reliable way to upload them is by using the `upload-large-folder` command from `huggingface-cli`. To do so, you first have to log in with appropriate credentials (you need a token with write permissions to the target repository): ``` huggingface-cli login ``` Next, use `upload-large-folder`. For example, to upload the `checkpoints` directory to the remote repository, run: ``` huggingface-cli upload-large-folder robotgeneralist/openpi-nomagic-multibox checkpoints --repo-type=model ``` Note that there is no way to specify a target path where the data will be stored on the remote. The contents of the directory will be placed under the root directory. So, for example, if your local folder is organized like the following: ``` checkpoints --some-dir --file1 --file2 ``` after uploading to the remote, you will have: ``` some-dir --file1 --file2 ``` Luckily, you can still upload additional files later on. For example, if after the first upload you try to upload: ``` checkpoints --some-dir --file3 --file4 ``` the remote will become: ``` some-dir --file1 --file2 --file3 --file4 ``` Hence, even though slightly inconvenient, this seems to be the best method for uploading big checkpoints, because of its efficiency and robustness.
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.05_0.25_epoch1	MinaMila	2025-06-16T07:21:18Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T07:19:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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NAMAA-Space/zarra	NAMAA-Space	2025-06-16T07:20:05Z	54	2	model2vec	[ "model2vec", "safetensors", "embeddings", "static-embeddings", "sentence-transformers", "sentence-similarity", "ar", "dataset:allenai/c4", "base_model:jinaai/jina-embeddings-v3", "base_model:finetune:jinaai/jina-embeddings-v3", "license:mit", "region:us" ]	sentence-similarity	2025-05-27T10:22:20Z	--- library_name: model2vec license: mit model_name: Abdelkareem/zarra tags: - embeddings - static-embeddings - sentence-transformers datasets: - allenai/c4 language: - ar base_model: - jinaai/jina-embeddings-v3 pipeline_tag: sentence-similarity --- # Zarra: Arabic Static Embedding Model ![image/png](https://cdn-uploads.huggingface.co/production/uploads/628f7a71dd993507cfcbe587/t4ALUMHL25wTuNzgNQwUg.png) Zarra is a static embedding model built using the Model2Vec distillation framework. It is a distilled version of a Sentence Transformer, specifically optimized for the Arabic language. Unlike traditional transformer-based models, Zarra produces static embeddings, enabling ultra-fast inference on both CPU and GPU—making it ideal for resource-constrained environments or real-time applications. ## Why Zarra? ⚡ Exceptional Speed: Delivers embeddings up to 500x faster than sentence transformers. 🧠 Compact & Efficient: Up to 50x smaller in size, allowing easy deployment on edge devices. 🧰 Versatile: Well-suited for search, clustering, classification, deduplication, and more. 🌍 Arabic-First: Specifically trained on high-quality Arabic data, ensuring relevance and performance across a range of Arabic NLP tasks. <p align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/628f7a71dd993507cfcbe587/3JEnPfgF2BfbN5H81K0XD.png" alt="Speed vs Performance Chart" width="700"/> </p> ## About Model2Vec The Model2Vec distillation technique transfers knowledge from large transformer models into lightweight static embedding spaces, preserving semantic quality while dramatically improving speed and efficiency. Zarra represents the best of both worlds: the semantic power of transformers and the speed and simplicity of static vectors. ## Installation Install model2vec using pip: ``` pip install model2vec ``` ## Usage ### Using Model2Vec The [Model2Vec library](https://github.com/MinishLab/model2vec) is the fastest and most lightweight way to run Model2Vec models. Load this model using the `from_pretrained` method: ```python from model2vec import StaticModel # Load a pretrained Model2Vec model model = StaticModel.from_pretrained("NAMAA-Space/zarra") # Compute text embeddings embeddings = model.encode(["Example sentence"]) ``` ### Using Sentence Transformers You can also use the [Sentence Transformers library](https://github.com/UKPLab/sentence-transformers) to load and use the model: ```python from sentence_transformers import SentenceTransformer # Load a pretrained Sentence Transformer model model = SentenceTransformer("NAMAA-Space/zarra") # Compute text embeddings embeddings = model.encode(["Example sentence"]) ``` ## How it Works Model2vec creates a small, fast, and powerful model that outperforms other static embedding models by a large margin on all tasks we could find, while being much faster to create than traditional static embedding models such as GloVe. Best of all, you don't need any data to distill a model using Model2Vec. It works by passing a vocabulary through a sentence transformer model, then reducing the dimensionality of the resulting embeddings using PCA, and finally weighting the embeddings using [SIF weighting](https://openreview.net/pdf?id=SyK00v5xx). During inference, we simply take the mean of all token embeddings occurring in a sentence. ## Benchmark on Arabic ## Speed \| Model \| Speed (sentences/second) \| Device \| \|---------------------------------------\|--------------------------\|--------\| \| zarra \| 26893.63 \| cpu \| \| bojji \| 27478.15 \| cpu \| \| potion-multilingual-128M \| 27145.31 \| cpu \| \| paraphrase-multilingual-MiniLM-L12-v2 \| 2363.24 \| cuda \| \| silma_ai_embedding_sts_v0.1 \| 627.13 \| cuda \| \| muffakir_embedding \| 621.77 \| cuda \| \| get_multilingual_base \| 895.41 \| cuda \| \| arabic_retrieval_v1.0 \| 618.56 \| cuda \| \| arabic_triplet_matryoshka_v2 \| 610.64 \| cuda \| - Zarra and Bojji excel in speed, achieving 26893.63 and 27478.15 sentences per second on CPU, respectively, far surpassing CUDA-based models like arabic_triplet_matryoshka_v2 (610.64). - Top Performer: Bojji is the fastest model, slightly ahead of Zarra and potion-multilingual-128M (27145.31), highlighting the efficiency of Model2Vec-based models on CPU. - Key Observation: The high speed of Zarra and Bojji on CPU makes them ideal for resource-constrained environments, offering significant advantages over CUDA-dependent models. ## Size of the Model \| Model \| Parameters (M) \| Size (MB) \| Relative to Largest (%) \| Less than Largest (x) \| \|----------------------------------\|----------------\|-----------\|-------------------------\|-----------------------\| \| zarra \| 64.00 \| 244.14 \| 41.92 \| 2.39 \| \| bojji \| 124.88 \| 476.40 \| 81.79 \| 1.22 \| \| potion-multilingual-128M \| 128.09 \| 488.63 \| 83.89 \| 1.19 \| \| paraphrase-multilingual-MiniLM-… \| 117.65 \| 448.82 \| 77.06 \| 1.30 \| \| silma_ai_embedding_sts_v0.1 \| 135.19 \| 515.72 \| 88.54 \| 1.13 \| \| muffakir_embedding \| 135.19 \| 515.72 \| 88.54 \| 1.13 \| \| arabic_retrieval_v1.0 \| 135.19 \| 515.73 \| 88.54 \| 1.13 \| \| arabic_triplet_matryoshka_v2 \| 135.19 \| 515.72 \| 88.54 \| 1.13 \| \| get_multilingual_base \| 305.37 \| 582.45 \| 100.00 \| 1.00 \| - Zarra is the smallest model, with only 64 million parameters and 244.14 MB in size, making it 2.39 times smaller than the largest model (get_multilingual_base). - Bojji is slightly larger at 124.88 million parameters and 476.40 MB, but still significantly smaller than most other models. - Top Performer: Zarra leads in compactness, offering the smallest footprint, which is critical for deployment on resource-limited devices. - Key Observation: The compact size of Zarra and Bojji aligns with their design goal of efficiency, making them highly suitable for edge computing and real-time applications. \| Model \| Avg \| MIRAC \| MLQAR \| Massi \| Multi \| STS17 \| STS22 \| XNLI_ \| \|---------------------------------------\|-------\|-------\|-------\|-------\|-------\|-------\|-------\|-------\| \| arabic_triplet_matryoshka_v2 \| 0.6610 \| 0.6262 \| 0.5093 \| 0.5577 \| 0.5868 \| 0.8531 \| 0.6396 \| 0.8542 \| \| muffakir_embedding \| 0.6494 \| 0.6424 \| 0.5267 \| 0.5462 \| 0.5943 \| 0.8485 \| 0.6291 \| 0.7583 \| \| arabic_retrieval_v1.0 \| 0.6473 \| 0.6159 \| 0.5674 \| 0.5832 \| 0.5993 \| 0.8002 \| 0.6254 \| 0.7393 \| \| gate_arabert-v1 \| 0.6444 \| 0.5774 \| 0.4808 \| 0.5345 \| 0.5847 \| 0.8278 \| 0.6310 \| 0.8746 \| \| get_multilingual_base \| 0.6440 \| 0.7177 \| 0.5698 \| 0.5071 \| 0.5521 \| 0.7881 \| 0.6145 \| 0.7584 \| \| arabic_sts_matryoshka \| 0.6413 \| 0.5828 \| 0.4840 \| 0.5457 \| 0.5494 \| 0.8290 \| 0.6242 \| 0.8740 \| \| silma_ai_embedding_sts_v0.1 \| 0.6138 \| 0.3799 \| 0.5011 \| 0.5600 \| 0.5749 \| 0.8559 \| 0.6122 \| 0.8125 \| \| Arabic-MiniLM-L12-v2-all-nli-triplet \| 0.5431 \| 0.2240 \| 0.3612 \| 0.4775 \| 0.5698 \| 0.8111 \| 0.5540 \| 0.8043 \| \| paraphrase-multilingual-MiniLM-L12-v2 \| 0.5208 \| 0.2191 \| 0.3496 \| 0.4515 \| 0.5573 \| 0.7916 \| 0.4908 \| 0.7859 \| \| bojji \| 0.5177 \| 0.2941 \| 0.3989 \| 0.4667 \| 0.5433 \| 0.7233 \| 0.5880 \| 0.6094 \| \| zarra \| 0.4822 \| 0.2295 \| 0.3473 \| 0.4119 \| 0.5237 \| 0.6469 \| 0.6218 \| 0.5942 \| \| potion-multilingual-128M \| 0.4699 \| 0.1658 \| 0.3150 \| 0.4285 \| 0.5338 \| 0.6511 \| 0.5951 \| 0.5999 \| \| all_minilm_l6_v2 \| 0.2843 \| 0.0005 \| 0.0064 \| 0.1905 \| 0.4934 \| 0.5089 \| 0.2518 \| 0.5384 \| ### Sorted by STS17_main (Score) \| Model Name \| STS17_main \| \|---------------------------------------\|------------\| \| silma_ai_embedding_sts_v0.1 \| 0.856 \| \| arabic_triplet_matryoshka_v2 \| 0.853 \| \| muffakir_embedding \| 0.849 \| \| arabic_sts_matryoshka \| 0.829 \| \| gate_arabert-v1 \| 0.828 \| \| Arabic-MiniLM-L12-v2-all-nli-triplet \| 0.811 \| \| arabic_retrieval_v1.0 \| 0.800 \| \| paraphrase-multilingual-MiniLM-L12-v2 \| 0.792 \| \| get_multilingual_base \| 0.788 \| \| bojji \| 0.723 \| \| potion-multilingual-128M \| 0.651 \| \| zarra \| 0.647 \| \| all_minilm_l6_v2 \| 0.509 \| ### Sorted by STS22.v2_main (Score) \| Model Name \| STS22.v2_main \| \|---------------------------------------\|---------------\| \| arabic_triplet_matryoshka_v2 \| 0.640 \| \| gate_arabert-v1 \| 0.631 \| \| muffakir_embedding \| 0.629 \| \| arabic_retrieval_v1.0 \| 0.625 \| \| arabic_sts_matryoshka \| 0.624 \| \| zarra \| 0.622 \| \| get_multilingual_base \| 0.615 \| \| silma_ai_embedding_sts_v0.1 \| 0.612 \| \| potion-multilingual-128M \| 0.595 \| \| bojji \| 0.588 \| \| Arabic-MiniLM-L12-v2-all-nli-triplet \| 0.554 \| \| paraphrase-multilingual-MiniLM-L12-v2 \| 0.491 \| \| all_minilm_l6_v2 \| 0.252 \| ## Additional Resources - [Zarra & Bojji Blog](https://kareemai.com/blog/posts/minishlab/blog_zaraah.html) - [NAMAA Collection](https://huggingface.co/collections/NAMAA-Space/zaraah-683f1f8a1eec1ec8f2badee5) - [MinishLab](https://minishlab.github.io/) - [Model2Vec Repo](https://github.com/MinishLab/model2vec)
allura-org/Q3-8B-Kintsugi	allura-org	2025-06-16T07:16:59Z	8	3	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "mergekit", "axolotl", "unsloth", "roleplay", "conversational", "dataset:PygmalionAI/PIPPA", "dataset:Alfitaria/nemotron-ultra-reasoning-synthkink", "dataset:PocketDoc/Dans-Prosemaxx-Gutenberg", "dataset:FreedomIntelligence/Medical-R1-Distill-Data", "dataset:cognitivecomputations/SystemChat-2.0", "dataset:allenai/tulu-3-sft-personas-instruction-following", "dataset:kalomaze/Opus_Instruct_25k", "dataset:simplescaling/s1K-claude-3-7-sonnet", "dataset:ai2-adapt-dev/flan_v2_converted", "dataset:grimulkan/theory-of-mind", "dataset:grimulkan/physical-reasoning", "dataset:nvidia/HelpSteer3", "dataset:nbeerbower/gutenberg2-dpo", "dataset:nbeerbower/gutenberg-moderne-dpo", "dataset:nbeerbower/Purpura-DPO", "dataset:antiven0m/physical-reasoning-dpo", "dataset:allenai/tulu-3-IF-augmented-on-policy-70b", "dataset:NobodyExistsOnTheInternet/system-message-DPO", "base_model:Qwen/Qwen3-8B-Base", "base_model:finetune:Qwen/Qwen3-8B-Base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-13T21:25:09Z	--- license: apache-2.0 base_model: Qwen/Qwen3-8B-Base library_name: transformers tags: - mergekit - axolotl - unsloth - roleplay - conversational datasets: - PygmalionAI/PIPPA - Alfitaria/nemotron-ultra-reasoning-synthkink - PocketDoc/Dans-Prosemaxx-Gutenberg - FreedomIntelligence/Medical-R1-Distill-Data - cognitivecomputations/SystemChat-2.0 - allenai/tulu-3-sft-personas-instruction-following - kalomaze/Opus_Instruct_25k - simplescaling/s1K-claude-3-7-sonnet - ai2-adapt-dev/flan_v2_converted - grimulkan/theory-of-mind - grimulkan/physical-reasoning - nvidia/HelpSteer3 - nbeerbower/gutenberg2-dpo - nbeerbower/gutenberg-moderne-dpo - nbeerbower/Purpura-DPO - antiven0m/physical-reasoning-dpo - allenai/tulu-3-IF-augmented-on-policy-70b - NobodyExistsOnTheInternet/system-message-DPO --- # Q3-8B-Kintsugi ![Sketch drawing of a picture of a Kitsune hugging a Fox plushie on her bed. Generated with Midjourney v7](https://cdn-uploads.huggingface.co/production/uploads/634262af8d8089ebaefd410e/o_fhP0riFrKh-5XyPxQyk.png) <small><i>get it? because kintsugi sounds like kitsune? hahaha-</i></small> # Overview *Q3-8B-Kintsugi* is a roleplaying model finetuned from [Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base). During testing, Kintsugi punched well above its weight class in terms of parameters, especially for 1-on-1 roleplaying and general storywriting. # Quantizations EXL3: - [Official EXL3 quant repo](https://huggingface.co/allura-quants/allura-org_Q3-8B-Kintsugi-EXL3) GGUF: - [Official static GGUF quants](https://huggingface.co/allura-quants/allura-org_Q3-8B-Kintsugi-GGUF) MLX: - [8, 6, and 4bpw MLX-formrt quants by soundTeam](https://huggingface.co/collections/allura-quants/q3-8b-kintsugi-mlx-684fc48444f1214749f538c4) # Usage - Format is plain-old ChatML (please note that, unlike regular Qwen 3, you do not need to prefill empty think tags for it not to reason -- see below). - Settings used by testers varied, but we generally stayed around 0.9 temperature and 0.1 min p. Do not use repetition penalties (DRY included). They break it. - Any system prompt can likely be used, but I used the Shingame system prompt (link will be added later i promise) - The official instruction following version of Qwen3-8B was not used as a base. Instruction-following is trained in post-hoc, and "thinking" traces were not included. __As a result of this, "thinking" will not function.__ # Training Process 1. The [base model](https://huggingface.co/Qwen/Qwen3-8B-Base) first went through a supervised finetune on a corpus of instruction following data, roleplay conversations, and human writing based on the [Ink](https://huggingface.co/collections/allura-org/ink-6772fd1442308781594bbabb)/[Bigger Body](https://huggingface.co/collections/allura-org/bigger-body-67b277af0861cec33b54745d)/[Remnant](https://huggingface.co/collections/allura-org/remnant-6817c2113bbb2aed501513d0) lineage. 2. Finally, a KTO reinforcement learning phase steered the model away from the very purple prose the initial merge had, and improved its logical+spatial reasoning and sense of overall "intelligence". Both stages here are very similar to [Q3-30B-A3B-Designant](https://huggingface.co/allura-org/Q3-30B-A3B-Designant), which went through a very similar process with the same data. # Credits - Fizz - Training, Data Wrangling - Toaster, Mango, Bot, probably others I forgot ;-; - Testing - inflatebot - original Designant model card that this one was yoinked from - Artus - Funding - Alibaba - Making the original model - Axolotl, Unsloth, Huggingface - Making the frameworks used to train this model (Axolotl was used for the SFT process, and Unsloth+TRL was used for the KTO process) - All quanters, inside and outside the org, specifically Artus, Lyra, and soundTeam/Heni We would like to thank the Allura community on Discord, especially Curse, Heni, Artus and Mawnipulator, for their companionship and moral support. You all mean the world to us <3
Triangle104/Q3-8B-Kintsugi-Q4_K_M-GGUF	Triangle104	2025-06-16T07:15:51Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "axolotl", "unsloth", "roleplay", "conversational", "llama-cpp", "gguf-my-repo", "dataset:PygmalionAI/PIPPA", "dataset:Alfitaria/nemotron-ultra-reasoning-synthkink", "dataset:PocketDoc/Dans-Prosemaxx-Gutenberg", "dataset:FreedomIntelligence/Medical-R1-Distill-Data", "dataset:cognitivecomputations/SystemChat-2.0", "dataset:allenai/tulu-3-sft-personas-instruction-following", "dataset:kalomaze/Opus_Instruct_25k", "dataset:simplescaling/s1K-claude-3-7-sonnet", "dataset:ai2-adapt-dev/flan_v2_converted", "dataset:grimulkan/theory-of-mind", "dataset:grimulkan/physical-reasoning", "dataset:nvidia/HelpSteer3", "dataset:nbeerbower/gutenberg2-dpo", "dataset:nbeerbower/gutenberg-moderne-dpo", "dataset:nbeerbower/Purpura-DPO", "dataset:antiven0m/physical-reasoning-dpo", "dataset:allenai/tulu-3-IF-augmented-on-policy-70b", "dataset:NobodyExistsOnTheInternet/system-message-DPO", "base_model:allura-org/Q3-8B-Kintsugi", "base_model:quantized:allura-org/Q3-8B-Kintsugi", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T07:12:01Z	--- license: apache-2.0 base_model: allura-org/Q3-8B-Kintsugi library_name: transformers tags: - mergekit - axolotl - unsloth - roleplay - conversational - llama-cpp - gguf-my-repo datasets: - PygmalionAI/PIPPA - Alfitaria/nemotron-ultra-reasoning-synthkink - PocketDoc/Dans-Prosemaxx-Gutenberg - FreedomIntelligence/Medical-R1-Distill-Data - cognitivecomputations/SystemChat-2.0 - allenai/tulu-3-sft-personas-instruction-following - kalomaze/Opus_Instruct_25k - simplescaling/s1K-claude-3-7-sonnet - ai2-adapt-dev/flan_v2_converted - grimulkan/theory-of-mind - grimulkan/physical-reasoning - nvidia/HelpSteer3 - nbeerbower/gutenberg2-dpo - nbeerbower/gutenberg-moderne-dpo - nbeerbower/Purpura-DPO - antiven0m/physical-reasoning-dpo - allenai/tulu-3-IF-augmented-on-policy-70b - NobodyExistsOnTheInternet/system-message-DPO --- # Triangle104/Q3-8B-Kintsugi-Q4_K_M-GGUF This model was converted to GGUF format from [`allura-org/Q3-8B-Kintsugi`](https://huggingface.co/allura-org/Q3-8B-Kintsugi) 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/allura-org/Q3-8B-Kintsugi) for more details on the model. --- Q3-8B-Kintsugi is a roleplaying model finetuned from Qwen3-8B-Base. During testing, Kintsugi punched well above its weight class in terms of parameters, especially for 1-on-1 roleplaying and general storywriting. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Q3-8B-Kintsugi-Q4_K_M-GGUF --hf-file q3-8b-kintsugi-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Q3-8B-Kintsugi-Q4_K_M-GGUF --hf-file q3-8b-kintsugi-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Q3-8B-Kintsugi-Q4_K_M-GGUF --hf-file q3-8b-kintsugi-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Q3-8B-Kintsugi-Q4_K_M-GGUF --hf-file q3-8b-kintsugi-q4_k_m.gguf -c 2048 ```
Triangle104/Q3-8B-Kintsugi-Q4_K_S-GGUF	Triangle104	2025-06-16T07:14:57Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "axolotl", "unsloth", "roleplay", "conversational", "llama-cpp", "gguf-my-repo", "dataset:PygmalionAI/PIPPA", "dataset:Alfitaria/nemotron-ultra-reasoning-synthkink", "dataset:PocketDoc/Dans-Prosemaxx-Gutenberg", "dataset:FreedomIntelligence/Medical-R1-Distill-Data", "dataset:cognitivecomputations/SystemChat-2.0", "dataset:allenai/tulu-3-sft-personas-instruction-following", "dataset:kalomaze/Opus_Instruct_25k", "dataset:simplescaling/s1K-claude-3-7-sonnet", "dataset:ai2-adapt-dev/flan_v2_converted", "dataset:grimulkan/theory-of-mind", "dataset:grimulkan/physical-reasoning", "dataset:nvidia/HelpSteer3", "dataset:nbeerbower/gutenberg2-dpo", "dataset:nbeerbower/gutenberg-moderne-dpo", "dataset:nbeerbower/Purpura-DPO", "dataset:antiven0m/physical-reasoning-dpo", "dataset:allenai/tulu-3-IF-augmented-on-policy-70b", "dataset:NobodyExistsOnTheInternet/system-message-DPO", "base_model:allura-org/Q3-8B-Kintsugi", "base_model:quantized:allura-org/Q3-8B-Kintsugi", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T06:39:48Z	--- license: apache-2.0 base_model: allura-org/Q3-8B-Kintsugi library_name: transformers tags: - mergekit - axolotl - unsloth - roleplay - conversational - llama-cpp - gguf-my-repo datasets: - PygmalionAI/PIPPA - Alfitaria/nemotron-ultra-reasoning-synthkink - PocketDoc/Dans-Prosemaxx-Gutenberg - FreedomIntelligence/Medical-R1-Distill-Data - cognitivecomputations/SystemChat-2.0 - allenai/tulu-3-sft-personas-instruction-following - kalomaze/Opus_Instruct_25k - simplescaling/s1K-claude-3-7-sonnet - ai2-adapt-dev/flan_v2_converted - grimulkan/theory-of-mind - grimulkan/physical-reasoning - nvidia/HelpSteer3 - nbeerbower/gutenberg2-dpo - nbeerbower/gutenberg-moderne-dpo - nbeerbower/Purpura-DPO - antiven0m/physical-reasoning-dpo - allenai/tulu-3-IF-augmented-on-policy-70b - NobodyExistsOnTheInternet/system-message-DPO --- # Triangle104/Q3-8B-Kintsugi-Q4_K_S-GGUF This model was converted to GGUF format from [`allura-org/Q3-8B-Kintsugi`](https://huggingface.co/allura-org/Q3-8B-Kintsugi) 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/allura-org/Q3-8B-Kintsugi) for more details on the model. --- Q3-8B-Kintsugi is a roleplaying model finetuned from Qwen3-8B-Base. During testing, Kintsugi punched well above its weight class in terms of parameters, especially for 1-on-1 roleplaying and general storywriting. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Q3-8B-Kintsugi-Q4_K_S-GGUF --hf-file q3-8b-kintsugi-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Q3-8B-Kintsugi-Q4_K_S-GGUF --hf-file q3-8b-kintsugi-q4_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Q3-8B-Kintsugi-Q4_K_S-GGUF --hf-file q3-8b-kintsugi-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Q3-8B-Kintsugi-Q4_K_S-GGUF --hf-file q3-8b-kintsugi-q4_k_s.gguf -c 2048 ```
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.05_0.5_epoch2	MinaMila	2025-06-16T07:14:19Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T07:12:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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fsa32442/cl-tohoku-bert-base-japanese-v3-ner-wikipedia-dataset	fsa32442	2025-06-16T07:13:00Z	0	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-06-16T07:12:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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AndyBlocker/ViStream	AndyBlocker	2025-06-16T07:10:28Z	0	0	pytorch	[ "pytorch", "computer-vision", "object-tracking", "spiking-neural-networks", "visual-streaming-perception", "energy-efficient", "cvpr-2025", "object-detection", "license:cc-by-4.0", "region:us" ]	object-detection	2025-06-16T05:52:58Z	--- license: cc-by-4.0 library_name: pytorch tags: - computer-vision - object-tracking - spiking-neural-networks - visual-streaming-perception - energy-efficient - cvpr-2025 pipeline_tag: object-detection --- # ViStream: Law-of-Charge-Conservation Inspired Spiking Neural Network for Visual Streaming Perception ViStream is a novel energy-efficient framework for Visual Streaming Perception (VSP) that leverages Spiking Neural Networks (SNNs) with Law of Charge Conservation (LoCC) properties. ## Model Details ### Model Description - Developed by: Kang You, Ziling Wei, Jing Yan, Boning Zhang, Qinghai Guo, Yaoyu Zhang, Zhezhi He - Model type: Spiking Neural Network for Visual Streaming Perception - Language(s): PyTorch implementation - License: CC-BY-4.0 - Paper: [CVPR 2025](https://openaccess.thecvf.com/content/CVPR2025/papers/You_VISTREAM_Improving_Computation_Efficiency_of_Visual_Streaming_Perception_via_Law-of-Charge-Conservation_CVPR_2025_paper.pdf) - Repository: [GitHub](https://github.com/Intelligent-Computing-Research-Group/ViStream) ### Model Architecture ViStream introduces two key innovations: 1. Law of Charge Conservation (LoCC) property in ST-BIF neurons 2. Differential Encoding (DiffEncode) scheme for temporal optimization The framework achieves significant computational reduction while maintaining accuracy equivalent to ANN counterparts. ## Uses ### Direct Use ViStream can be directly used for: - Multiple Object Tracking (MOT) - Single Object Tracking (SOT) - Video Object Segmentation (VOS) - Multiple Object Tracking and Segmentation (MOTS) - Pose Tracking ### Downstream Use The model can be fine-tuned for various visual streaming perception tasks in: - Autonomous driving - UAV navigation - AR/VR applications - Real-time surveillance ## Bias, Risks, and Limitations ### Limitations - Requires specific hardware optimization for maximum energy benefits - Performance may vary with different frame rates - Limited to visual perception tasks ### Recommendations - Test thoroughly on target hardware before deployment - Consider computational constraints of edge devices - Validate performance on domain-specific datasets ## How to Get Started with the Model ```python from huggingface_hub import hf_hub_download import torch # Download the checkpoint checkpoint_path = hf_hub_download( repo_id="AndyBlocker/ViStream", filename="checkpoint-90.pth" ) # Load the model (requires ViStream implementation) checkpoint = torch.load(checkpoint_path, map_location='cpu') ``` For complete usage examples, see the [GitHub repository](https://github.com/Intelligent-Computing-Research-Group/ViStream). ## Training Details ### Training Data The model was trained on multiple datasets for various visual streaming perception tasks including object tracking, video object segmentation, and pose tracking. ### Training Procedure Training Details: - Framework: PyTorch - Optimization: Energy-efficient SNN training with Law of Charge Conservation - Architecture: ResNet-based backbone with spike quantization layers ## Evaluation The model demonstrates competitive performance across multiple visual streaming perception tasks while achieving significant energy efficiency improvements compared to traditional ANN-based approaches. Detailed evaluation results are available in the [CVPR 2025 paper](https://openaccess.thecvf.com/content/CVPR2025/papers/You_VISTREAM_Improving_Computation_Efficiency_of_Visual_Streaming_Perception_via_Law-of-Charge-Conservation_CVPR_2025_paper.pdf). ## Model Card Authors Kang You, Ziling Wei, Jing Yan, Boning Zhang, Qinghai Guo, Yaoyu Zhang, Zhezhi He ## Model Card Contact For questions about this model, please open an issue in the [GitHub repository](https://github.com/Intelligent-Computing-Research-Group/ViStream). ## Citation ```bibtex @inproceedings{you2025vistream, title={VISTREAM: Improving Computation Efficiency of Visual Streaming Perception via Law-of-Charge-Conservation Inspired Spiking Neural Network}, author={You, Kang and Wei, Ziling and Yan, Jing and Zhang, Boning and Guo, Qinghai and Zhang, Yaoyu and He, Zhezhi}, booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference}, pages={8796--8805}, year={2025} } ```
phospho-app/jmota27-ACT_BBOX-boats_datasets-u50na	phospho-app	2025-06-16T07:10:27Z	0	0	null	[ "phosphobot", "act", "region:us" ]	null	2025-06-16T07:05:08Z	--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` The object 'black boat' was detected in 0 episodes in secondary_0 camera (should be: 10 episodes min). This is not enough to train a model. Check your dataset: https://lerobot-visualize-dataset.hf.space/jmota27/boats_datasets/ and rephrase the instruction. ``` ## Training parameters: - Dataset: [jmota27/boats_datasets](https://huggingface.co/datasets/jmota27/boats_datasets) - Wandb run URL: None - Epochs: None - Batch size: 100 - Training steps: 10000 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.5_0.15_0.75_epoch2	MinaMila	2025-06-16T07:08:40Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T07:06:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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NICFRU/nc_ner_bert_model_german_alle_ner_tags	NICFRU	2025-06-16T07:08:17Z	0	0	null	[ "safetensors", "bert", "region:us" ]	null	2025-06-16T07:05:43Z	# nc_ner_bert_model This model is a fine-tuned version of bert-base-german-cased on the german-ler dataset. It achieves the following results on the evaluation set: Loss: 0.011337515898048878 F1: 0.9723312768741821 Precision: 0.9669083472764455 Recall: 0.9778153788306072 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - num_epochs: 3 ### Training results \| loss \| epoch \| step \| eval_loss \| eval_f1 \| eval_precision \| eval_recall \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 0.7592 \| 0.029958058717795086 \| 50 \| nan \| nan \| nan \| nan \| \| 0.1908 \| 0.05991611743559017 \| 100 \| nan \| nan \| nan \| nan \| \| 0.1288 \| 0.08987417615338526 \| 150 \| nan \| nan \| nan \| nan \| \| 0.0877 \| 0.11983223487118035 \| 200 \| nan \| nan \| nan \| nan \| \| nan \| 0.11983223487118035 \| 200 \| 0.07891597598791122 \| 0.71008601947254 \| 0.6772448611611973 \| 0.7462745877210412 \| \| 0.0782 \| 0.14979029358897544 \| 250 \| nan \| nan \| nan \| nan \| \| 0.071 \| 0.17974835230677053 \| 300 \| nan \| nan \| nan \| nan \| \| 0.0605 \| 0.2097064110245656 \| 350 \| nan \| nan \| nan \| nan \| \| 0.0491 \| 0.2396644697423607 \| 400 \| nan \| nan \| nan \| nan \| \| nan \| 0.2396644697423607 \| 400 \| 0.05076289176940918 \| 0.799463190184049 \| 0.7723652528245971 \| 0.828531690840453 \| \| 0.0471 \| 0.2696225284601558 \| 450 \| nan \| nan \| nan \| nan \| \| 0.0463 \| 0.2995805871779509 \| 500 \| nan \| nan \| nan \| nan \| \| 0.0544 \| 0.32953864589574594 \| 550 \| nan \| nan \| nan \| nan \| \| 0.0355 \| 0.35949670461354105 \| 600 \| nan \| nan \| nan \| nan \| \| nan \| 0.35949670461354105 \| 600 \| 0.03874693065881729 \| 0.8478779208392943 \| 0.8152971386647102 \| 0.8831710709318498 \| \| 0.0479 \| 0.3894547633313361 \| 650 \| nan \| nan \| nan \| nan \| \| 0.0343 \| 0.4194128220491312 \| 700 \| nan \| nan \| nan \| nan \| \| 0.033 \| 0.44937088076692633 \| 750 \| nan \| nan \| nan \| nan \| \| 0.0367 \| 0.4793289394847214 \| 800 \| nan \| nan \| nan \| nan \| \| nan \| 0.4793289394847214 \| 800 \| 0.03735911101102829 \| 0.8572247706422018 \| 0.8258147670778863 \| 0.891118617126962 \| \| 0.0343 \| 0.5092869982025164 \| 850 \| nan \| nan \| nan \| nan \| \| 0.0361 \| 0.5392450569203115 \| 900 \| nan \| nan \| nan \| nan \| \| 0.0408 \| 0.5692031156381067 \| 950 \| nan \| nan \| nan \| nan \| \| 0.0315 \| 0.5991611743559018 \| 1000 \| nan \| nan \| nan \| nan \| \| nan \| 0.5991611743559018 \| 1000 \| 0.030343208461999893 \| 0.8759884281581485 \| 0.8510399100618324 \| 0.902443870454997 \| \| 0.036 \| 0.6291192330736968 \| 1050 \| nan \| nan \| nan \| nan \| \| 0.0225 \| 0.6590772917914919 \| 1100 \| nan \| nan \| nan \| nan \| \| 0.0275 \| 0.689035350509287 \| 1150 \| nan \| nan \| nan \| nan \| \| 0.0296 \| 0.7189934092270821 \| 1200 \| nan \| nan \| nan \| nan \| \| nan \| 0.7189934092270821 \| 1200 \| 0.03183047100901604 \| 0.8875096974398758 \| 0.866830839174086 \| 0.9091992847208424 \| \| 0.04 \| 0.7489514679448772 \| 1250 \| nan \| nan \| nan \| nan \| \| 0.0281 \| 0.7789095266626722 \| 1300 \| nan \| nan \| nan \| nan \| \| 0.0433 \| 0.8088675853804673 \| 1350 \| nan \| nan \| nan \| nan \| \| 0.0299 \| 0.8388256440982624 \| 1400 \| nan \| nan \| nan \| nan \| \| nan \| 0.8388256440982624 \| 1400 \| 0.027965761721134186 \| 0.8886951467596629 \| 0.8671077504725898 \| 0.9113848599244984 \| \| 0.0381 \| 0.8687837028160575 \| 1450 \| nan \| nan \| nan \| nan \| \| 0.0298 \| 0.8987417615338527 \| 1500 \| nan \| nan \| nan \| nan \| \| 0.0278 \| 0.9286998202516477 \| 1550 \| nan \| nan \| nan \| nan \| \| 0.0261 \| 0.9586578789694428 \| 1600 \| nan \| nan \| nan \| nan \| \| nan \| 0.9586578789694428 \| 1600 \| 0.028123166412115097 \| 0.9013783731314309 \| 0.8811918770165117 \| 0.9225114245976554 \| \| 0.0262 \| 0.9886159376872379 \| 1650 \| nan \| nan \| nan \| nan \| \| 0.0178 \| 1.0185739964050329 \| 1700 \| nan \| nan \| nan \| nan \| \| 0.0132 \| 1.048532055122828 \| 1750 \| nan \| nan \| nan \| nan \| \| 0.0157 \| 1.078490113840623 \| 1800 \| nan \| nan \| nan \| nan \| \| nan \| 1.078490113840623 \| 1800 \| 0.028561240062117577 \| 0.9077876791941109 \| 0.8856548856548857 \| 0.9310550367574012 \| \| 0.0132 \| 1.1084481725584183 \| 1850 \| nan \| nan \| nan \| nan \| \| 0.0155 \| 1.1384062312762133 \| 1900 \| nan \| nan \| nan \| nan \| \| 0.0129 \| 1.1683642899940083 \| 1950 \| nan \| nan \| nan \| nan \| \| 0.0148 \| 1.1983223487118035 \| 2000 \| nan \| nan \| nan \| nan \| \| nan \| 1.1983223487118035 \| 2000 \| 0.026516983285546303 \| 0.9101913315111284 \| 0.8946459412780656 \| 0.9262865090403338 \| \| 0.0106 \| 1.2282804074295985 \| 2050 \| nan \| nan \| nan \| nan \| \| 0.0108 \| 1.2582384661473935 \| 2100 \| nan \| nan \| nan \| nan \| \| 0.0165 \| 1.2881965248651888 \| 2150 \| nan \| nan \| nan \| nan \| \| 0.0167 \| 1.3181545835829838 \| 2200 \| nan \| nan \| nan \| nan \| \| nan \| 1.3181545835829838 \| 2200 \| 0.02606791816651821 \| 0.9090380703283929 \| 0.8869565217391304 \| 0.932247168686668 \| \| 0.0151 \| 1.348112642300779 \| 2250 \| nan \| nan \| nan \| nan \| \| 0.0169 \| 1.378070701018574 \| 2300 \| nan \| nan \| nan \| nan \| \| 0.0165 \| 1.4080287597363692 \| 2350 \| nan \| nan \| nan \| nan \| \| 0.0127 \| 1.4379868184541642 \| 2400 \| nan \| nan \| nan \| nan \| \| nan \| 1.4379868184541642 \| 2400 \| 0.02857920527458191 \| 0.9142130490071408 \| 0.9003853564547206 \| 0.9284720842439896 \| \| 0.0131 \| 1.4679448771719592 \| 2450 \| nan \| nan \| nan \| nan \| \| 0.0147 \| 1.4979029358897544 \| 2500 \| nan \| nan \| nan \| nan \| \| 0.0131 \| 1.5278609946075494 \| 2550 \| nan \| nan \| nan \| nan \| \| 0.0116 \| 1.5578190533253444 \| 2600 \| nan \| nan \| nan \| nan \| \| nan \| 1.5578190533253444 \| 2600 \| 0.0249184537678957 \| 0.9242824958370065 \| 0.9115146831530139 \| 0.9374130737134909 \| \| 0.0166 \| 1.5877771120431396 \| 2650 \| nan \| nan \| nan \| nan \| \| 0.0145 \| 1.6177351707609346 \| 2700 \| nan \| nan \| nan \| nan \| \| 0.0152 \| 1.6476932294787296 \| 2750 \| nan \| nan \| nan \| nan \| \| 0.0119 \| 1.6776512881965249 \| 2800 \| nan \| nan \| nan \| nan \| \| nan \| 1.6776512881965249 \| 2800 \| 0.024047361686825752 \| 0.9198402649264634 \| 0.9021780664883454 \| 0.9382078283330022 \| \| 0.0157 \| 1.70760934691432 \| 2850 \| nan \| nan \| nan \| nan \| \| 0.0139 \| 1.737567405632115 \| 2900 \| nan \| nan \| nan \| nan \| \| 0.0161 \| 1.76752546434991 \| 2950 \| nan \| nan \| nan \| nan \| \| 0.0118 \| 1.7974835230677053 \| 3000 \| nan \| nan \| nan \| nan \| \| nan \| 1.7974835230677053 \| 3000 \| 0.02312026545405388 \| 0.9283470749901845 \| 0.9173617846750728 \| 0.9395986489171468 \| \| 0.0097 \| 1.8274415817855003 \| 3050 \| nan \| nan \| nan \| nan \| \| 0.016 \| 1.8573996405032953 \| 3100 \| nan \| nan \| nan \| nan \| \| 0.013 \| 1.8873576992210905 \| 3150 \| nan \| nan \| nan \| nan \| \| 0.0133 \| 1.9173157579388855 \| 3200 \| nan \| nan \| nan \| nan \| \| nan \| 1.9173157579388855 \| 3200 \| 0.023281875997781754 \| 0.9217849819353578 \| 0.9062980030721967 \| 0.9378104510232466 \| \| 0.0175 \| 1.9472738166566805 \| 3250 \| nan \| nan \| nan \| nan \| \| 0.0154 \| 1.9772318753744758 \| 3300 \| nan \| nan \| nan \| nan \| \| 0.0096 \| 2.007189934092271 \| 3350 \| nan \| nan \| nan \| nan \| \| 0.0057 \| 2.0371479928100658 \| 3400 \| nan \| nan \| nan \| nan \| \| nan \| 2.0371479928100658 \| 3400 \| 0.023734014481306076 \| 0.9255650818394388 \| 0.9080481743452494 \| 0.9437711106695807 \| \| 0.0083 \| 2.067106051527861 \| 3450 \| nan \| nan \| nan \| nan \| \| 0.0081 \| 2.097064110245656 \| 3500 \| nan \| nan \| nan \| nan \| \| 0.0075 \| 2.127022168963451 \| 3550 \| nan \| nan \| nan \| nan \| \| 0.0057 \| 2.156980227681246 \| 3600 \| nan \| nan \| nan \| nan \| \| nan \| 2.156980227681246 \| 3600 \| 0.025288647040724754 \| 0.9282016215688189 \| 0.9129515757109915 \| 0.9439697993244586 \| \| 0.0049 \| 2.1869382863990414 \| 3650 \| nan \| nan \| nan \| nan \| \| 0.0076 \| 2.2168963451168366 \| 3700 \| nan \| nan \| nan \| nan \| \| 0.0067 \| 2.2468544038346314 \| 3750 \| nan \| nan \| nan \| nan \| \| 0.009 \| 2.2768124625524266 \| 3800 \| nan \| nan \| nan \| nan \| \| nan \| 2.2768124625524266 \| 3800 \| 0.025000886991620064 \| 0.926643935703848 \| 0.9090214067278287 \| 0.9449632425988476 \| \| 0.0067 \| 2.306770521270222 \| 3850 \| nan \| nan \| nan \| nan \| \| 0.0081 \| 2.3367285799880166 \| 3900 \| nan \| nan \| nan \| nan \| \| 0.0073 \| 2.366686638705812 \| 3950 \| nan \| nan \| nan \| nan \| \| 0.0059 \| 2.396644697423607 \| 4000 \| nan \| nan \| nan \| nan \| \| nan \| 2.396644697423607 \| 4000 \| 0.024387583136558533 \| 0.9357906087638466 \| 0.9235681114551083 \| 0.9483409497317703 \| \| 0.0073 \| 2.426602756141402 \| 4050 \| nan \| nan \| nan \| nan \| \| 0.0075 \| 2.456560814859197 \| 4100 \| nan \| nan \| nan \| nan \| \| 0.0052 \| 2.4865188735769923 \| 4150 \| nan \| nan \| nan \| nan \| \| 0.006 \| 2.516476932294787 \| 4200 \| nan \| nan \| nan \| nan \| \| nan \| 2.516476932294787 \| 4200 \| 0.024684011936187744 \| 0.9356576241308392 \| 0.9225569718037853 \| 0.9491357043512816 \| \| 0.0088 \| 2.5464349910125823 \| 4250 \| nan \| nan \| nan \| nan \| \| 0.0122 \| 2.5763930497303775 \| 4300 \| nan \| nan \| nan \| nan \| \| 0.008 \| 2.6063511084481723 \| 4350 \| nan \| nan \| nan \| nan \| \| 0.0072 \| 2.6363091671659675 \| 4400 \| nan \| nan \| nan \| nan \| \| nan \| 2.6363091671659675 \| 4400 \| 0.02404804341495037 \| 0.9307684796406601 \| 0.9151305683563749 \| 0.9469501291476257 \| \| 0.0075 \| 2.6662672258837627 \| 4450 \| nan \| nan \| nan \| nan \| \| 0.0072 \| 2.696225284601558 \| 4500 \| nan \| nan \| nan \| nan \| \| 0.0061 \| 2.7261833433193527 \| 4550 \| nan \| nan \| nan \| nan \| \| 0.0057 \| 2.756141402037148 \| 4600 \| nan \| nan \| nan \| nan \| \| nan \| 2.756141402037148 \| 4600 \| 0.026269957423210144 \| 0.9321470473210794 \| 0.9176130895091434 \| 0.9471488178025035 \| \| 0.0038 \| 2.786099460754943 \| 4650 \| nan \| nan \| nan \| nan \| \| 0.008 \| 2.8160575194727384 \| 4700 \| nan \| nan \| nan \| nan \| \| 0.0073 \| 2.846015578190533 \| 4750 \| nan \| nan \| nan \| nan \| \| 0.0065 \| 2.8759736369083284 \| 4800 \| nan \| nan \| nan \| nan \| \| nan \| 2.8759736369083284 \| 4800 \| 0.02427930384874344 \| 0.9350088356567839 \| 0.9241218707549 \| 0.9461553745281145 \| \| 0.0056 \| 2.9059316956261236 \| 4850 \| nan \| nan \| nan \| nan \| \| 0.0074 \| 2.9358897543439184 \| 4900 \| nan \| nan \| nan \| nan \| \| 0.0059 \| 2.9658478130617136 \| 4950 \| nan \| nan \| nan \| nan \| \| 0.006 \| 2.995805871779509 \| 5000 \| nan \| nan \| nan \| nan \| \| nan \| 2.995805871779509 \| 5000 \| 0.025616737082600594 \| 0.9314559499364427 \| 0.9170196380438969 \| 0.9463540631829922 \| \| 0.004 \| 3.0257639304973036 \| 5050 \| nan \| nan \| nan \| nan \| \| 0.0047 \| 3.055721989215099 \| 5100 \| nan \| nan \| nan \| nan \| \| 0.0026 \| 3.085680047932894 \| 5150 \| nan \| nan \| nan \| nan \| \| 0.0047 \| 3.115638106650689 \| 5200 \| nan \| nan \| nan \| nan \| \| nan \| 3.115638106650689 \| 5200 \| 0.02595394104719162 \| 0.935866053069617 \| 0.9225868725868726 \| 0.9495330816610371 \| \| 0.0036 \| 3.145596165368484 \| 5250 \| nan \| nan \| nan \| nan \| \| 0.0035 \| 3.1755542240862793 \| 5300 \| nan \| nan \| nan \| nan \| \| 0.0034 \| 3.205512282804074 \| 5350 \| nan \| nan \| nan \| nan \| \| 0.0025 \| 3.2354703415218693 \| 5400 \| nan \| nan \| nan \| nan \| \| nan \| 3.2354703415218693 \| 5400 \| 0.02661316469311714 \| 0.9393134651322907 \| 0.9300740163615115 \| 0.9487383270415259 \| \| 0.0041 \| 3.2654284002396645 \| 5450 \| nan \| nan \| nan \| nan \| \| 0.0033 \| 3.2953864589574597 \| 5500 \| nan \| nan \| nan \| nan \| \| 0.0042 \| 3.3253445176752545 \| 5550 \| nan \| nan \| nan \| nan \| \| 0.0033 \| 3.3553025763930497 \| 5600 \| nan \| nan \| nan \| nan \| \| nan \| 3.3553025763930497 \| 5600 \| 0.02527858316898346 \| 0.9387915764613265 \| 0.9300058490933906 \| 0.947744883767137 \| \| 0.0016 \| 3.385260635110845 \| 5650 \| nan \| nan \| nan \| nan \| \| 0.0027 \| 3.4152186938286397 \| 5700 \| nan \| nan \| nan \| nan \| \| 0.0032 \| 3.445176752546435 \| 5750 \| nan \| nan \| nan \| nan \| \| 0.0054 \| 3.47513481126423 \| 5800 \| nan \| nan \| nan \| nan \| \| nan \| 3.47513481126423 \| 5800 \| 0.026085887104272842 \| 0.9393850083505256 \| 0.9290711232024874 \| 0.9499304589707928 \| \| 0.0041 \| 3.5050928699820254 \| 5850 \| nan \| nan \| nan \| nan \| \| 0.0031 \| 3.53505092869982 \| 5900 \| nan \| nan \| nan \| nan \| \| 0.0043 \| 3.5650089874176154 \| 5950 \| nan \| nan \| nan \| nan \| \| 0.0036 \| 3.5949670461354106 \| 6000 \| nan \| nan \| nan \| nan \| \| nan \| 3.5949670461354106 \| 6000 \| 0.026285560801625252 \| 0.9379647749510763 \| 0.9240408714092925 \| 0.9523147228293265 \| \| 0.003 \| 3.6249251048532054 \| 6050 \| nan \| nan \| nan \| nan \| \| 0.0036 \| 3.6548831635710006 \| 6100 \| nan \| nan \| nan \| nan \| \| 0.003 \| 3.684841222288796 \| 6150 \| nan \| nan \| nan \| nan \| \| 0.0018 \| 3.7147992810065906 \| 6200 \| nan \| nan \| nan \| nan \| \| nan \| 3.7147992810065906 \| 6200 \| 0.026966776698827744 \| 0.9423455332546242 \| 0.9333463262521926 \| 0.9515199682098152 \| \| 0.002 \| 3.744757339724386 \| 6250 \| nan \| nan \| nan \| nan \| \| 0.0031 \| 3.774715398442181 \| 6300 \| nan \| nan \| nan \| nan \| \| 0.0026 \| 3.804673457159976 \| 6350 \| nan \| nan \| nan \| nan \| \| 0.0032 \| 3.834631515877771 \| 6400 \| nan \| nan \| nan \| nan \| \| nan \| 3.834631515877771 \| 6400 \| 0.02665964514017105 \| 0.9404937543031378 \| 0.9312426957537983 \| 0.9499304589707928 \| \| 0.0046 \| 3.8645895745955663 \| 6450 \| nan \| nan \| nan \| nan \| \| 0.0037 \| 3.894547633313361 \| 6500 \| nan \| nan \| nan \| nan \| \| 0.006 \| 3.9245056920311563 \| 6550 \| nan \| nan \| nan \| nan \| \| 0.0041 \| 3.9544637507489515 \| 6600 \| nan \| nan \| nan \| nan \| \| nan \| 3.9544637507489515 \| 6600 \| 0.025271492078900337 \| 0.9411996066863323 \| 0.9316721822075141 \| 0.9509239022451818 \| \| 0.0028 \| 3.9844218094667463 \| 6650 \| nan \| nan \| nan \| nan \| \| 0.003 \| 4.014379868184542 \| 6700 \| nan \| nan \| nan \| nan \| \| 0.0027 \| 4.044337926902337 \| 6750 \| nan \| nan \| nan \| nan \| \| 0.0017 \| 4.0742959856201315 \| 6800 \| nan \| nan \| nan \| nan \| \| nan \| 4.0742959856201315 \| 6800 \| 0.026743704453110695 \| 0.9429133858267716 \| 0.934269553345036 \| 0.951718656864693 \| \| 0.0029 \| 4.104254044337927 \| 6850 \| nan \| nan \| nan \| nan \| \| 0.0025 \| 4.134212103055722 \| 6900 \| nan \| nan \| nan \| nan \| \| 0.0017 \| 4.164170161773517 \| 6950 \| nan \| nan \| nan \| nan \| \| 0.0015 \| 4.194128220491312 \| 7000 \| nan \| nan \| nan \| nan \| \| nan \| 4.194128220491312 \| 7000 \| 0.026866145431995392 \| 0.9408062930186825 \| 0.9312828499124002 \| 0.9505265249354262 \| \| 0.0014 \| 4.224086279209107 \| 7050 \| nan \| nan \| nan \| nan \| \| 0.0023 \| 4.254044337926902 \| 7100 \| nan \| nan \| nan \| nan \| \| 0.0034 \| 4.284002396644698 \| 7150 \| nan \| nan \| nan \| nan \| \| 0.0027 \| 4.313960455362492 \| 7200 \| nan \| nan \| nan \| nan \| \| nan \| 4.313960455362492 \| 7200 \| 0.026673471555113792 \| 0.9432142505658893 \| 0.9344773790951638 \| 0.9521160341744487 \| \| 0.001 \| 4.343918514080288 \| 7250 \| nan \| nan \| nan \| nan \| \| 0.0016 \| 4.373876572798083 \| 7300 \| nan \| nan \| nan \| nan \| \| 0.0061 \| 4.403834631515878 \| 7350 \| nan \| nan \| nan \| nan \| \| 0.0015 \| 4.433792690233673 \| 7400 \| nan \| nan \| nan \| nan \| \| nan \| 4.433792690233673 \| 7400 \| 0.026809940114617348 \| 0.9424722194906088 \| 0.9330218068535826 \| 0.9521160341744487 \| \| 0.0022 \| 4.463750748951468 \| 7450 \| nan \| nan \| nan \| nan \| \| 0.001 \| 4.493708807669263 \| 7500 \| nan \| nan \| nan \| nan \| \| 0.0015 \| 4.5236668663870585 \| 7550 \| nan \| nan \| nan \| nan \| \| 0.0019 \| 4.553624925104853 \| 7600 \| nan \| nan \| nan \| nan \| \| nan \| 4.553624925104853 \| 7600 \| 0.02733566425740719 \| 0.9416846652267818 \| 0.9307199689501261 \| 0.9529107887939599 \| \| 0.0012 \| 4.583582983822648 \| 7650 \| nan \| nan \| nan \| nan \| \| 0.0025 \| 4.613541042540444 \| 7700 \| nan \| nan \| nan \| nan \| \| 0.001 \| 4.6434991012582385 \| 7750 \| nan \| nan \| nan \| nan \| \| 0.0015 \| 4.673457159976033 \| 7800 \| nan \| nan \| nan \| nan \| \| nan \| 4.673457159976033 \| 7800 \| 0.027779242023825645 \| 0.9427390791027155 \| 0.9337361138179692 \| 0.9519173455195709 \| \| 0.0021 \| 4.703415218693829 \| 7850 \| nan \| nan \| nan \| nan \| \| 0.0031 \| 4.733373277411624 \| 7900 \| nan \| nan \| nan \| nan \| \| 0.0017 \| 4.7633313361294185 \| 7950 \| nan \| nan \| nan \| nan \| \| 0.0022 \| 4.793289394847214 \| 8000 \| nan \| nan \| nan \| nan \| \| nan \| 4.793289394847214 \| 8000 \| 0.02728326804935932 \| 0.9410609037328094 \| 0.9306392073052263 \| 0.951718656864693 \| \| 0.0023 \| 4.823247453565009 \| 8050 \| nan \| nan \| nan \| nan \| \| 0.0019 \| 4.853205512282804 \| 8100 \| nan \| nan \| nan \| nan \| \| 0.0011 \| 4.883163571000599 \| 8150 \| nan \| nan \| nan \| nan \| \| 0.0028 \| 4.913121629718394 \| 8200 \| nan \| nan \| nan \| nan \| \| nan \| 4.913121629718394 \| 8200 \| 0.02626235969364643 \| 0.9436411920920625 \| 0.9343591741332294 \| 0.9531094774488377 \| \| 0.002 \| 4.943079688436189 \| 8250 \| nan \| nan \| nan \| nan \| \| 0.0013 \| 4.973037747153985 \| 8300 \| nan \| nan \| nan \| nan \| \| 0.0023 \| 5.002995805871779 \| 8350 \| nan \| nan \| nan \| nan \| \| 0.001 \| 5.032953864589574 \| 8400 \| nan \| nan \| nan \| nan \| \| nan \| 5.032953864589574 \| 8400 \| 0.02664945460855961 \| 0.9427953607234125 \| 0.9328924333787201 \| 0.9529107887939599 \| \| 0.0008 \| 5.06291192330737 \| 8450 \| nan \| nan \| nan \| nan \| \| 0.001 \| 5.092869982025165 \| 8500 \| nan \| nan \| nan \| nan \| \| 0.0019 \| 5.12282804074296 \| 8550 \| nan \| nan \| nan \| nan \| \| 0.0016 \| 5.152786099460755 \| 8600 \| nan \| nan \| nan \| nan \| \| nan \| 5.152786099460755 \| 8600 \| 0.02724417671561241 \| 0.942354905234214 \| 0.9316504854368932 \| 0.9533081661037155 \| \| 0.001 \| 5.18274415817855 \| 8650 \| nan \| nan \| nan \| nan \| \| 0.001 \| 5.2127022168963455 \| 8700 \| nan \| nan \| nan \| nan \| \| 0.0008 \| 5.24266027561414 \| 8750 \| nan \| nan \| nan \| nan \| \| 0.0009 \| 5.272618334331935 \| 8800 \| nan \| nan \| nan \| nan \| \| nan \| 5.272618334331935 \| 8800 \| 0.027002455666661263 \| 0.9444772593030125 \| 0.936 \| 0.9531094774488377 \| \| 0.0015 \| 5.302576393049731 \| 8850 \| nan \| nan \| nan \| nan \| \| 0.0027 \| 5.3325344517675255 \| 8900 \| nan \| nan \| nan \| nan \| \| 0.0015 \| 5.36249251048532 \| 8950 \| nan \| nan \| nan \| nan \| \| 0.0018 \| 5.392450569203116 \| 9000 \| nan \| nan \| nan \| nan \| \| nan \| 5.392450569203116 \| 9000 \| 0.026851218193769455 \| 0.9485511531638084 \| 0.941130451789556 \| 0.9560898072720048 \| \| 0.0011 \| 5.422408627920911 \| 9050 \| nan \| nan \| nan \| nan \| \| 0.001 \| 5.4523666866387055 \| 9100 \| nan \| nan \| nan \| nan \| \| 0.0009 \| 5.482324745356501 \| 9150 \| nan \| nan \| nan \| nan \| \| 0.0022 \| 5.512282804074296 \| 9200 \| nan \| nan \| nan \| nan \| \| nan \| 5.512282804074296 \| 9200 \| 0.026996750384569168 \| 0.9460842188114915 \| 0.9370493081270708 \| 0.9552950526524936 \| \| 0.0018 \| 5.542240862792091 \| 9250 \| nan \| nan \| nan \| nan \| \| 0.0008 \| 5.572198921509886 \| 9300 \| nan \| nan \| nan \| nan \| \| 0.0017 \| 5.602156980227681 \| 9350 \| nan \| nan \| nan \| nan \| \| 0.0009 \| 5.632115038945477 \| 9400 \| nan \| nan \| nan \| nan \| \| nan \| 5.632115038945477 \| 9400 \| 0.02744028903543949 \| 0.9479802955665025 \| 0.9401993355481728 \| 0.955891118617127 \| \| 0.0012 \| 5.662073097663272 \| 9450 \| nan \| nan \| nan \| nan \| \| 0.0016 \| 5.692031156381066 \| 9500 \| nan \| nan \| nan \| nan \| \| 0.0007 \| 5.721989215098862 \| 9550 \| nan \| nan \| nan \| nan \| \| 0.0006 \| 5.751947273816657 \| 9600 \| nan \| nan \| nan \| nan \| \| nan \| 5.751947273816657 \| 9600 \| 0.027505146339535713 \| 0.9460523725142744 \| 0.937560975609756 \| 0.9546989866878601 \| \| 0.0007 \| 5.781905332534452 \| 9650 \| nan \| nan \| nan \| nan \| \| 0.0007 \| 5.811863391252247 \| 9700 \| nan \| nan \| nan \| nan \| \| 0.0007 \| 5.841821449970042 \| 9750 \| nan \| nan \| nan \| nan \| \| 0.001 \| 5.871779508687837 \| 9800 \| nan \| nan \| nan \| nan \| \| nan \| 5.871779508687837 \| 9800 \| 0.02771810069680214 \| 0.9461561177281229 \| 0.9375731564572767 \| 0.954897675342738 \| \| 0.0011 \| 5.9017375674056325 \| 9850 \| nan \| nan \| nan \| nan \| \| 0.001 \| 5.931695626123427 \| 9900 \| nan \| nan \| nan \| nan \| \| 0.0008 \| 5.961653684841222 \| 9950 \| nan \| nan \| nan \| nan \| \| 0.002 \| 5.991611743559018 \| 10000 \| nan \| nan \| nan \| nan \| \| nan \| 5.991611743559018 \| 10000 \| 0.027671782299876213 \| 0.946808510638298 \| 0.9388552451650714 \| 0.954897675342738 \| \| nan \| 6.0 \| 10014 \| nan \| nan \| nan \| nan \| ## Framework versions - Transformers: 2.3.0 - Pytorch: (siehe Umgebung) - Datasets: (siehe Umgebung) - Tokenizers: (siehe Umgebung)
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.05_0.5_epoch1	MinaMila	2025-06-16T07:07:47Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T07:05:59Z	--- 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. 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]
LarryAIDraw/Genshin_Yae_Miko	LarryAIDraw	2025-06-16T07:04:33Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-06-16T06:22:38Z	--- license: creativeml-openrail-m --- https://civitai.com/models/1662698/genshin-yae-miko
Hectore/80s_commercial_screenshot	Hectore	2025-06-16T07:04:12Z	222	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:mit", "region:us" ]	text-to-image	2025-05-26T10:27:37Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/80S_COMMERCIALS_SCREENSHOT_e000001_00_20250524094529.png - text: '-' output: url: images/80S_COMMERCIALS_SCREENSHOT_e000006_02_20250524124152.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: 80scommercial license: mit --- # 80S COMMERCIAL SCREENSHOT <Gallery /> ## Model description LoRA for Flux designed to generate screenshots inspired by 1980s commercials. Features low-resolution images, retro graphics, overlaid text, and color palettes typical of the TV era. Ideal for creating nostalgic, vintage broadcast-style scenes. ## Trigger words You should use `80scommercial` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Hectore/80s_commercial_screenshot/tree/main) them in the Files & versions tab.
Wilbur1240/ppo-SnowballTarget	Wilbur1240	2025-06-16T07:03:43Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2025-06-16T07:03:34Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget 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: Wilbur1240/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
GAGABIG/CNN	GAGABIG	2025-06-16T07:02:28Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-13T09:57:53Z	--- license: apache-2.0 ---
BootesVoid/cmbyjnk1403xvrdqsg2kyovgu_cmbypcpjv048qrdqs299msggc	BootesVoid	2025-06-16T07:00:50Z	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	2025-06-16T07:00:49Z	--- 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: EMILY01 --- # Cmbyjnk1403Xvrdqsg2Kyovgu_Cmbypcpjv048Qrdqs299Msggc <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 `EMILY01` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "EMILY01", "lora_weights": "https://huggingface.co/BootesVoid/cmbyjnk1403xvrdqsg2kyovgu_cmbypcpjv048qrdqs299msggc/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('BootesVoid/cmbyjnk1403xvrdqsg2kyovgu_cmbypcpjv048qrdqs299msggc', weight_name='lora.safetensors') image = pipeline('EMILY01').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/BootesVoid/cmbyjnk1403xvrdqsg2kyovgu_cmbypcpjv048qrdqs299msggc/discussions) to add images that show off what you’ve made with this LoRA.
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.5_0.15_0.75_epoch1	MinaMila	2025-06-16T07:00:41Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T06:58:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
Baron-qui/distilhubert-finetuned-gtzan	Baron-qui	2025-06-16T06:59:19Z	1	0	transformers	[ "transformers", "tensorboard", "safetensors", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:ntu-spml/distilhubert", "base_model:finetune:ntu-spml/distilhubert", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2025-06-14T00:12:46Z	--- library_name: transformers license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.84 --- <!-- 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. --> # distilhubert-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.5618 - Accuracy: 0.84 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 6 - eval_batch_size: 6 - 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_ratio: 0.1 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.8647 \| 1.0 \| 150 \| 1.7671 \| 0.58 \| \| 1.0983 \| 2.0 \| 300 \| 1.1722 \| 0.65 \| \| 0.875 \| 3.0 \| 450 \| 0.9809 \| 0.73 \| \| 0.596 \| 4.0 \| 600 \| 0.9323 \| 0.75 \| \| 0.4549 \| 5.0 \| 750 \| 0.6444 \| 0.82 \| \| 0.1644 \| 6.0 \| 900 \| 0.5420 \| 0.85 \| \| 0.136 \| 7.0 \| 1050 \| 0.5333 \| 0.82 \| \| 0.1289 \| 8.0 \| 1200 \| 0.6917 \| 0.82 \| \| 0.029 \| 9.0 \| 1350 \| 0.5613 \| 0.85 \| \| 0.0409 \| 10.0 \| 1500 \| 0.5618 \| 0.84 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
Anni0401/Olaf_Scholz_Llama-8B_v0.1	Anni0401	2025-06-16T06:59:09Z	0	0	peft	[ "peft", "safetensors", "llama", "arxiv:1910.09700", "base_model:unsloth/Meta-Llama-3.1-8B-Instruct", "base_model:adapter:unsloth/Meta-Llama-3.1-8B-Instruct", "region:us" ]	null	2025-06-16T06:56:22Z	--- base_model: unsloth/Meta-Llama-3.1-8B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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numiros/Comma-Epsilon-v0.1-exl2	numiros	2025-06-16T06:55:19Z	0	0	null	[ "exl2", "base_model:numiros/Comma-Epsilon-v0.1", "base_model:finetune:numiros/Comma-Epsilon-v0.1", "license:apache-2.0", "region:us" ]	null	2025-06-16T05:47:23Z	--- license: apache-2.0 base_model: - numiros/Comma-Epsilon-v0.1 tags: - exl2 --- [4bpw](https://huggingface.co/numiros/Comma-Epsilon-v0.1-exl2/tree/4bpw) [5bpw](https://huggingface.co/numiros/Comma-Epsilon-v0.1-exl2/tree/5bpw)
John6666/satyr-remix-ankara-illustrious-v17-sdxl	John6666	2025-06-16T06:55:10Z	0	1	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "girls", "fantasy", "paintery", "styles", "prompt comphrehension", "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	2025-06-16T06:49:23Z	--- 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 - girls - fantasy - paintery - styles - prompt comphrehension - illustrious base_model: OnomaAIResearch/Illustrious-xl-early-release-v0 --- Original model is [here](https://civitai.com/models/974951?modelVersionId=1905968). This model created by [Labdoge207](https://civitai.com/user/Labdoge207).
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.05_0.75_epoch1	MinaMila	2025-06-16T06:54:20Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T06:52:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.5_0.25_0.05_epoch2	MinaMila	2025-06-16T06:52:31Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T06:50:25Z	--- 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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MalvinasMan/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-arctic_opaque_jaguar	MalvinasMan	2025-06-16T06:50:44Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am arctic opaque jaguar", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-29T13:03:46Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-arctic_opaque_jaguar tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am arctic opaque jaguar - unsloth - trl licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-arctic_opaque_jaguar 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="MalvinasMan/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-arctic_opaque_jaguar", 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: 4.48.2 - Pytorch: 2.5.1 - Datasets: 3.6.0 - 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}} } ```
rafaelrocha1214/msa0o	rafaelrocha1214	2025-06-16T06:49:42Z	0	0	null	[ "license:bigcode-openrail-m", "region:us" ]	null	2025-06-16T06:49:42Z	--- license: bigcode-openrail-m ---
davidamaral1029/msa0o	davidamaral1029	2025-06-16T06:49:42Z	0	0	null	[ "license:bigcode-openrail-m", "region:us" ]	null	2025-06-16T06:49:42Z	--- license: bigcode-openrail-m ---
westlake-repl/SaProt_1.3B_AFDB_OMG_NCBI	westlake-repl	2025-06-16T06:48:45Z	197	0	null	[ "pytorch", "safetensors", "esm", "license:mit", "region:us" ]	null	2025-05-06T12:26:20Z	--- license: mit --- We further trained a 1.3 billion parameter version of the SaProt model, setting the context length to 1536 during training, and used a combined dataset of AFDB, OMG_prot50, and NCBI (70% identity filtering), totaling 383 million sequences. The training strategy is similar to that of [SaProt-O](https://github.com/westlake-repl/Denovo-Pinal/wiki/Tutorial), employing multimodal input integration (sequence and structural data) to ensure better alignment with real-world research applications. Specifically, the training data is a mixture of UniRef50 (40%), OMG (30%), and NCBI (30%). For sequences from OMG and NCBI lacking corresponding structural information, we employ mask language modeling where the model predicts the masked amino acid tokens. For the UniRef50 dataset, which includes structural data, we applied four distinct training strategies, each sampled with equal probability (25%): - Predicting all amino acid tokens given partial masked structural tokens. - Predicting all amino acid tokens given complete structural tokens. - Predicting partial amino acid tokens given their amino acid token context and partial masked structural tokens. - Predicting partial amino acid tokens given their amino acid token context and complete structural tokens. SaProt_1.3B_AFDB_OMG_NCBI is also a model very useful for protein editing. For instance, if you wish to modify certain regions of your protein—whether natural proteins or de novo designed—you can easily mask these amino acids by inputting partial or complete structures. Remarkably, the model functions effectively even if only sequence data is provided. If you have text data and would like to incorporate it, please refer to [SaProt-T/O](http://113.45.254.183:9527/). The relevant link can be found in the interface of [Pinal](http://www.denovo-pinal.com/). ### Loading model from huggingface > SaProt_1.3B_AFDB_OMG_NCBI, unlike [SaProt_650M_AF2](https://huggingface.co/westlake-repl/SaProt_650M_AF2), does not support loading from the esm library The following code shows how to load the model. ``` from transformers import EsmTokenizer, EsmForMaskedLM model_path = "/your/path/to/SaProt_1.3B_AFDB_OMG_NCBI" tokenizer = EsmTokenizer.from_pretrained(model_path) model = EsmForMaskedLM.from_pretrained(model_path) #################### Example #################### device = "cuda" model.to(device) seq = "M#EvVpQpL#VyQdYaKv" # Here "#" represents lower plDDT regions (plddt < 70) tokens = tokenizer.tokenize(seq) print(tokens) inputs = tokenizer(seq, return_tensors="pt") inputs = {k: v.to(device) for k, v in inputs.items()} outputs = model(**inputs) print(outputs.logits.shape) """ ['M#', 'Ev', 'Vp', 'Qp', 'L#', 'Vy', 'Qd', 'Ya', 'Kv'] torch.Size([1, 11, 446]) """ ```
Alptekinege/Huihui-MoE-1.5B-A0.6B-abliterated-Q6_K-GGUF	Alptekinege	2025-06-16T06:45:28Z	0	0	transformers	[ "transformers", "gguf", "moe", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:huihui-ai/Huihui-MoE-1.5B-A0.6B-abliterated", "base_model:quantized:huihui-ai/Huihui-MoE-1.5B-A0.6B-abliterated", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T06:45:19Z	--- license: apache-2.0 base_model: huihui-ai/Huihui-MoE-1.5B-A0.6B-abliterated library_name: transformers license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE pipeline_tag: text-generation tags: - moe - llama-cpp - gguf-my-repo extra_gated_prompt: 'Usage Warnings “Risk of Sensitive or Controversial Outputs“: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs. “Not Suitable for All Audiences:“ Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security. “Legal and Ethical Responsibilities“: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences. “Research and Experimental Use“: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications. “Monitoring and Review Recommendations“: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content. “No Default Safety Guarantees“: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.' --- # Alptekinege/Huihui-MoE-1.5B-A0.6B-abliterated-Q6_K-GGUF This model was converted to GGUF format from [`huihui-ai/Huihui-MoE-1.5B-A0.6B-abliterated`](https://huggingface.co/huihui-ai/Huihui-MoE-1.5B-A0.6B-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/huihui-ai/Huihui-MoE-1.5B-A0.6B-abliterated) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Alptekinege/Huihui-MoE-1.5B-A0.6B-abliterated-Q6_K-GGUF --hf-file huihui-moe-1.5b-a0.6b-abliterated-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Alptekinege/Huihui-MoE-1.5B-A0.6B-abliterated-Q6_K-GGUF --hf-file huihui-moe-1.5b-a0.6b-abliterated-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Alptekinege/Huihui-MoE-1.5B-A0.6B-abliterated-Q6_K-GGUF --hf-file huihui-moe-1.5b-a0.6b-abliterated-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Alptekinege/Huihui-MoE-1.5B-A0.6B-abliterated-Q6_K-GGUF --hf-file huihui-moe-1.5b-a0.6b-abliterated-q6_k.gguf -c 2048 ```
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.5_0.25_0.05_epoch1	MinaMila	2025-06-16T06:44:21Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T06:42:37Z	--- 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. 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phucminh/deepseek-finetuned_new	phucminh	2025-06-16T06:40:31Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-16T06:38:06Z	--- base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: phucminh - License: apache-2.0 - Finetuned from model : unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
prithivMLmods/visionOCR-3B-061125	prithivMLmods	2025-06-16T06:38:00Z	36	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "text-generation-inference", "OCR", "Receipt", "VisionOCR", "Messy Handwriting OCR", "conversational", "en", "zh", "dataset:linxy/LaTeX_OCR", "dataset:mychen76/ds_receipts_v2_eval", "dataset:mychen76/invoices-and-receipts_ocr_v1", "dataset:prithivMLmods/Latex-KIE", "arxiv:2412.08746", "arxiv:2309.00071", "arxiv:2409.12191", "arxiv:2308.12966", "arxiv:2412.02210", "base_model:Qwen/Qwen2.5-VL-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-06-11T15:09:35Z	--- license: apache-2.0 language: - en - zh tags: - text-generation-inference - OCR - Receipt - VisionOCR - Messy Handwriting OCR datasets: - linxy/LaTeX_OCR - mychen76/ds_receipts_v2_eval - mychen76/invoices-and-receipts_ocr_v1 - prithivMLmods/Latex-KIE base_model: - Qwen/Qwen2.5-VL-3B-Instruct pipeline_tag: image-text-to-text library_name: transformers --- ![OCR.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Xn8x267VedkZf6HFRsROD.png) # visionOCR-3B-061125 > The visionOCR-3B-061125 model is a fine-tuned version of Qwen/Qwen2.5-VL-3B-Instruct, optimized for Document-Level Optical Character Recognition (OCR), long-context vision-language understanding, and accurate image-to-text conversion with mathematical LaTeX formatting. Built on top of the Qwen2.5-VL architecture, this model significantly improves document comprehension, structured data extraction, and visual reasoning across diverse input formats. # Key Enhancements * Advanced Document-Level OCR: Capable of extracting structured content from complex, multi-page documents such as invoices, academic papers, forms, and scanned reports. * Enhanced Long-Context Vision-Language Understanding: Designed to handle dense document layouts, long sequences of embedded text, tables, and diagrams with coherent cross-reference understanding. * State-of-the-Art Performance Across Resolutions: Achieves competitive results on OCR and visual QA benchmarks such as DocVQA, MathVista, RealWorldQA, and MTVQA. * Video Understanding up to 20+ minutes: Supports detailed comprehension of long-duration videos for content summarization, Q\&A, and multi-modal reasoning. * Visually-Grounded Device Interaction: Enables mobile/robotic device operation via visual inputs and text-based instructions using contextual understanding and decision-making logic. # Quick Start with Transformers ```python from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor from qwen_vl_utils import process_vision_info model = Qwen2_5_VLForConditionalGeneration.from_pretrained( "prithivMLmods/visionOCR-3B-061125", torch_dtype="auto", device_map="auto" ) processor = AutoProcessor.from_pretrained("prithivMLmods/visionOCR-3B-061125") messages = [ { "role": "user", "content": [ { "type": "image", "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", }, {"type": "text", "text": "Describe this image."}, ], } ] text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs, video_inputs = process_vision_info(messages) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt", ) inputs = inputs.to("cuda") generated_ids = model.generate(*inputs, max_new_tokens=128) generated_ids_trimmed = [ out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text) ``` # Intended Use This model is intended for: High-fidelity OCR from documents, forms, receipts, and printed or scanned materials. * Image and document-based question answering for educational and enterprise applications. * Extraction and LaTeX formatting of mathematical expressions from printed or handwritten content. * Retrieval and summarization from long documents, slides, and multi-modal inputs. * Multilingual OCR and structured content extraction for global use cases. * Robotic or mobile automation with vision-guided contextual interaction. # Limitations * May show degraded performance on extremely low-quality or occluded images. * Not optimized for real-time applications on low-resource or edge devices due to computational demands. * Variable accuracy on uncommon or low-resource languages/scripts. * Long video processing may require substantial memory and is not optimized for streaming applications. * Visual token settings affect performance; suboptimal configurations can impact results. * In rare cases, outputs may contain hallucinated or contextually misaligned information. ## References * DocVLM: Make Your VLM an Efficient Reader [https://arxiv.org/pdf/2412.08746v1](https://arxiv.org/pdf/2412.08746v1) * YaRN: Efficient Context Window Extension of Large Language Models [https://arxiv.org/pdf/2309.00071](https://arxiv.org/pdf/2309.00071) * Qwen2-VL: Enhancing Vision-Language Model’s Perception of the World at Any Resolution [https://arxiv.org/pdf/2409.12191](https://arxiv.org/pdf/2409.12191) * Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond [https://arxiv.org/pdf/2308.12966](https://arxiv.org/pdf/2308.12966) * A Comprehensive and Challenging OCR Benchmark for Evaluating Large Multimodal Models in Literacy [https://arxiv.org/pdf/2412.02210](https://arxiv.org/pdf/2412.02210)
prithivMLmods/visionOCR-3B-061125-GGUF	prithivMLmods	2025-06-16T06:37:56Z	131	0	transformers	[ "transformers", "gguf", "qwen2_5_vl", "text-generation-inference", "OCR", "image-text-to-text", "en", "base_model:prithivMLmods/visionOCR-3B-061125", "base_model:quantized:prithivMLmods/visionOCR-3B-061125", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	image-text-to-text	2025-06-12T05:09:58Z	--- license: apache-2.0 tags: - text-generation-inference - OCR base_model: - prithivMLmods/visionOCR-3B-061125 language: - en pipeline_tag: image-text-to-text library_name: transformers --- # visionOCR-3B-061125-GGUF > The visionOCR-3B-061125 model is a fine-tuned version of Qwen/Qwen2.5-VL-3B-Instruct, optimized for Document-Level Optical Character Recognition (OCR), long-context vision-language understanding, and accurate image-to-text conversion with mathematical LaTeX formatting. Built on top of the Qwen2.5-VL architecture, this model significantly improves document comprehension, structured data extraction, and visual reasoning across diverse input formats. ## Model Files \| File Name \| Size \| Format \| Description \| \|-----------\|------\|--------\|-------------\| \| visionOCR-3B-061125-BF16.gguf \| 6.18 GB \| BF16 \| Brain floating point 16-bit \| \| visionOCR-3B-061125-Q6_K.gguf \| 2.54 GB \| Q6_K \| 6-bit quantized \| \| visionOCR-3B-061125-Q5_K_M.gguf \| 2.22 GB \| Q5_K_M \| 5-bit quantized, medium quality \| \| visionOCR-3B-061125-Q4_K_M.gguf \| 1.93 GB \| Q4_K_M \| 4-bit quantized, medium quality \| \| visionOCR-3B-061125-Q3_K_M.gguf \| 1.59 GB \| Q3_K_M \| 3-bit quantized, medium quality \| \| visionOCR-3B-061125-Q3_K_S.gguf \| 1.45 GB \| Q3_K_S \| 3-bit quantized, small quality \| \| visionOCR-3B-061125-Q2_K.gguf \| 1.27 GB \| Q2_K \| 2-bit quantized \| ## Quants Usage (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) 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)
dgambettaphd/M_llm2_run2_gen10_WXS_doc1000_synt64_lr1e-04_acm_FRESH	dgambettaphd	2025-06-16T06:37:47Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T06:37:35Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Triangle104/Dolphin-Mistral-24B-Venice-Edition-Q8_0-GGUF	Triangle104	2025-06-16T06:36:03Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition", "base_model:quantized:cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-16T06:33:11Z	--- license: apache-2.0 base_model: cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition tags: - llama-cpp - gguf-my-repo --- # Triangle104/Dolphin-Mistral-24B-Venice-Edition-Q8_0-GGUF This model was converted to GGUF format from [`cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition`](https://huggingface.co/cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition) 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/cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition) for more details on the model. --- Dolphin Mistral 24B Venice Edition is a collaborative project we undertook with Venice.ai with the goal of creating the most uncensored version of Mistral 24B for use within the Venice ecosystem. Dolphin Mistral 24B Venice Edition is now live on https://venice.ai/ as “Venice Uncensored,” the new default model for all Venice users. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Dolphin-Mistral-24B-Venice-Edition-Q8_0-GGUF --hf-file dolphin-mistral-24b-venice-edition-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Dolphin-Mistral-24B-Venice-Edition-Q8_0-GGUF --hf-file dolphin-mistral-24b-venice-edition-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Dolphin-Mistral-24B-Venice-Edition-Q8_0-GGUF --hf-file dolphin-mistral-24b-venice-edition-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Dolphin-Mistral-24B-Venice-Edition-Q8_0-GGUF --hf-file dolphin-mistral-24b-venice-edition-q8_0.gguf -c 2048 ```
ka-ops/Meta-Llama-3.1-8B-Instruct-FP8	ka-ops	2025-06-16T06:35:16Z	0	0	null	[ "safetensors", "llama", "fp8", "vllm", "text-generation", "conversational", "en", "de", "fr", "it", "pt", "hi", "es", "th", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:quantized:meta-llama/Llama-3.1-8B-Instruct", "license:llama3.1", "compressed-tensors", "region:us" ]	text-generation	2025-06-16T06:24:00Z	--- tags: - fp8 - vllm language: - en - de - fr - it - pt - hi - es - th pipeline_tag: text-generation license: llama3.1 base_model: meta-llama/Meta-Llama-3.1-8B-Instruct --- # Meta-Llama-3.1-8B-Instruct-FP8 ## Model Overview - Model Architecture: Meta-Llama-3.1 - Input: Text - Output: Text - Model Optimizations: - Weight quantization: FP8 - Activation quantization: FP8 - Intended Use Cases: Intended for commercial and research use in multiple languages. Similarly to [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct), this models is intended for assistant-like chat. - Out-of-scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. - Release Date: 7/23/2024 - Version: 1.0 - License(s): [llama3.1](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B/blob/main/LICENSE) - Model Developers: Neural Magic Quantized version of [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct). It achieves an average score of 73.44 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 73.79. ### Model Optimizations This model was obtained by quantizing the weights and activations of [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) to FP8 data type, ready for inference with vLLM built from source. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks are quantized. Symmetric per-tensor quantization is applied, in which a single linear scaling maps the FP8 representations of the quantized weights and activations. [LLM Compressor](https://github.com/vllm-project/llm-compressor) is used for quantization with 512 sequences of UltraChat. ## Deployment ### Use with vLLM This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below. ```python from vllm import LLM, SamplingParams from transformers import AutoTokenizer model_id = "neuralmagic/Meta-Llama-3.1-8B-Instruct-FP8" sampling_params = SamplingParams(temperature=0.6, top_p=0.9, max_tokens=256) tokenizer = AutoTokenizer.from_pretrained(model_id) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] prompts = tokenizer.apply_chat_template(messages, tokenize=False) llm = LLM(model=model_id) outputs = llm.generate(prompts, sampling_params) generated_text = outputs[0].outputs[0].text print(generated_text) ``` vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details. ## Creation This model was created by applying [LLM Compressor with calibration samples from UltraChat](https://github.com/vllm-project/llm-compressor/blob/sa/big_model_support/examples/big_model_offloading/big_model_w8a8_calibrate.py), as presented in the code snipet below. ```python import torch from datasets import load_dataset from transformers import AutoTokenizer from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot from llmcompressor.transformers.compression.helpers import ( calculate_offload_device_map, custom_offload_device_map, ) recipe = """ quant_stage: quant_modifiers: QuantizationModifier: ignore: ["lm_head"] config_groups: group_0: weights: num_bits: 8 type: float strategy: tensor dynamic: false symmetric: true input_activations: num_bits: 8 type: float strategy: tensor dynamic: false symmetric: true targets: ["Linear"] """ model_stub = "meta-llama/Meta-Llama-3.1-8B-Instruct" model_name = model_stub.split("/")[-1] device_map = calculate_offload_device_map( model_stub, reserve_for_hessians=False, num_gpus=1, torch_dtype="auto" ) model = SparseAutoModelForCausalLM.from_pretrained( model_stub, torch_dtype="auto", device_map=device_map ) tokenizer = AutoTokenizer.from_pretrained(model_stub) output_dir = f"./{model_name}-FP8" DATASET_ID = "HuggingFaceH4/ultrachat_200k" DATASET_SPLIT = "train_sft" NUM_CALIBRATION_SAMPLES = 512 MAX_SEQUENCE_LENGTH = 4096 ds = load_dataset(DATASET_ID, split=DATASET_SPLIT) ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES)) def preprocess(example): return { "text": tokenizer.apply_chat_template( example["messages"], tokenize=False, ) } ds = ds.map(preprocess) def tokenize(sample): return tokenizer( sample["text"], padding=False, max_length=MAX_SEQUENCE_LENGTH, truncation=True, add_special_tokens=False, ) ds = ds.map(tokenize, remove_columns=ds.column_names) oneshot( model=model, output_dir=output_dir, dataset=ds, recipe=recipe, max_seq_length=MAX_SEQUENCE_LENGTH, num_calibration_samples=NUM_CALIBRATION_SAMPLES, save_compressed=True, ) ``` ## Evaluation The model was evaluated on MMLU, ARC-Challenge, GSM-8K, Hellaswag, Winogrande and TruthfulQA. Evaluation was conducted using the Neural Magic fork of [lm-evaluation-harness](https://github.com/neuralmagic/lm-evaluation-harness/tree/llama_3.1_instruct) (branch llama_3.1_instruct) and the [vLLM](https://docs.vllm.ai/en/stable/) engine. This version of the lm-evaluation-harness includes versions of ARC-Challenge, GSM-8K, MMLU, and MMLU-cot that match the prompting style of [Meta-Llama-3.1-Instruct-evals](https://huggingface.co/datasets/meta-llama/Meta-Llama-3.1-8B-Instruct-evals). ### Accuracy #### Open LLM Leaderboard evaluation scores <table> <tr> <td><strong>Benchmark</strong> </td> <td><strong>Meta-Llama-3.1-8B-Instruct </strong> </td> <td><strong>Meta-Llama-3.1-8B-Instruct-FP8(this model)</strong> </td> <td><strong>Recovery</strong> </td> </tr> <tr> <td>MMLU (5-shot) </td> <td>67.95 </td> <td>67.97 </td> <td>100.0% </td> </tr> <tr> <td>MMLU-cot (0-shot) </td> <td>71.24 </td> <td>71.12 </td> <td>99.83% </td> </tr> <tr> <td>ARC Challenge (0-shot) </td> <td>82.00 </td> <td>81.66 </td> <td>99.59% </td> </tr> <tr> <td>GSM-8K-cot (8-shot, strict-match) </td> <td>81.96 </td> <td>81.12 </td> <td>98.98% </td> </tr> <tr> <td>Hellaswag (10-shot) </td> <td>80.46 </td> <td>80.4 </td> <td>99.93% </td> </tr> <tr> <td>Winogrande (5-shot) </td> <td>78.45 </td> <td>77.90 </td> <td>99.30% </td> </tr> <tr> <td>TruthfulQA (0-shot, mc2) </td> <td>54.50 </td> <td>53.92 </td> <td>98.94% </td> </tr> <tr> <td><strong>Average</strong> </td> <td><strong>73.79</strong> </td> <td><strong>73.44</strong> </td> <td><strong>99.52%</strong> </td> </tr> </table> ### Reproduction The results were obtained using the following commands: #### MMLU ``` lm_eval \ --model vllm \ --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-FP8",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \ --tasks mmlu \ --num_fewshot 5 \ --batch_size auto ``` #### MMLU-cot ``` lm_eval \ --model vllm \ --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-FP8",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \ --tasks mmlu_cot_0shot_llama_3.1_instruct \ --apply_chat_template \ --num_fewshot 0 \ --batch_size auto ``` #### ARC-Challenge ``` lm_eval \ --model vllm \ --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-FP8",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \ --tasks arc_challenge_llama_3.1_instruct \ --apply_chat_template \ --num_fewshot 0 \ --batch_size auto ``` #### GSM-8K ``` lm_eval \ --model vllm \ --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-FP8",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \ --tasks gsm8k_cot_llama_3.1_instruct \ --apply_chat_template \ --fewshot_as_multiturn \ --num_fewshot 8 \ --batch_size auto ``` #### Hellaswag ``` lm_eval \ --model vllm \ --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-FP8",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \ --tasks hellaswag \ --num_fewshot 10 \ --batch_size auto ``` #### Winogrande ``` lm_eval \ --model vllm \ --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-FP8",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \ --tasks winogrande \ --num_fewshot 5 \ --batch_size auto ``` #### TruthfulQA ``` lm_eval \ --model vllm \ --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-FP8",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \ --tasks truthfulqa \ --num_fewshot 0 \ --batch_size auto ```
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.15_0.15_epoch2	MinaMila	2025-06-16T06:33:39Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T06:31:49Z	--- 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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kaonan/my-gemma-2-finetuned-model	kaonan	2025-06-16T06:33:04Z	0	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-16T06:28:11Z	--- 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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mrpeerat/new_model	mrpeerat	2025-06-16T06:32:44Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "arxiv:2203.05482", "base_model:aisingapore/Gemma-SEA-LION-v3-9B", "base_model:merge:aisingapore/Gemma-SEA-LION-v3-9B", "base_model:aisingapore/Gemma-SEA-LION-v3-9B-IT", "base_model:merge:aisingapore/Gemma-SEA-LION-v3-9B-IT", "base_model:google/gemma-2-9b-it", "base_model:merge:google/gemma-2-9b-it", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T06:29:10Z	--- base_model: - aisingapore/Gemma-SEA-LION-v3-9B - aisingapore/Gemma-SEA-LION-v3-9B-IT - google/gemma-2-9b-it library_name: transformers tags: - mergekit - merge --- # linear_model This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Linear](https://arxiv.org/abs/2203.05482) merge method using [aisingapore/Gemma-SEA-LION-v3-9B-IT](https://huggingface.co/aisingapore/Gemma-SEA-LION-v3-9B-IT) as a base. ### Models Merged The following models were included in the merge: * /mnt/weka/aisg/peerat/LLaMA-Factory/Wangchanlion-gemma2-wangchanxFull-Syn120k-1e4-full * [aisingapore/Gemma-SEA-LION-v3-9B](https://huggingface.co/aisingapore/Gemma-SEA-LION-v3-9B) * [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: aisingapore/Gemma-SEA-LION-v3-9B parameters: weight: 1.0 density: 1 - model: /mnt/weka/aisg/peerat/LLaMA-Factory/Wangchanlion-gemma2-wangchanxFull-Syn120k-1e4-full parameters: weight: 1.0 density: 1 - model: google/gemma-2-9b-it parameters: weight: 1.0 density: 1 merge_method: linear base_model: aisingapore/Gemma-SEA-LION-v3-9B-IT parameters: # t: [0, 0.5, 1, 0.5, 0] weight: 1.0 density: 1 normalize: true int8_mask: true tokenizer: source: aisingapore/Gemma-SEA-LION-v3-9B-IT dtype: bfloat16 ```
prithivMLmods/Ross-640-BMath-1.5B	prithivMLmods	2025-06-16T06:31:02Z	16	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "math", "trl", "SFT", "conversational", "en", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-11T12:13:52Z	--- license: apache-2.0 language: - en base_model: - Qwen/Qwen2.5-1.5B-Instruct pipeline_tag: text-generation library_name: transformers tags: - text-generation-inference - math - trl - SFT --- ![89.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/vxacZQLTe3BR8F7Np_CQU.png) # Ross-640-BMath-1.5B > Ross-640-BMath-1.5B is an experimental, high-precision math explanation model fine-tuned on Qwen2-1.5B, designed to provide step-by-step mathematical derivations and detailed concept explanations across a wide range of mathematical domains. It is not optimized for general reasoning or conversation, and focuses primarily on structured, non-reasoning math workflows including algebra, calculus, number theory, and combinatorics. > \[!note] > GGUF: [https://huggingface.co/prithivMLmods/Ross-640-BMath-1.5B-GGUF](https://huggingface.co/prithivMLmods/Ross-640-BMath-1.5B-GGUF) --- ## Key Features 1. Hard Math Concept Focus Specializes in algebra, calculus, combinatorics, linear algebra, number theory, and more—delivering fine-tuned, low-latency outputs ideal for math-intensive applications. 2. Step-by-Step Explanations Emphasizes procedural clarity over abstract reasoning, offering structured, educational breakdowns of mathematical problems and derivations. 3. Symbolic Computation & Annotation Outputs include LaTeX-compatible syntax, inline math symbols, and clear annotation to support academic and technical workflows. 4. Educational Utility Optimized for learning and teaching, providing clear responses to mathematical queries with minimal noise or conversational drift. 5. Lightweight Architecture Built on Qwen2-1.5B and fine-tuned for efficiency and precision, making it suitable for deployment in resource-constrained environments, educational tools, or math-centric chat interfaces. --- ## Quickstart with Transformers ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "prithivMLmods/Ross-640-BMath-1.5B" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Explain step-by-step how to integrate (x^2 + 1)/(x^3 + 3x) dx." messages = [ {"role": "system", "content": "You are a helpful assistant skilled in solving complex math problems with clear and structured steps."}, {"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] print(response) ``` --- ## Intended Use** * Detailed mathematical explanations and problem-solving * Education-focused tutoring and math derivation tools * Math-focused applications and formula documentation * Symbolic derivations and LaTeX generation * Integration with learning platforms and academic software --- ## Limitations * Not suitable for general-purpose conversation or reasoning tasks * Context length constraints may limit effectiveness on large proofs * May struggle with non-mathematical or open-ended creative tasks * Experimental: Fine-tuned primarily for explanation clarity, not deep symbolic reasoning or formal proof validation
prithivMLmods/Ross-640-BMath-1.5B-GGUF	prithivMLmods	2025-06-16T06:30:52Z	215	0	transformers	[ "transformers", "gguf", "qwen2", "text-generation-inference", "math", "text-generation", "en", "base_model:prithivMLmods/Ross-640-BMath-1.5B", "base_model:quantized:prithivMLmods/Ross-640-BMath-1.5B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-06-11T12:19:02Z	--- license: apache-2.0 language: - en base_model: - prithivMLmods/Ross-640-BMath-1.5B pipeline_tag: text-generation library_name: transformers tags: - text-generation-inference - math --- # Ross-640-BMath-1.5B-GGUF > Ross-640-BMath-1.5B is an experimental, high-precision math explanation model fine-tuned on Qwen2-1.5B, designed to provide step-by-step mathematical derivations and detailed concept explanations across a wide range of mathematical domains. It is not optimized for general reasoning or conversation, and focuses primarily on structured, non-reasoning math workflows including algebra, calculus, number theory, and combinatorics. ## Model Files \| File Name \| Size \| Format \| Description \| \|-----------\|------\|--------\|-------------\| \| Ross-640-BMath-1.5B.F32.gguf \| 6.18 GB \| F32 \| Full precision 32-bit floating point \| \| Ross-640-BMath-1.5B.F16.gguf \| 3.09 GB \| F16 \| Half precision 16-bit floating point \| \| Ross-640-BMath-1.5B.BF16.gguf \| 3.09 GB \| BF16 \| Brain floating point 16-bit \| \| Ross-640-BMath-1.5B.Q8_0.gguf \| 1.65 GB \| Q8_0 \| 8-bit quantized \| \| Ross-640-BMath-1.5B.Q6_K.gguf \| 1.27 GB \| Q6_K \| 6-bit quantized \| \| Ross-640-BMath-1.5B.Q5_K_M.gguf \| 1.13 GB \| Q5_K_M \| 5-bit quantized, medium quality \| \| Ross-640-BMath-1.5B.Q5_K_S.gguf \| 1.1 GB \| Q5_K_S \| 5-bit quantized, small quality \| \| Ross-640-BMath-1.5B.Q4_K_M.gguf \| 986 MB \| Q4_K_M \| 4-bit quantized, medium quality \| \| Ross-640-BMath-1.5B.Q4_K_S.gguf \| 940 MB \| Q4_K_S \| 4-bit quantized, small quality \| \| Ross-640-BMath-1.5B.Q3_K_L.gguf \| 880 MB \| Q3_K_L \| 3-bit quantized, large quality \| \| Ross-640-BMath-1.5B.Q3_K_M.gguf \| 824 MB \| Q3_K_M \| 3-bit quantized, medium quality \| \| Ross-640-BMath-1.5B.Q3_K_S.gguf \| 761 MB \| Q3_K_S \| 3-bit quantized, small quality \| \| Ross-640-BMath-1.5B.Q2_K.gguf \| 676 MB \| Q2_K \| 2-bit quantized \| ## Quants Usage (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) 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)
himedia/fincredit-gemma3-4b-lr5e05-bs2-r16-steps10-20250616_062824	himedia	2025-06-16T06:30:44Z	0	0	null	[ "safetensors", "financial", "credit-rating", "korean", "gemma", "unsloth", "fine-tuned", "text-generation", "conversational", "ko", "base_model:unsloth/Llama-3.2-3B-Instruct", "base_model:finetune:unsloth/Llama-3.2-3B-Instruct", "license:apache-2.0", "region:us" ]	text-generation	2025-06-16T06:30:36Z	--- language: ko license: apache-2.0 base_model: unsloth/Llama-3.2-3B-Instruct tags: - financial - credit-rating - korean - gemma - unsloth - fine-tuned model_name: FinCreditLlama-3.2-3B pipeline_tag: text-generation --- # FinCreditLlama-3.2-3B ## 모델 개요 FinCreditLlama-3.2-3B는 금융 신용 평가를 위해 특별히 설계된 한국어 언어 모델입니다. 베이스 모델: unsloth/Llama-3.2-3B-Instruct 데이터셋: himedia/financial_dummy_data_v2 학습 방법: LoRA (Low-Rank Adaptation) 학습 일시: 20250616_062824 ## 하이퍼파라미터 - Learning Rate: 5e-05 - Max Steps: 10 - Batch Size: 2 - Gradient Accumulation: 4 - LoRA r: 16 - LoRA alpha: 16 - Max Sequence Length: 2048 - Warmup Steps: 5 ## 사용 방법 ```python from transformers import AutoTokenizer, AutoModelForCausalLM # 모델과 토크나이저 로드 tokenizer = AutoTokenizer.from_pretrained("himedia/fincredit-gemma3-4b-lr5e05-bs2-r16-steps10-20250616_062824") model = AutoModelForCausalLM.from_pretrained("himedia/fincredit-gemma3-4b-lr5e05-bs2-r16-steps10-20250616_062824") # 간단한 추론 예제 prompt = "고객의 신용등급을 평가해주세요:" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_length=200) result = tokenizer.decode(outputs[0], skip_special_tokens=True) print(result) ``` ## 레포지토리명 구성 ``` fincredit-gemma3-4b-lr5e05-bs2-r16-steps10-20250616_062824 = fincredit-gemma3-4b-lr5e05-bs2-r16-steps10-20250616_062824 ``` - `fincredit-gemma3-4b`: 모델 기본명 - `lr5e05`: Learning Rate - `bs2`: Batch Size - `r16`: LoRA rank - `steps10`: 학습 스텝 - `20250616_062824`: 학습 시각 ## 성능 이 모델은 한국어 금융 텍스트에 대해 파인튜닝되어 신용 평가 관련 질의응답에 특화되어 있습니다. ## 라이선스 Apache 2.0
himedia/fincredit-gemma3-4b-lr5e05-bs2-r16-steps10-20250616_062452	himedia	2025-06-16T06:30:40Z	0	0	null	[ "safetensors", "gemma3", "financial", "credit-rating", "korean", "unsloth", "fine-tuned", "complete-model", "text-generation", "conversational", "ko", "base_model:unsloth/gemma-3-4b-it", "base_model:finetune:unsloth/gemma-3-4b-it", "license:apache-2.0", "region:us" ]	text-generation	2025-06-16T06:29:12Z	--- language: ko license: apache-2.0 base_model: unsloth/gemma-3-4b-it tags: - financial - credit-rating - korean - gemma3 - unsloth - fine-tuned - complete-model model_name: FinCreditGemma-3-4B pipeline_tag: text-generation --- # FinCreditGemma-3-4B ## 모델 개요 FinCreditGemma-3-4B는 금융 신용 평가를 위해 특별히 설계된 한국어 언어 모델입니다. 베이스 모델: unsloth/gemma-3-4b-it 데이터셋: himedia/financial_dummy_data_v2 학습 방법: LoRA (Low-Rank Adaptation) + 완전 병합 학습 일시: 20250616_062452 ## ✅ 주요 특징 - 완전한 모델: config.json 포함, vocab_size 정상 - Unsloth 기반: 고성능 파인튜닝 - 병합 완료: LoRA 어댑터가 베이스 모델에 완전 병합 - 즉시 사용 가능: AutoModelForCausalLM으로 직접 로드 가능 ## 하이퍼파라미터 - Learning Rate: 5e-05 - Max Steps: 10 - Batch Size: 2 - Gradient Accumulation: 4 - LoRA r: 16 - LoRA alpha: 16 - Max Sequence Length: 2048 - Warmup Steps: 5 ## 사용 방법 ```python from transformers import AutoTokenizer, AutoModelForCausalLM # 모델과 토크나이저 로드 (config.json 포함) tokenizer = AutoTokenizer.from_pretrained("himedia/fincredit-gemma3-4b-lr5e05-bs2-r16-steps10-20250616_062452") model = AutoModelForCausalLM.from_pretrained("himedia/fincredit-gemma3-4b-lr5e05-bs2-r16-steps10-20250616_062452") # 간단한 추론 예제 prompt = "고객의 신용등급을 평가해주세요:" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(inputs, max_length=200) result = tokenizer.decode(outputs[0], skip_special_tokens=True) print(result) ``` ## 기술적 세부사항 - Vocab Size: N/A - Model Type: gemma3 - Architecture: Gemma3ForConditionalGeneration - 저장 방식**: Unsloth LoRA → PEFT 병합 → 완전한 모델 ## 레포지토리명 구성 ``` fincredit-gemma3-4b-lr5e05-bs2-r16-steps10-20250616_062452 = fincredit-gemma3-4b-lr5e05-bs2-r16-steps10-20250616_062452 ``` - `fincredit-gemma3-4b`: 모델 기본명 - `lr5e05`: Learning Rate - `bs2`: Batch Size - `r16`: LoRA rank - `steps10`: 학습 스텝 - `20250616_062452`: 학습 시각 ## 성능 이 모델은 한국어 금융 텍스트에 대해 파인튜닝되어 신용 평가 관련 질의응답에 특화되어 있습니다. ## 라이선스 Apache 2.0
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.5_0.25_0.15_epoch1	MinaMila	2025-06-16T06:28:28Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T06:26:45Z	--- 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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onnx-community/indicwav2vec-hindi-ONNX	onnx-community	2025-06-16T06:22:33Z	0	0	transformers.js	[ "transformers.js", "onnx", "wav2vec2", "automatic-speech-recognition", "base_model:ai4bharat/indicwav2vec-hindi", "base_model:quantized:ai4bharat/indicwav2vec-hindi", "region:us" ]	automatic-speech-recognition	2025-06-16T06:22:10Z	--- library_name: transformers.js base_model: - ai4bharat/indicwav2vec-hindi --- # indicwav2vec-hindi (ONNX) This is an ONNX version of [ai4bharat/indicwav2vec-hindi](https://huggingface.co/ai4bharat/indicwav2vec-hindi). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
mlx-community/llm-jp-3.1-13b-instruct4	mlx-community	2025-06-16T06:21:21Z	0	0	mlx	[ "mlx", "safetensors", "llama", "text-generation", "conversational", "en", "ja", "base_model:llm-jp/llm-jp-3.1-13b-instruct4", "base_model:finetune:llm-jp/llm-jp-3.1-13b-instruct4", "license:apache-2.0", "region:us" ]	text-generation	2025-06-16T05:37:34Z	--- license: apache-2.0 language: - en - ja programming_language: - C - C++ - C# - Go - Java - JavaScript - Lua - PHP - Python - Ruby - Rust - Scala - TypeScript pipeline_tag: text-generation library_name: mlx inference: false base_model: llm-jp/llm-jp-3.1-13b-instruct4 tags: - mlx --- # mlx-community/llm-jp-3.1-13b-instruct4 This model [mlx-community/llm-jp-3.1-13b-instruct4](https://huggingface.co/mlx-community/llm-jp-3.1-13b-instruct4) was converted to MLX format from [llm-jp/llm-jp-3.1-13b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-13b-instruct4) using mlx-lm version 0.24.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/llm-jp-3.1-13b-instruct4") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.5_0.25_0.25_epoch2	MinaMila	2025-06-16T06:20:27Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T06:18:43Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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prithivMLmods/Procyon-1.5B-Qwen2-Theorem	prithivMLmods	2025-06-16T06:20:18Z	5	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "math", "theorem", "SFT", "trl", "conversational", "en", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-11T09:16:57Z	--- license: apache-2.0 language: - en base_model: - Qwen/Qwen2.5-1.5B-Instruct pipeline_tag: text-generation library_name: transformers tags: - text-generation-inference - math - theorem - SFT - trl --- ![67.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/pkjF_jYh5v6IELgIe-Ksj.png) # Procyon-1.5B-Qwen2-Theorem > Procyon-1.5B-Qwen2-Theorem is an experimental theorem explanation model fine-tuned on Qwen2-1.5B. Specially crafted for mathematical theorem understanding, structured concept breakdowns, and non-reasoning based explanation tasks, it targets domains where clarity and formal structure take precedence over freeform reasoning. > \[!note] > GGUF: [https://huggingface.co/prithivMLmods/Procyon-1.5B-Qwen2-Theorem-GGUF](https://huggingface.co/prithivMLmods/Procyon-1.5B-Qwen2-Theorem-GGUF) --- ## Key Features 1. Mathematical Theorem Explanation Designed to deliver structured, formal, and accessible explanations of theorems across pure and applied mathematics, including areas such as algebra, calculus, topology, and number theory. 2. Concept Breakdown without Deep Reasoning Focuses on clarity over inference, offering non-reasoning-based breakdowns suitable for educational tools, step-by-step formal writing, and documentation-heavy workflows. 3. Concise and Interpretable Output Outputs content that aligns with pedagogical clarity: definitions, hypotheses, conclusions, and related implications—all in clean, human-readable structure. 4. Multi-Format Support Capable of generating content in formats such as LaTeX, Markdown, JSON (structured concept trees), and plain text, suitable for academic publishing and automated knowledge bases. 5. Lightweight and Efficient With a 1.5B parameter footprint, it is ideal for deployment on edge devices, local academic tools, and integrated learning platforms, offering quick responses without heavy compute demands. --- ## Quickstart with Transformers ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "prithivMLmods/Procyon-1.5B-Qwen2-Theorem" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Explain the Fundamental Theorem of Calculus in simple terms with hypotheses and conclusion." messages = [ {"role": "system", "content": "You are an assistant skilled at explaining mathematical theorems in a structured and simple format."}, {"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] print(response) ``` --- ## Intended Use** * Theorem explanation and educational enrichment * Math-aware structured content generation * LaTeX and Markdown generation for academic writing * Technical teaching tools and tutoring support * Early-stage research on symbolic language learning --- ## Limitations * Not designed for deep reasoning or proof synthesis * May underperform in conversational, general-purpose tasks * Best suited for deterministic, formulaic, and structured outputs * Performance on non-mathematical or abstract logical tasks may be limited
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.15_0.25_epoch2	MinaMila	2025-06-16T06:20:02Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T06:18:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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openbmb/MiniCPM-V-2_6-int4	openbmb	2025-06-16T06:19:21Z	24,622	82	transformers	[ "transformers", "safetensors", "minicpmv", "feature-extraction", "minicpm-v", "vision", "ocr", "multi-image", "video", "custom_code", "image-text-to-text", "conversational", "multilingual", "dataset:openbmb/RLAIF-V-Dataset", "base_model:openbmb/MiniCPM-V-2_6", "base_model:quantized:openbmb/MiniCPM-V-2_6", "4-bit", "bitsandbytes", "region:us" ]	image-text-to-text	2024-08-04T05:55:10Z	--- pipeline_tag: image-text-to-text datasets: - openbmb/RLAIF-V-Dataset library_name: transformers language: - multilingual tags: - minicpm-v - vision - ocr - multi-image - video - custom_code base_model: - openbmb/MiniCPM-V-2_6 --- ## News <!-- omit in toc --> * [2025.01.14] 🔥🔥 We open source [MiniCPM-o 2.6](https://huggingface.co/openbmb/MiniCPM-o-2_6), with significant performance improvement over MiniCPM-V 2.6, and support real-time speech-to-speech conversation and multimodal live streaming. Try it now. ## MiniCPM-V 2.6 int4 This is the int4 quantized version of [MiniCPM-V 2.6](https://huggingface.co/openbmb/MiniCPM-V-2_6). Running with int4 version would use lower GPU memory (about 7GB). ## Usage Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.10： ``` Pillow==10.1.0 torch==2.1.2 torchvision==0.16.2 transformers==4.40.0 sentencepiece==0.1.99 accelerate==0.30.1 bitsandbytes==0.43.1 ``` ```python # test.py import torch from PIL import Image from transformers import AutoModel, AutoTokenizer model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2_6-int4', trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2_6-int4', trust_remote_code=True) model.eval() image = Image.open('xx.jpg').convert('RGB') question = 'What is in the image?' msgs = [{'role': 'user', 'content': [image, question]}] res = model.chat( image=None, msgs=msgs, tokenizer=tokenizer ) print(res) ## if you want to use streaming, please make sure sampling=True and stream=True ## the model.chat will return a generator res = model.chat( image=None, msgs=msgs, tokenizer=tokenizer, sampling=True, temperature=0.7, stream=True ) generated_text = "" for new_text in res: generated_text += new_text print(new_text, flush=True, end='') ```
nirma-meena-viral-video-original/Nirma.Meena.Viral.Video.Original.Link	nirma-meena-viral-video-original	2025-06-16T06:14:09Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-16T06:09:29Z	--- license: apache-2.0 --- [![My Image](https://cdnb.artstation.com/p/assets/images/images/076/569/795/large/full-video-video-adik-kakak-viral-tiktok-baju-biru-full-7-menit-link-hd-30.jpg)](https://tinyurl.com/38v3p999)
veddhanth/lora-trained-xl-stage-2-finetuned-enc-v2-spat-sneaker	veddhanth	2025-06-16T06:13:42Z	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	2025-06-16T05:58:58Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: a photo of sks sneaker 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 - veddhanth/lora-trained-xl-stage-2-finetuned-enc-v2-spat-sneaker <Gallery /> ## Model description These are veddhanth/lora-trained-xl-stage-2-finetuned-enc-v2-spat-sneaker 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: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of sks sneaker to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](veddhanth/lora-trained-xl-stage-2-finetuned-enc-v2-spat-sneaker/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]

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