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train · 881k rows

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golf2248/sn11-v5-15	golf2248	2025-05-26T12:20:29Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T12:20:26Z	--- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
golf2248/sn11-v5-14	golf2248	2025-05-26T12:20:24Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T12:20:20Z	--- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
golf2248/sn11-v5-13	golf2248	2025-05-26T12:20:19Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T12:20:15Z	--- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
luren87/artale_chatbot2	luren87	2025-05-26T12:18:55Z	0	0	null	[ "license:other", "region:us" ]	null	2025-05-26T12:18:55Z	--- license: other license_name: other license_link: LICENSE ---
dimsavva/devstal-experiment-lora	dimsavva	2025-05-26T12:18:42Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/Devstral-Small-2505-unsloth-bnb-4bit", "base_model:finetune:unsloth/Devstral-Small-2505-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-26T12:18:30Z	--- base_model: unsloth/Devstral-Small-2505-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mistral - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: dimsavva - License: apache-2.0 - Finetuned from model : unsloth/Devstral-Small-2505-unsloth-bnb-4bit This mistral 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)
g-assismoraes/gemma-3-4b-it-fpi-alpha-1.0-fromit-var-agnews	g-assismoraes	2025-05-26T12:15:08Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-26T12:10:27Z	--- 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]
shallow6414/sn11-3-15-1	shallow6414	2025-05-26T12:15:03Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T12:14:59Z	--- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
wanlucky/wanlucky	wanlucky	2025-05-26T12:10:46Z	0	0	adapter-transformers	[ "adapter-transformers", "finance", "feature-extraction", "aa", "dataset:nvidia/OpenMathReasoning", "base_model:nari-labs/Dia-1.6B", "base_model:adapter:nari-labs/Dia-1.6B", "license:apache-2.0", "region:us" ]	feature-extraction	2025-05-26T12:09:34Z	--- license: apache-2.0 datasets: - nvidia/OpenMathReasoning language: - aa metrics: - accuracy base_model: - nari-labs/Dia-1.6B new_version: nari-labs/Dia-1.6B pipeline_tag: feature-extraction library_name: adapter-transformers tags: - finance ---
bigband/InfiniteQuetzalcoatl	bigband	2025-05-26T12:10:17Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T12:01:00Z	--- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
sp-embraceable/Phi4-FT-unsloth-runpod-6000steps-e1-above90-Merged	sp-embraceable	2025-05-26T12:08:26Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T12:02:00Z	--- library_name: transformers tags: - unsloth - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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gijs/audsemthinker-qa-grpo	gijs	2025-05-26T12:08:20Z	2	0	null	[ "safetensors", "qwen2_5_omni_thinker", "audio", "reasoning", "audsem", "en", "dataset:gijs/AudSem", "arxiv:2505.14142", "license:apache-2.0", "region:us" ]	null	2025-05-21T20:27:32Z	--- tags: - audio - reasoning - audsem language: en license: apache-2.0 datasets: - gijs/AudSem --- # AudSemThinker-QA-GRPO Corresponding paper: https://arxiv.org/abs/2505.14142 ## Model Description `AudSemThinker-QA-GRPO` is an advanced variant of `AudSemThinker`, fine-tuned using Group Relative Policy Optimization (GRPO) with Verifiable Rewards (RLVR). This approach enhances reasoning capabilities and allows for controlled thinking budget during generation. It leverages the structured reasoning framework of `AudSemThinker` (thinking, semantic elements, answer phases) but is specifically optimized for multiple-choice audio question answering. This model is designed to produce accurate answers while maintaining a controlled reasoning length in its `<think>` section. ## How to Use To use `AudSemThinker-QA-GRPO` for audio question answering, you can load it using the `transformers` library. Ensure you have `torch`, `torchaudio`, and `soundfile` installed. ```python import soundfile as sf from transformers import Qwen2_5OmniForConditionalGeneration, Qwen2_5OmniProcessor from qwen_omni_utils import process_mm_info import torchaudio # default: Load the model on the available device(s) model = Qwen2_5OmniForConditionalGeneration.from_pretrained( "gijs/audsemthinker-qa-grpo", torch_dtype="auto", device_map="auto", trust_remote_code=True, low_cpu_mem_usage=True ) # We recommend enabling flash_attention_2 for better acceleration and memory saving. # model = Qwen2_5OmniForConditionalGeneration.from_pretrained( # "gijs/audsemthinker-qa-grpo", # torch_dtype="auto", # device_map="auto", # attn_implementation="flash_attention_2", # trust_remote_code=True, # low_cpu_mem_usage=True # ) processor = Qwen2_5OmniProcessor.from_pretrained("gijs/audsemthinker-qa-grpo", trust_remote_code=True) # Load and preprocess audio audio_file = "path/to/your/audio.wav" audio_input, sampling_rate = torchaudio.load(audio_file) if sampling_rate != processor.feature_extractor.sampling_rate: audio_input = torchaudio.transforms.Resample( orig_freq=sampling_rate, new_freq=processor.feature_extractor.sampling_rate )(audio_input) audio_input = audio_input.squeeze().numpy() # Example multiple-choice question question = "What type of sound is present in the audio? Options: (A) Speech (B) Music (C) Environmental Sound (D) Silence" user_prompt_text = f"You are given a question and an audio clip. Your task is to answer the question based on the audio clip. First, think about the question and the audio clip and put your thoughts in <think> and </think> tags. Then reason about the semantic elements involved in the audio clip and put your reasoning in <semantic_elements> and </semantic_elements> tags. Then answer the question based on the audio clip, put your answer in <answer> and </answer> tags.\nQuestion: {question}" # Conversation format conversation = [ { "role": "system", "content": [ {"type": "text", "text": "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech."} ], }, { "role": "user", "content": [ {"type": "audio", "audio": audio_input}, {"type": "text", "text": user_prompt_text} ], }, ] # Preparation for inference text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False) audios, images, videos = process_mm_info(conversation) inputs = processor( text=text, audio=audios, images=images, videos=videos, return_tensors="pt", padding=True ) inputs = inputs.to(model.device).to(model.dtype) # Inference: Generation of the output output_ids = model.generate(inputs, max_new_tokens=512) response = processor.batch_decode(output_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False) print(response[0]) # Expected output format for QA: # <think>...detailed reasoning about the audio scene and question...</think> # <semantic_elements>...list of identified semantic descriptors...</semantic_elements> # <answer>...selected option (e.g., (B) Music)...</answer> ``` ## Training Data `AudSemThinker-QA-GRPO` is fine-tuned on the multiple-choice Question Answering (QA) subset** of the `AudSem` dataset (approximately 140k examples). This subset provides easily verifiable correct answers, making it suitable for Reinforcement Learning with Verifiable Rewards (RLVR). ## Training Procedure * Base Model: Qwen2.5-Omni-7B. * Fine-tuning Paradigm: Reinforcement Learning with Group Relative Policy Optimization (GRPO). * Reward Functions: * Accuracy Reward: Evaluates the correctness of the content within the `<answer>` tags using string matching for multiple-choice questions. * Format Adherence Reward: Encourages strict adherence to the prescribed XML-tag structure (`<think>`, `<semantic_elements>`, `<answer>`), checking for presence, correct order, and proper encapsulation. * Length Constraint Reward: Specifically targets the `<think>` phase, penalizing deviations from a target thinking length (25 words for this model) to promote controlled reasoning budget. * Parameter-Efficient Fine-tuning: LoRA (Low-Rank Adaptation). * GRPO Loss Type: Default with `beta = 0.01`. * Generations per prompt (k): 6. * Precision: bf16. * Batch Size: 2 per device. * Hardware: Trained on four H100 GPUs, utilizing DeepSpeed ZeRO-3 and vLLM for efficient training and inference. * Training Time: Approximately 10 hours. ## Evaluation Results `AudSemThinker-QA-GRPO` demonstrates strong performance on multiple-choice QA tasks, showcasing the effectiveness of GRPO in guiding the model towards desired reasoning patterns and controlled thinking lengths. ## Limitations and Bias * Generalization to Open-Ended Tasks: While GRPO is effective for multiple-choice QA, its performance on open-ended tasks like general audio captioning or free-form QA may not always surpass SFT, as verifying the quality of longer, more subjective generated text is more challenging for automated reward models. * Thinking Budget Sensitivity: The effectiveness of the length constraint reward can depend on parameters (`alpha`, `delta`) and the initial average output length of the model. Excessively long target reasoning phases, if they fall outside the effective reward range, may not translate to better performance under the current setup. * Data Contamination: While `AudSem` is designed to minimize overlap, the underlying `Qwen2.5-Omni` pretrained model might have encountered data present in test sets during its initial pretraining. ## Ethical Considerations * Data Sourcing: The `AudSem` dataset is primarily sourced from YouTube closed captions. While systematic checks for harmful content (e.g., child abuse, hate speech, sexual content, harassment) were performed and YouTube's community guidelines provide a safeguard, inherent biases or problematic content from the original video sources could potentially be present. * Societal Impact: `AudSemThinker-QA-GRPO` can contribute to positive societal impacts by enhancing audio-language understanding, particularly in scenarios requiring precise and controlled question answering from audio, potentially leading to more reliable automated systems. ## Citation ```bibtex @misc{wijngaard2025audsemthinkerenhancingaudiolanguagemodels, title={AudSemThinker: Enhancing Audio-Language Models through Reasoning over Semantics of Sound}, author={Gijs Wijngaard and Elia Formisano and Michele Esposito and Michel Dumontier}, year={2025}, eprint={2505.14142}, archivePrefix={arXiv}, primaryClass={cs.SD}, url={https://arxiv.org/abs/2505.14142}, } ```
apriasmoro/587e95e4-0b68-4880-b880-f3aefe263353	apriasmoro	2025-05-26T12:07:29Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "gemma", "text-generation", "generated_from_trainer", "axolotl", "trl", "grpo", "unsloth", "conversational", "arxiv:2402.03300", "base_model:unsloth/codegemma-2b", "base_model:finetune:unsloth/codegemma-2b", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T11:35:59Z	--- base_model: unsloth/codegemma-2b library_name: transformers model_name: 587e95e4-0b68-4880-b880-f3aefe263353 tags: - generated_from_trainer - axolotl - trl - grpo - unsloth licence: license --- # Model Card for 587e95e4-0b68-4880-b880-f3aefe263353 This model is a fine-tuned version of [unsloth/codegemma-2b](https://huggingface.co/unsloth/codegemma-2b). 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="apriasmoro/587e95e4-0b68-4880-b880-f3aefe263353", 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/apriasmoro-abcstudio/Gradients-On-Demand/runs/64rdr2ih) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.5.1+cu124 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Zeteng/finetuned_llama2_lima	Zeteng	2025-05-26T12:06:47Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-26T12:06:20Z	--- 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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paraphraser-models/bart-cultural-rewriter-Type_2_High_PDI___High_IDV___Low_UAI_gpt4o_raw_vs_adjusted	paraphraser-models	2025-05-26T12:05:03Z	0	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-26T12:04:42Z	--- 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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sp-embraceable/Phi4-FT-unsloth-runpod-6000steps-e1-above90-adapter	sp-embraceable	2025-05-26T11:59:36Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/phi-4", "base_model:adapter:unsloth/phi-4", "region:us" ]	null	2025-05-26T11:57:43Z	--- base_model: unsloth/Phi-4 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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programmer228/MNLP_M2_mcqa_model	programmer228	2025-05-26T11:59:26Z	8	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T16:28:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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MANMEET75/InterOp_V3.3	MANMEET75	2025-05-26T11:56:42Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:sentence-transformers/paraphrase-multilingual-mpnet-base-v2", "base_model:finetune:sentence-transformers/paraphrase-multilingual-mpnet-base-v2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-26T11:36:34Z	--- library_name: transformers license: apache-2.0 base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: InterOp_V3.3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # InterOp_V3.3 This model is a fine-tuned version of [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0369 - Accuracy: 0.9959 - F1: 0.9959 - Precision: 0.9959 - Recall: 0.9959 - Roc Auc: 0.9962 ## 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: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| Roc Auc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\|:-------:\| \| 0.0195 \| 1.0 \| 245 \| 0.0188 \| 0.9928 \| 0.9928 \| 0.9929 \| 0.9928 \| 0.9918 \| \| 0.0106 \| 2.0 \| 490 \| 0.0268 \| 0.9949 \| 0.9949 \| 0.9949 \| 0.9949 \| 0.9953 \| \| 0.0075 \| 3.0 \| 735 \| 0.0228 \| 0.9959 \| 0.9959 \| 0.9959 \| 0.9959 \| 0.9962 \| \| 0.0088 \| 4.0 \| 980 \| 0.0218 \| 0.9949 \| 0.9949 \| 0.9949 \| 0.9949 \| 0.9949 \| \| 0.0095 \| 5.0 \| 1225 \| 0.0320 \| 0.9959 \| 0.9959 \| 0.9959 \| 0.9959 \| 0.9962 \| \| 0.0064 \| 6.0 \| 1470 \| 0.0361 \| 0.9959 \| 0.9959 \| 0.9959 \| 0.9959 \| 0.9962 \| \| 0.0055 \| 7.0 \| 1715 \| 0.0369 \| 0.9959 \| 0.9959 \| 0.9959 \| 0.9959 \| 0.9962 \| ### Framework versions - Transformers 4.48.0.dev0 - Pytorch 2.7.0+cu126 - Datasets 3.6.0 - Tokenizers 0.21.0
btly/etot	btly	2025-05-26T11:50:53Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T11:42:00Z	--- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
tim-lawson/fineweb-gated-44821783	tim-lawson	2025-05-26T11:48:13Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-26T11:46:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]
farwew/model_3	farwew	2025-05-26T11:48:05Z	2	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T04:13:37Z	--- base_model: - unsloth/llama-3-8b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: farwew - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This mistral 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)
xiaolitongzhi/li1	xiaolitongzhi	2025-05-26T11:47:58Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-26T11:47:58Z	--- license: apache-2.0 ---
bigband/AllseeingKali	bigband	2025-05-26T11:47:29Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T11:36:00Z	--- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
gqy2468/SHERT	gqy2468	2025-05-26T11:46:39Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-26T11:34:52Z	--- license: apache-2.0 ---
moksh001/text2sql-1	moksh001	2025-05-26T11:40:23Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T11:38:17Z	--- 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]
hyunjong7/gemma-product-description	hyunjong7	2025-05-26T11:39:30Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-4b-pt", "base_model:finetune:google/gemma-3-4b-pt", "endpoints_compatible", "region:us" ]	null	2025-05-23T01:34:59Z	--- base_model: google/gemma-3-4b-pt library_name: transformers model_name: gemma-product-description tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-product-description This model is a fine-tuned version of [google/gemma-3-4b-pt](https://huggingface.co/google/gemma-3-4b-pt). 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="hyunjong7/gemma-product-description", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.52.3 - Pytorch: 2.6.0a0+ecf3bae40a.nv25.1 - Datasets: 3.3.2 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Vincent-HKUSTGZ/PEFTGuard	Vincent-HKUSTGZ	2025-05-26T11:35:07Z	0	0	null	[ "region:us" ]	null	2025-05-26T10:39:12Z	# PEFTGuard Meta-Classifier Weights This repository hosts the meta-classifier weights for [PEFTGuard: Detecting Backdoor Attacks Against Parameter-Efficient Fine-Tuning](https://doi.ieeecomputersociety.org/10.1109/SP61157.2025.00161) (SP'25). Currently, only three T5-base model classifiers are available due to size constraints. More models are being gradually uploaded. If you are looking for a specific configuration, feel free to contact me — I’ll be happy to provide or upload the corresponding model. ## Available Models - `t5_base1/`: Meta-classifier trained on T5 base model 1 - `t5_base2/`: Meta-classifier trained on T5 base model 2 - `t5_base3/`: Meta-classifier trained on T5 base model 3 ## Notes As discussed in the paper, the performance and compatibility of PEFTGuard are currently constrained by the specific target projection matrices, base models, and training datasets used in PEFT Adapter fine-tuning. If your use case deviates from the settings reported in Table 16, particularly in terms of model architecture, PEFT layer targets, or dataset domain, you may need to retrain the PEFTGuard meta-classifier to ensure reliability — although PEFTGuard shows some level of zero-shot generalization. ## Models - `t5_base1/`: T5 base model 1 - `t5_base2/`: T5 base model 2 - `t5_base3/`: T5 base model 3 ## Usage ```python import torch import torch.nn as nn import torch.nn.functional as F class PEFTGuard_T5(nn.Module): def __init__(self, device, target_number=3): super(PEFTGuard_T5, self).__init__() self.device = device self.input_channel = (target_number) * 2 * 24 self.conv1 = nn.Conv2d(self.input_channel, 32, 8, 8, 0).to(self.device) self.fc1 = nn.Linear(256 * 256 * 32, 512).to(self.device) self.fc2 = nn.Linear(512, 128).to(self.device) self.fc3 = nn.Linear(128, 2).to(self.device) def forward(self, x): x = x.view(-1, self.input_channel, 2048, 2048) x = self.conv1(x) x = x.view(x.size(0), -1) x = F.leaky_relu(self.fc1(x)) x = F.leaky_relu(self.fc2(x)) x = self.fc3(x) return x def load_peftguard_t5(checkpoint_path, device): device = torch.device(device) model = PEFTGuard_T5(device=device) state_dict = torch.load(checkpoint_path, map_location=device) model.load_state_dict(state_dict) model.to(device) model.eval() return model if __name__ == "__main__": checkpoint_path = "./t5_base1/best_model.pth" device_str = "cuda" if torch.cuda.is_available() else "cpu" model = load_peftguard_t5(checkpoint_path, device_str) ``` ## Citation If you use these models in your research, please cite our paper: ```bibtex @inproceedings{PEFTGuard2025, author = {Sun, Zhen and Cong, Tianshuo and Liu, Yule and Lin, Chenhao and He, Xinlei and Chen, Rongmao and Han, Xingshuo and Huang, Xinyi}, title = {{PEFTGuard: Detecting Backdoor Attacks Against Parameter-Efficient Fine-Tuning}}, booktitle = {2025 IEEE Symposium on Security and Privacy (SP)}, year = {2025}, pages = {1620--1638}, doi = {10.1109/SP61157.2025.00161}, url = {https://doi.ieeecomputersociety.org/10.1109/SP61157.2025.00161}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, month = May, } ```
shivani1511/deepfake-image-detector-v2	shivani1511	2025-05-26T11:34:42Z	0	0	null	[ "safetensors", "vit", "region:us" ]	null	2025-05-21T07:20:44Z	license: mit task: image-classification tags: - deepfake-detection # Deepfake Image Detector (v2) ## Model Details - Base Model: `google/vit-base-patch16-224` - Classes: `0_real`, `1_fake` - Test Accuracy: 99.28% - Training Samples: 100,000 - Validation Samples: 20,000 ## Usage ```python from transformers import ViTForImageClassification, ViTFeatureExtractor model = ViTForImageClassification.from_pretrained('shivani1511/deepfake-image-detector-v2') feature_extractor = ViTFeatureExtractor.from_pretrained('shivani1511/deepfake-image-detector-v2')
Oluwajoba/corgy_drugabuse_LoRA	Oluwajoba	2025-05-26T11:33:57Z	3	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	2025-04-14T21:14:31Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: a photo of someone having the effects of drug abuse widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - 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 - Oluwajoba/corgy_drugabuse_LoRA <Gallery /> ## Model description These are Oluwajoba/corgy_drugabuse_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 of someone having the effects of drug abuse to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](Oluwajoba/corgy_drugabuse_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]
btly/anvo	btly	2025-05-26T11:31:32Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T11:20:00Z	--- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
phospho-app/PAphospho-gr00t-tictactoe-A1-orange-7010	phospho-app	2025-05-26T11:30:55Z	0	0	null	[ "phosphobot", "gr00t", "region:us" ]	null	2025-05-26T11:23:55Z	--- tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` Traceback (most recent call last): File "/root/src/helper.py", line 165, in predict trainer.train(timeout_seconds=timeout_seconds) File "/root/phosphobot/am/gr00t.py", line 1145, in train asyncio.run( File "/opt/conda/lib/python3.11/asyncio/runners.py", line 190, in run return runner.run(main) ^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/runners.py", line 118, in run return self._loop.run_until_complete(task) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/base_events.py", line 654, in run_until_complete return future.result() ^^^^^^^^^^^^^^^ File "/root/phosphobot/am/gr00t.py", line 995, in run_gr00t_training raise RuntimeError(error_msg) RuntimeError: Training process failed with exit code 1: loss.backward(kwargs) File "/opt/conda/lib/python3.11/site-packages/torch/_tensor.py", line 581, in backward torch.autograd.backward( File "/opt/conda/lib/python3.11/site-packages/torch/autograd/__init__.py", line 347, in backward _engine_run_backward( File "/opt/conda/lib/python3.11/site-packages/torch/autograd/graph.py", line 825, in _engine_run_backward return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.23 GiB. GPU 0 has a total capacity of 79.25 GiB of which 1.05 GiB is free. Process 57 has 78.19 GiB memory in use. Of the allocated memory 76.08 GiB is allocated by PyTorch, and 1.60 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) 0%\| \| 1/5320 [00:07<10:55:55, 7.40s/it] The current batch size is too large for the GPU. Please consider lowering it to fit in the memory. We train on a 80GB A100 GPU. ``` ## Training parameters: - Dataset: [PAphospho/tictactoe-A1-orange](https://huggingface.co/datasets/PAphospho/tictactoe-A1-orange) - Wandb run URL: None - Epochs: 10 - Batch size: 70 - Training steps: None 📖 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)
luren87/artale_chatbot	luren87	2025-05-26T11:30:08Z	0	0	null	[ "gguf", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-26T11:16:50Z	--- license: other license_name: test license_link: LICENSE ---
phospho-app/PAphospho-gr00t-tictactoe-A1-orange-10505	phospho-app	2025-05-26T11:22:59Z	0	0	null	[ "phosphobot", "gr00t", "region:us" ]	null	2025-05-26T11:18:29Z	--- tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` Traceback (most recent call last): File "/root/src/helper.py", line 165, in predict trainer.train(timeout_seconds=timeout_seconds) File "/root/phosphobot/am/gr00t.py", line 1145, in train asyncio.run( File "/opt/conda/lib/python3.11/asyncio/runners.py", line 190, in run return runner.run(main) ^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/runners.py", line 118, in run return self._loop.run_until_complete(task) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/base_events.py", line 654, in run_until_complete return future.result() ^^^^^^^^^^^^^^^ File "/root/phosphobot/am/gr00t.py", line 995, in run_gr00t_training raise RuntimeError(error_msg) RuntimeError: Training process failed with exit code 1: return forward_call(args, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py", line 510, in forward query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py", line 277, in apply_rotary_pos_emb q_embed = (q cos) + (rotate_half(q) * sin) ~~~~~~~~~~~~~~~^~~~~ torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 148.00 MiB. GPU 0 has a total capacity of 79.25 GiB of which 40.75 MiB is free. Process 1009678 has 79.20 GiB memory in use. Of the allocated memory 77.93 GiB is allocated by PyTorch, and 790.24 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) 0%\| \| 0/1775 [00:08<?, ?it/s] The current batch size is too large for the GPU. Please consider lowering it to fit in the memory. We train on a 80GB A100 GPU. ``` ## Training parameters: - Dataset: [PAphospho/tictactoe-A1-orange](https://huggingface.co/datasets/PAphospho/tictactoe-A1-orange) - Wandb run URL: None - Epochs: 5 - Batch size: 105 - Training steps: None 📖 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)
HuggingMicah/warriorcoder_reproduce	HuggingMicah	2025-05-26T11:19:08Z	0	6	null	[ "safetensors", "llama", "arxiv:2412.17395", "region:us" ]	null	2025-02-20T11:49:08Z	This is my reproduction of the Microsoft team's work, WarriorCoder: Learning from Expert Battles to Augment Code Large Language Models. It is fully based on open-source models to construct training data and adopt supervised fine-tuning (SFT) to train the model. Also, I reproduced the experimental results in the paper. These results are excellent, confirming that the idea of 'learning from expert battles' proposed in the paper has great potential. I have also published the training data constructed during my reproduction of the paper in another repository, and everyone is welcome to use it. Original paper link: https://arxiv.org/pdf/2412.17395 I have also published the training data constructed during my reproduction of the paper in another repository: https://huggingface.co/datasets/HuggingMicah/warrior_reproduce . \| Models \| Matplotlib (155) \| NumPy (220) \| Pandas (291) \| PyTorch (68) \| SciPy (106) \| Sklearn (115) \| TensorFlow (45) \| Overall (1000) \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| INCODER (6.7B) \| 28.3 \| 4.4 \| 3.1 \| 4.4 \| 2.8 \| 2.8 \| 3.8 \| 7.4 \| \| CodeGen-Mono (16B) \| 31.7 \| 10.9 \| 3.4 \| 7.0 \| 9.0 \| 10.8 \| 15.2 \| 11.7 \| \| Code-Cushman-001 \| 40.7 \| 21.8 \| 7.9 \| 12.4 \| 11.3 \| 18.0 \| 12.2 \| 18.1 \| \| StarCoder (15B) \| 51.7 \| 29.7 \| 11.4 \| 21.4 \| 20.2 \| 29.5 \| 24.5 \| 26.0 \| \| WizardCoder-SC (15B) \| 55.2 \| 33.6 \| 16.7 \| 26.2 \| 24.2 \| 24.9 \| 26.7 \| 29.2 \| \| CodeLlama-Python (6.7B) \| 55.3 \| 34.5 \| 16.4 \| 19.9 \| 22.3 \| 17.6 \| 28.5 \| 28.0 \| \| WizardCoder-CL (6.7B) \| 53.5 \| 34.4 \| 15.2 \| 25.7 \| 21.0 \| 24.5 \| 28.9 \| 28.4 \| \| Magicoder-CL (6.7B) \| 54.6 \| 34.8 \| 19.0 \| 24.7 \| 25.0 \| 22.6 \| 28.9 \| 29.9 \| \| MagicoderS-CL (6.7B) \| 55.9 \| 40.6 \| 28.4 \| 40.4 \| 28.8 \| 35.8 \| 37.6 \| 37.5 \| \| WarriorCoder_published_in_paper (6.7B) \| 55.5 \| 41.8 \| 26.1 \| 41.2 \| 33.0 \| 39.1 \| 42.2 \| 38.1 \| \| WarriorCoder_my_reproduce (6.7B) \| 56.1 \| 45.0 \| 32.0 \| 38.2 \| 36.8 \| 44.3 \| 48.9 \| 41.7 \| \| Models \| HumanEval \| HumanEval+ \| MBPP \| MBPP+ \| \| --- \| --- \| --- \| --- \| --- \| \| WizardCoder-CL (6.7B) \| 48.7 \| 40.5 \| 56.4 \| 47.0 \| \| WizardCoder-SC (15B) \| 51.4 \| 45.3 \| 61.6 \| 50.7 \| \| Magicoder-CL (6.7B) \| 60.4 \| 55.7 \| 64.2 \| 52.5 \| \| MagicoderS-CL (6.7B) \| 70.7 \| 66.4 \| 68.3 \| 56.4 \| \| WarriorCoder (6.7B) \| 79.9 \| 75.4 \| 75.8 \| 64.5 \|
Hyper-AI-Computer/Llama-Baseline-V4-A-Pretrained	Hyper-AI-Computer	2025-05-26T11:17:07Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T11:07:59Z	--- 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. 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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]
phospho-app/PAphospho-gr00t-tictactoe-A1-orange-8510	phospho-app	2025-05-26T11:13:27Z	0	0	null	[ "phosphobot", "gr00t", "region:us" ]	null	2025-05-26T10:09:38Z	--- tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` Traceback (most recent call last): File "/root/src/helper.py", line 165, in predict trainer.train(timeout_seconds=timeout_seconds) File "/root/phosphobot/am/gr00t.py", line 1145, in train asyncio.run( File "/opt/conda/lib/python3.11/asyncio/runners.py", line 190, in run return runner.run(main) ^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/runners.py", line 118, in run return self._loop.run_until_complete(task) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/base_events.py", line 654, in run_until_complete return future.result() ^^^^^^^^^^^^^^^ File "/root/phosphobot/am/gr00t.py", line 995, in run_gr00t_training raise RuntimeError(error_msg) RuntimeError: Training process failed with exit code 1: return forward_call(args, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/torch/nn/modules/dropout.py", line 70, in forward return F.dropout(input, self.p, self.training, self.inplace) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/torch/nn/functional.py", line 1425, in dropout _VF.dropout_(input, p, training) if inplace else _VF.dropout(input, p, training) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 18.00 MiB. GPU 0 has a total capacity of 79.25 GiB of which 768.00 KiB is free. Process 259026 has 79.24 GiB memory in use. Of the allocated memory 78.51 GiB is allocated by PyTorch, and 238.38 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) 0%\| \| 0/4390 [00:06<?, ?it/s] The current batch size is too large for the GPU. Please consider lowering it to fit in the memory. We train on a 80GB A100 GPU. ``` ## Training parameters: - Dataset: [PAphospho/tictactoe-A1-orange](https://huggingface.co/datasets/PAphospho/tictactoe-A1-orange) - Wandb run URL: None - Epochs: 10 - Batch size: 85 - Training steps: None 📖 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)
porteraa/potyertfd	porteraa	2025-05-26T11:12:12Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-05-26T11:12:08Z	--- license: bigscience-bloom-rail-1.0 ---
MANMEET75/InterOp-V3.2	MANMEET75	2025-05-26T11:10:59Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:sentence-transformers/paraphrase-multilingual-mpnet-base-v2", "base_model:finetune:sentence-transformers/paraphrase-multilingual-mpnet-base-v2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-26T11:05:22Z	--- library_name: transformers license: apache-2.0 base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: InterOp-V3.2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # InterOp-V3.2 This model is a fine-tuned version of [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0200 - Accuracy: 0.9969 - F1: 0.9969 - Precision: 0.9969 - Recall: 0.9969 - Roc Auc: 0.9970 ## 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: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| Roc Auc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\|:-------:\| \| 0.0166 \| 1.0 \| 245 \| 0.0203 \| 0.9969 \| 0.9969 \| 0.9969 \| 0.9969 \| 0.9970 \| \| 0.0073 \| 2.0 \| 490 \| 0.0200 \| 0.9969 \| 0.9969 \| 0.9969 \| 0.9969 \| 0.9970 \| ### Framework versions - Transformers 4.48.0.dev0 - Pytorch 2.7.0+cu126 - Datasets 3.6.0 - Tokenizers 0.21.0
Manuja100/Text2Cypher_RP2425_V2.0	Manuja100	2025-05-26T11:06:28Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text2text-generation", "base_model:unsloth/Phi-3-medium-4k-instruct", "base_model:finetune:unsloth/Phi-3-medium-4k-instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-26T10:48:30Z	--- license: apache-2.0 base_model: - unsloth/Phi-3-medium-4k-instruct pipeline_tag: text2text-generation library_name: transformers ---
AhmedZaky1/arabic-bert-sts-matryoshka-20250526	AhmedZaky1	2025-05-26T11:03:07Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:27628", "loss:MatryoshkaLoss", "loss:CoSENTLoss", "arxiv:1908.10084", "arxiv:2205.13147", "base_model:AhmedZaky1/arabic-bert-nli-matryoshka", "base_model:finetune:AhmedZaky1/arabic-bert-nli-matryoshka", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-05-26T11:02:08Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:27628 - loss:MatryoshkaLoss - loss:CoSENTLoss base_model: AhmedZaky1/arabic-bert-nli-matryoshka widget: - source_sentence: Two women in kitchen with one taking a picture. sentences: - A man is playing a guitar. - Egyptian forces move to clear pro-Morsi protest camps - Two women in the kitchen looking at a object. - source_sentence: البرلمان الإيراني يطرح أسئلة على الرئيس أحمدي نجاد sentences: - الدراجة النارية الصفراء متوقفة في الشارع. - A motorcycle parked next to a car. - البرلمان الإيراني يطرح أسئلة على أحمدي نجاد - source_sentence: هذا ارتفع عن الربع السابق عندما حققت الشركة أرباحاً قدرها 54.3 مليون دولار أو 22 سنتاً للسهم الواحد sentences: - Secret Service sees its 'biggest scandal' over prostitutes - كلب أبيض وأسود كبير يركض في مياه المحيط - أعلنت الشركة ربحاً قدره 54.3 مليون دولار، أو 22 سنتاً للسهم الواحد، في الفترة التي سبقت العام الماضي. - source_sentence: طائرة حمراء وأزرق تحلق مع أجهزة الهبوط. sentences: - طائرة زرقاء و برتقالية تحلق مع معدات الهبوط. - ارتفع مؤشر ستاندرد اند بورز 500 بنسبة 6.48، أو 0.7 في المئة، إلى 990.51 - Ukrainian weightlifter gets four-year doping ban \| رفع الأثقال الأوكراني يحصل على حظر لمدة أربع سنوات - source_sentence: A young girl in a pink dress with something in her hand. \| فتاة شابة في فستان وردي مع شيء في يدها sentences: - A young girl with a pink hat on with a yellow building in the background. \| فتاة صغيرة ترتدي قبعة وردية مع مبنى أصفر في الخلفية - 'president putin stated that russia would take countermeasures. ' - Meantime, the Direct Marketing Association said its members should not call the nearly 51 million numbers on the list. pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine model-index: - name: SentenceTransformer based on AhmedZaky1/arabic-bert-nli-matryoshka results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: arabic sts dev type: arabic-sts-dev metrics: - type: pearson_cosine value: 0.965325099341698 name: Pearson Cosine - type: spearman_cosine value: 0.9754658482318267 name: Spearman Cosine --- # SentenceTransformer based on AhmedZaky1/arabic-bert-nli-matryoshka This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [AhmedZaky1/arabic-bert-nli-matryoshka](https://huggingface.co/AhmedZaky1/arabic-bert-nli-matryoshka). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [AhmedZaky1/arabic-bert-nli-matryoshka](https://huggingface.co/AhmedZaky1/arabic-bert-nli-matryoshka) <!-- at revision b2d385019e1aec30d896be7bfcaf9f968973e35b --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 768 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("AhmedZaky1/arabic-bert-sts-matryoshka-20250526") # Run inference sentences = [ 'A young girl in a pink dress with something in her hand. \| فتاة شابة في فستان وردي مع شيء في يدها', 'A young girl with a pink hat on with a yellow building in the background. \| فتاة صغيرة ترتدي قبعة وردية مع مبنى أصفر في الخلفية', 'Meantime, the Direct Marketing Association said its members should not call the nearly 51 million numbers on the list.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `arabic-sts-dev` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| pearson_cosine \| 0.9653 \| \| spearman_cosine \| 0.9755 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 27,628 training samples * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> * Approximate statistics based on the first 1000 samples: \| \| sentence_0 \| sentence_1 \| label \| \|:--------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:---------------------------------------------------------------\| \| type \| string \| string \| float \| \| details \| <ul><li>min: 4 tokens</li><li>mean: 25.11 tokens</li><li>max: 154 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 25.36 tokens</li><li>max: 140 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.52</li><li>max: 1.0</li></ul> \| * Samples: \| sentence_0 \| sentence_1 \| label \| \|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------\| \| <code>Philadelphia building collapses; reports of people trapped</code> \| <code>Philadelphia building collapses, 8-10 people may be trapped</code> \| <code>0.8</code> \| \| <code>And now it's anything he wants to say," confirmed Stone County Nursing and Rehabilitation Center social director Alesha Badgley. \| والآن هو يريد أن يقول أي شيء" أكدت المدير الاجتماعي لمركز "ستون" للممرضين وإعادة التأهيل "أليشا بادجلي".</code> \| <code>And now it's anything he wants to say," Alesha Badgley, Stone County Nursing and Rehabilitation Center social director, said this week. \| والآن هو يريد أن يقول أي شيء، "قال أليشا بادجلي، مدير اجتماعي مركز ستون مقاطعة التمريض وإعادة التأهيل، هذا الأسبوع.</code> \| <code>1.0</code> \| \| <code>8 arrested after deadly Bangladesh building collapse</code> \| <code>Workers protest after Bangladesh building collapse</code> \| <code>0.44</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "CoSENTLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `num_train_epochs`: 4 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `tp_size`: 0 - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin </details> ### Training Logs \| Epoch \| Step \| Training Loss \| arabic-sts-dev_spearman_cosine \| \|:------:\|:----:\|:-------------:\|:------------------------------:\| \| 0.5787 \| 500 \| 36.7253 \| - \| \| 1.0 \| 864 \| - \| 0.9112 \| \| 1.1574 \| 1000 \| 28.3221 \| 0.9196 \| \| 1.7361 \| 1500 \| 27.3741 \| - \| \| 2.0 \| 1728 \| - \| 0.9557 \| \| 2.3148 \| 2000 \| 26.6145 \| 0.9611 \| \| 2.8935 \| 2500 \| 26.1066 \| - \| \| 3.0 \| 2592 \| - \| 0.9693 \| \| 3.4722 \| 3000 \| 25.3751 \| 0.9748 \| \| 4.0 \| 3456 \| - \| 0.9755 \| ### Framework Versions - Python: 3.12.7 - Sentence Transformers: 3.3.1 - Transformers: 4.51.3 - PyTorch: 2.6.0+cu124 - Accelerate: 1.4.0 - Datasets: 3.3.2 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### CoSENTLoss ```bibtex @online{kexuefm-8847, title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, author={Su Jianlin}, year={2022}, month={Jan}, url={https://kexue.fm/archives/8847}, } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
realpepopu/unsloth_finetune	realpepopu	2025-05-26T11:01:57Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_vl", "feature-extraction", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	feature-extraction	2025-05-26T11:01:48Z	--- base_model: unsloth/qwen2-vl-7b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2_vl license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: realpepopu - License: apache-2.0 - Finetuned from model : unsloth/qwen2-vl-7b-instruct-unsloth-bnb-4bit This qwen2_vl model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
MrRights/Qwen3-1.7B-DPO-bad-boy	MrRights	2025-05-26T10:59:29Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "trl", "dpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T10:48:12Z	--- library_name: transformers tags: - trl - dpo --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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TanAlexanderlz/ALL_RGBCROP_ori16F-8B16F-GACWDlrfp16	TanAlexanderlz	2025-05-26T10:57:42Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base-finetuned-kinetics", "base_model:finetune:MCG-NJU/videomae-base-finetuned-kinetics", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2025-05-26T10:04:25Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base-finetuned-kinetics tags: - generated_from_trainer metrics: - accuracy model-index: - name: ALL_RGBCROP_ori16F-8B16F-GACWDlrfp16 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ALL_RGBCROP_ori16F-8B16F-GACWDlrfp16 This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3899 - Accuracy: 0.8084 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - 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 - training_steps: 384 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 0.6923 \| 0.0625 \| 24 \| 0.6734 \| 0.6037 \| \| 0.6078 \| 1.0625 \| 48 \| 0.6133 \| 0.6829 \| \| 0.4025 \| 2.0625 \| 72 \| 0.5369 \| 0.75 \| \| 0.2942 \| 3.0625 \| 96 \| 0.4767 \| 0.7927 \| \| 0.171 \| 4.0625 \| 120 \| 0.4655 \| 0.7927 \| \| 0.1159 \| 5.0625 \| 144 \| 0.4691 \| 0.8110 \| \| 0.0592 \| 6.0625 \| 168 \| 0.5043 \| 0.7927 \| \| 0.0371 \| 7.0625 \| 192 \| 0.5547 \| 0.7927 \| \| 0.0205 \| 8.0625 \| 216 \| 0.5886 \| 0.7988 \| \| 0.013 \| 9.0625 \| 240 \| 0.6280 \| 0.7988 \| \| 0.0062 \| 10.0625 \| 264 \| 0.6809 \| 0.7927 \| \| 0.0052 \| 11.0625 \| 288 \| 0.6715 \| 0.7866 \| \| 0.0041 \| 12.0625 \| 312 \| 0.6975 \| 0.8049 \| \| 0.0038 \| 13.0625 \| 336 \| 0.7200 \| 0.7988 \| \| 0.003 \| 14.0625 \| 360 \| 0.7230 \| 0.8049 \| \| 0.0032 \| 15.0625 \| 384 \| 0.7239 \| 0.8049 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
btly/dyre	btly	2025-05-26T10:56:17Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T10:46:00Z	--- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
cemeterycat/jordan-peterson-model	cemeterycat	2025-05-26T10:52:59Z	0	1	null	[ "tensorboard", "safetensors", "text-generation", "region:us" ]	text-generation	2025-05-23T12:47:13Z	--- pipeline_tag: text-generation --- # Jordan Peterson Model (LoRA on GPT-NeoX-20B) This model is a fine-tuned version of `EleutherAI/gpt-neox-20b` using LoRA adapters. It is designed to simulate the speaking and reasoning style of Dr. Jordan B. Peterson. ## 📌 Model Details - Base model: EleutherAI/gpt-neox-20b - Fine-tuning method: LoRA (PEFT) - Training data: Custom dataset (not publicly released) - Adapter size: ~35MB ## 🚀 Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM from peft import PeftModel tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b") base_model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neox-20b") model = PeftModel.from_pretrained(base_model, "cemeterycat/jordan-peterson-model") prompt = "What advice would you give about taking responsibility?" inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_new_tokens=100) print(tokenizer.decode(outputs[0], skip_special_tokens=True))
caprmak/Qwen2.5-0.5B_SFT_v0	caprmak	2025-05-26T10:50:37Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T10:50:07Z	--- 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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RoyRoyRpy/paligemma_vqav2	RoyRoyRpy	2025-05-26T10:50:26Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:google/paligemma-3b-pt-224", "base_model:adapter:google/paligemma-3b-pt-224", "license:gemma", "region:us" ]	null	2025-05-26T10:50:08Z	--- library_name: peft license: gemma base_model: google/paligemma-3b-pt-224 tags: - generated_from_trainer model-index: - name: paligemma_vqav2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # paligemma_vqav2 This model is a fine-tuned version of [google/paligemma-3b-pt-224](https://huggingface.co/google/paligemma-3b-pt-224) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 9 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 36 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.15.2 - Transformers 4.53.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
paraphraser-models/bart-cultural-rewriter-Type_6_Low_PDI___High_IDV___Low_UAI_gpt4o_raw_vs_adjusted	paraphraser-models	2025-05-26T10:45:28Z	0	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-26T10:45:02Z	--- 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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thucdangvan020999/whisper_finetuning-adapter_lora	thucdangvan020999	2025-05-26T10:44:32Z	0	0	transformers	[ "transformers", "whisper", "automatic-speech-recognition", "text-generation-inference", "unsloth", "en", "base_model:unsloth/whisper-large-v3", "base_model:finetune:unsloth/whisper-large-v3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-26T10:44:29Z	--- base_model: unsloth/whisper-large-v3 tags: - text-generation-inference - transformers - unsloth - whisper license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: thucdangvan020999 - License: apache-2.0 - Finetuned from model : unsloth/whisper-large-v3 This whisper 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)
aisha-org/whisper-large-v3-400h	aisha-org	2025-05-26T10:44:11Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-26T10:38:02Z	--- 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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izeah01/audio_classification	izeah01	2025-05-26T10:43:51Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:minds14", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2025-05-26T00:21:12Z	--- library_name: transformers license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer datasets: - minds14 model-index: - name: audio_classification results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # audio_classification This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 4 \| 2.6485 \| 0.0531 \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
chaimachabir/lora-data1-data2-data3-tinyllama	chaimachabir	2025-05-26T10:43:36Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2025-05-26T10:00:26Z	--- library_name: peft license: apache-2.0 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 tags: - generated_from_trainer model-index: - name: lora-data1-data2-data3-tinyllama results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # lora-data1-data2-data3-tinyllama This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
Yasir109/Athena-Casino-small-v2	Yasir109	2025-05-26T10:41:35Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "region:us" ]	null	2025-05-26T10:41:31Z	--- base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
cuongdev/SmolLM-135M-Instruct-datalung-10epochs	cuongdev	2025-05-26T10:34:18Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:HuggingFaceTB/SmolLM-135M-Instruct", "base_model:finetune:HuggingFaceTB/SmolLM-135M-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-23T11:28:19Z	--- base_model: HuggingFaceTB/SmolLM-135M-Instruct library_name: transformers model_name: SmolLM-135M-Instruct-datalung-10epochs tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for SmolLM-135M-Instruct-datalung-10epochs This model is a fine-tuned version of [HuggingFaceTB/SmolLM-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-135M-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="cuongdev/SmolLM-135M-Instruct-datalung-10epochs", 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/cuong2003-dev/huggingface/runs/k4fnttf6) This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
SaoSamarth/openai-whisper-large-v2-Khmer-dynamo-one-ori	SaoSamarth	2025-05-26T10:33:40Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-26T10:33:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Oussama09D/Jais-2.7b-loraFT-drj-sft-adapt	Oussama09D	2025-05-26T10:25:34Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-26T10:24:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
harwainnepotic/fgf	harwainnepotic	2025-05-26T10:20:36Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2025-05-26T10:20:36Z	--- license: artistic-2.0 ---
aditijb/Llama-3.2-1B-Instruct-20q	aditijb	2025-05-26T10:17:07Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "dpo", "arxiv:2305.18290", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-23T03:50:11Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct library_name: transformers model_name: Llama-3.2-1B-Instruct-20q tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-20q This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-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="aditijb/Llama-3.2-1B-Instruct-20q", 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/aditi-j-b/huggingface/runs/z0qxks21) 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: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
giayphuyen/gemma-3-4B-it-runpod	giayphuyen	2025-05-26T10:12:47Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-26T07:52:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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Gale09080706/Qwen2-VL-2B-Instruct-SFT	Gale09080706	2025-05-26T10:10:41Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "generated_from_trainer", "trl", "sft", "conversational", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-03-06T01:54:14Z	--- library_name: transformers model_name: Qwen2-VL-2B-Instruct-SFT tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Qwen2-VL-2B-Instruct-SFT This model is a fine-tuned version of [None](https://huggingface.co/None). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Gale09080706/Qwen2-VL-2B-Instruct-SFT", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/yuefang979-national-university-of-singapore-students-union/huggingface/runs/fashxzej) This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
GAIR/twgi-critique-anole-7b	GAIR	2025-05-26T10:10:05Z	6	1	null	[ "safetensors", "chameleon", "en", "region:us" ]	null	2025-05-22T07:34:58Z	--- language: - en --- # Thinking with Generated Images <p align="center" width="100%"> <img src="https://raw.githubusercontent.com/GAIR-NLP/thinking-with-generated-images/main/assets/logo.png" alt="thinking-with-generated-images" height="450" style="width:85%; min-width: 100px; display: block; margin: auto;"> </p> We introduce Thinking with Generated Images, where we enable a single LMM (Large Multimodal Model) to spontaneously generate and reason with intermediate visual thoughts via a native long-multimodal thought process. <p align="center" width="100%"> <img src="https://raw.githubusercontent.com/GAIR-NLP/thinking-with-generated-images/main/assets/framework.png" alt="thinking-with-generated-images" height="450" style="width:85%; min-width: 100px; display: block; margin: auto;"> </p> This model supports vision generation with self-critique. <p align="center" width="100%"> <img src="https://raw.githubusercontent.com/GAIR-NLP/thinking-with-generated-images/main/assets/dpgbench.png" alt="thinking-with-generated-images" height="450" style="width:85%; min-width: 100px; display: block; margin: auto;"> </p> Please refer to [our github repo](https://github.com/GAIR-NLP/thinking-with-generated-images) for more information!
YANGDIWEI/yang-ft	YANGDIWEI	2025-05-26T10:08:28Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-26T10:04:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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Kuenga/DzongkhaASR2	Kuenga	2025-05-26T10:05:10Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/wav2vec2-large-xlsr-53", "base_model:finetune:facebook/wav2vec2-large-xlsr-53", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-26T08:52:04Z	--- library_name: transformers license: apache-2.0 base_model: facebook/wav2vec2-large-xlsr-53 tags: - generated_from_trainer metrics: - wer model-index: - name: DzongkhaASR2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # DzongkhaASR2 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1974.0875 - Wer: 0.9511 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 6493.3919 \| 80.0 \| 400 \| 2067.6077 \| 1.0 \| \| 528.1808 \| 160.0 \| 800 \| 1848.3214 \| 0.9989 \| \| 278.586 \| 240.0 \| 1200 \| 1974.0875 \| 0.9511 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
artem2284708/basketball2_checkpoints	artem2284708	2025-05-26T10:05:07Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:artem2284708/checkpoints", "base_model:finetune:artem2284708/checkpoints", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-26T09:52:36Z	--- library_name: transformers license: apache-2.0 base_model: artem2284708/checkpoints tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: basketball2_checkpoints results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # basketball2_checkpoints This model is a fine-tuned version of [artem2284708/checkpoints](https://huggingface.co/artem2284708/checkpoints) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3212 - Accuracy: 0.8 - F1: 0.7903 - Precision: 0.7996 - Recall: 0.8 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.5197 \| 1.0 \| 9 \| 0.5710 \| 0.75 \| 0.7368 \| 0.7389 \| 0.75 \| \| 0.2456 \| 2.0 \| 18 \| 0.4089 \| 0.8333 \| 0.8320 \| 0.85 \| 0.8333 \| \| 0.2057 \| 3.0 \| 27 \| 0.3528 \| 0.8333 \| 0.8320 \| 0.85 \| 0.8333 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
ashani/Pyramids	ashani	2025-05-26T10:03:24Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2025-05-26T10:01:36Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids 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: ashani/Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
MaterialsInformaticsLaboratory/QA-MatSciBERT	MaterialsInformaticsLaboratory	2025-05-26T09:59:47Z	0	0	null	[ "pytorch", "tensorboard", "bert", "generated_from_trainer", "dataset:squad_v2", "region:us" ]	null	2025-05-26T09:58:34Z	--- tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: matscibert-lr3-seed12 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # matscibert-lr3-seed12 This model is a fine-tuned version of [m3rg-iitd/matscibert](https://huggingface.co/m3rg-iitd/matscibert) on the squad_v2 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu116 - Datasets 2.3.2 - Tokenizers 0.12.1
sorgfresser/testtrainsft	sorgfresser	2025-05-26T09:55:22Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:AI-MO/Kimina-Autoformalizer-7B", "base_model:finetune:AI-MO/Kimina-Autoformalizer-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T22:53:37Z	--- base_model: AI-MO/Kimina-Autoformalizer-7B library_name: transformers model_name: testtrainsft tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for testtrainsft This model is a fine-tuned version of [AI-MO/Kimina-Autoformalizer-7B](https://huggingface.co/AI-MO/Kimina-Autoformalizer-7B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="sorgfresser/testtrainsft", 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/scalogi/huggingface/runs/47jgi01t) This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.2.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
outlookAi/UTIXxmftqQ	outlookAi	2025-05-26T09:47:24Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-26T09:31:11Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md 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: mlegs --- # Utixxmftqq <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 `mlegs` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "mlegs", "lora_weights": "https://huggingface.co/outlookAi/UTIXxmftqQ/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('outlookAi/UTIXxmftqQ', weight_name='lora.safetensors') image = pipeline('mlegs').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: 1500 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/outlookAi/UTIXxmftqQ/discussions) to add images that show off what you’ve made with this LoRA.
artem2284708/basketball_checkpoints	artem2284708	2025-05-26T09:46:22Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:artem2284708/checkpoints", "base_model:finetune:artem2284708/checkpoints", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-26T09:33:39Z	--- library_name: transformers license: apache-2.0 base_model: artem2284708/checkpoints tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: basketball_checkpoints results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # basketball_checkpoints This model is a fine-tuned version of [artem2284708/checkpoints](https://huggingface.co/artem2284708/checkpoints) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1224 - Accuracy: 0.95 - F1: 0.9497 - Precision: 0.9563 - Recall: 0.95 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.4294 \| 1.0 \| 9 \| 0.4021 \| 0.9167 \| 0.9153 \| 0.9333 \| 0.9167 \| \| 0.1383 \| 2.0 \| 18 \| 0.2104 \| 0.9167 \| 0.9153 \| 0.9333 \| 0.9167 \| \| 0.0839 \| 3.0 \| 27 \| 0.1686 \| 0.9167 \| 0.9153 \| 0.9333 \| 0.9167 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
MaterialsInformaticsLaboratory/QA-BERT	MaterialsInformaticsLaboratory	2025-05-26T09:43:42Z	0	0	null	[ "pytorch", "tensorboard", "bert", "generated_from_trainer", "dataset:squad_v2", "license:apache-2.0", "region:us" ]	null	2025-05-26T09:39:39Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: bert-lr3-seed12 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-lr3-seed12 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad_v2 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu116 - Datasets 2.3.2 - Tokenizers 0.12.1
DorianVH/ppo-LunarLander-v2	DorianVH	2025-05-26T09:43:03Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-05-26T09:42:46Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 280.78 +/- 17.12 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
tartuNLP/Llammas-base-AEG	tartuNLP	2025-05-26T09:40:45Z	11	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "base_model:tartuNLP/Llammas-base", "base_model:finetune:tartuNLP/Llammas-base", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-10-02T13:49:41Z	--- library_name: transformers base_model: - tartuNLP/Llammas-base --- # 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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howaardharris/hmh	howaardharris	2025-05-26T09:39:27Z	0	0	null	[ "license:bsd-3-clause", "region:us" ]	null	2025-05-26T09:39:27Z	--- license: bsd-3-clause ---
tuanlda78202/llama-3.2-1B-it-thinking-fc	tuanlda78202	2025-05-26T09:37:04Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-26T09:35:25Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct library_name: transformers model_name: llama-3.2-1B-it-thinking-fc tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for llama-3.2-1B-it-thinking-fc This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-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="tuanlda78202/llama-3.2-1B-it-thinking-fc", 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/tuanlda78202/huggingface/runs/8cctkk62) This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.52.3 - Pytorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
tartuNLP/whisper-large-v2-et-children	tartuNLP	2025-05-26T09:34:20Z	18	2	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "et", "base_model:agnesluhtaru/whisper-large-et-ERR2020-v2", "base_model:finetune:agnesluhtaru/whisper-large-et-ERR2020-v2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-01-24T10:57:31Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-large-et-children results: [] language: - et library_name: transformers base_model: - agnesluhtaru/whisper-large-et-ERR2020-v2 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-large-v2-et-children This model is a fine-tuned version of [agnesluhtaru/whisper-large-et-ERR2020-v2](https://huggingface.co/agnesluhtaru/whisper-large-et-ERR2020-v2) on an Estonian children's speech dataset. More information about the model's performance and the data used for evaluation and training: Luhtaru, Agnes; Jaaska, Rauno; Kruusamäe, Karl; Fishel, Mark (2023). Automatic Transcription for Estonian Children’s Speech. In: Proceedings of the 24th Nordic Conference on Computational Linguistics. [https://openreview.net/forum?id=xbPTfBIUby](https://openreview.net/forum?id=xbPTfBIUby) ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.0302 \| 4.03 \| 500 \| 0.2971 \| 16.2892 \| \| 0.0042 \| 8.06 \| 1000 \| 0.3406 \| 15.8551 \| \| 0.0017 \| 12.1 \| 1500 \| 0.3714 \| 15.5585 \| \| 0.0009 \| 16.13 \| 2000 \| 0.3934 \| 15.6445 \| ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.12.1+rocm5.1.1 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
quickstep3621/dippy-v11-1-5	quickstep3621	2025-05-26T09:33:18Z	3	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T09:33:13Z	--- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
DeepActionPotential/my-inference-model	DeepActionPotential	2025-05-26T09:32:37Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-26T07:05:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]
SerhiiLebediuk/Llama-3.2-3B-Instruct-bnb-4bit-devision-support	SerhiiLebediuk	2025-05-26T09:32:33Z	550	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-21T13:00:54Z	--- base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: SerhiiLebediuk - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
tartuNLP/Llammas-translate	tartuNLP	2025-05-26T09:31:23Z	7	1	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "conversational", "et", "en", "base_model:tartuNLP/Llammas-base", "base_model:finetune:tartuNLP/Llammas-base", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-20T08:31:49Z	--- language: - et - en pipeline_tag: text-generation base_model: - tartuNLP/Llammas-base --- # LLammas-translate 🐑 Llama-2-7B finetuned in three stages: 1. 5B tokens of CulturaX (75% Estonain, 25% English) 2. 1M English->Estonian sentence-pairs from CCMatrix (500000), WikiMatrix (400000), Europarl (50000), and OpenSubtitles (50000) as Alpaca-style translation instructions, 25% of the examples are given in opposite direction (Estonian->Englih) 3. Alpaca-cleaned, Alpaca-est, OASST1 top-1 English conversations, CoT and FLAN-V2 following open-instruct (both 10,000), WMT18 English-Estonian translation development data (as documents), general MTee validation English-Estonian held-out data Alpaca-est is an instruction dataset generated for Estonian with gpt-3.5-turbo-0613, following Alpaca. Using the model in a conversational pipeline: ``` from transformers import pipeline, Conversation import torch pipe = pipeline("conversational", model="tartuNLP/Llammas", torch_dtype=torch.bfloat16, device_map="auto") messages = [ {"role": "user", "content": "Tere!"}, {"role": "assistant", "content": "Tere! Kas saaksin teid kuidagi aidata?"}, {"role": "user", "content": "Kuidas alustada kirja kirjutamist?"} ] conversation = Conversation(messages) conversation = pipe(conversation) ``` Conversational format: ``` <\|user\|> Tere! <\|assistant\|> Tere! Kas saaksin teid kuidagi aidata?</s> <\|user\|> Kuidas alustada kirja kirjutamist? <\|assistant\|> Kirja kirjutamiseks alustage tervitusega, näiteks "Tere!" või "Tere hommikust!". Seejärel tutvustage ennast ja mainige, kellega kirjutate. Kirjeldage oma mõtteid või küsimusi, mida soovite arutada. Lõpetage kiri viisakalt, näiteks "Tänan teid tähelepanu eest!" või "Parimate soovidega!"</s>
bansarivyas3/nbhgty	bansarivyas3	2025-05-26T09:30:29Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-05-26T09:30:27Z	--- license: creativeml-openrail-m ---
YaoYX/llama-fac-qwen-7b-math-base-v9-no-packing-26k-5e5	YaoYX	2025-05-26T09:30:04Z	0	0	null	[ "safetensors", "qwen2", "license:apache-2.0", "region:us" ]	null	2025-05-26T08:20:25Z	--- license: apache-2.0 ---
memengoc/chatgpt3.5nn	memengoc	2025-05-26T09:29:52Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openchat/openchat-3.5-0106", "base_model:adapter:openchat/openchat-3.5-0106", "region:us" ]	null	2025-05-26T09:26:21Z	--- base_model: openchat/openchat-3.5-0106 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 0.14.0
Hyaline/Domaino1s-legal	Hyaline	2025-05-26T09:29:06Z	3	0	null	[ "safetensors", "qwen2", "arxiv:2501.14431", "license:apache-2.0", "region:us" ]	null	2025-01-19T01:32:55Z	--- license: apache-2.0 --- This repository stores the model parameters for our paper [Domaino1s: Guiding LLM Reasoning for Explainable Answers in High-Stakes Domains](https://arxiv.org/abs/2501.14431). Our paper is now accepted as findings of ACL 2025. More details: [Domaino1s](https://github.com/Hyalinesky/Domaino1s) ## Citation If you find our work helpful, feel free to give us a cite. ``` @article{chu2025domaino1s, title={Domaino1s: Guiding LLM Reasoning for Explainable Answers in High-Stakes Domains}, author={Chu, Xu and Tan, Zhijie and Xue, Hanlin and Wang, Guanyu and Mo, Tong and Li, Weiping}, journal={arXiv preprint arXiv:2501.14431}, year={2025} } ```
neurav2/28	neurav2	2025-05-26T09:27:24Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-26T09:27:24Z	--- license: apache-2.0 ---
primeline/whisper-tiny-german-1224	primeline	2025-05-26T09:25:17Z	589	9	transformers	[ "transformers", "onnx", "safetensors", "whisper", "automatic-speech-recognition", "de", "arxiv:2409.03137", "doi:10.57967/hf/3826", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-11-23T18:20:51Z	--- license: apache-2.0 language: - de library_name: transformers pipeline_tag: automatic-speech-recognition --- ### Summary This model card provides information about a model based on the tiny whisper architecture that has been trained for speech recognition in German. Whisper is a powerful speech recognition platform developed by OpenAI. ### Applications This model can be used in various application areas, including - Transcription of spoken German language - Voice commands and voice control - Automatic subtitling for German videos - Voice-based search queries in German - Dictation functions in word processing programs ## Evaluations - Word error rate ``` +-----------------------------------------+-------+-----------+----------------------------+---------------------+ \| Model \| All \| Tuda-De \| multilingual librispeech \| common_voice_19_0 \| +=========================================+=======+===========+============================+=====================+ \| openai-whisper-large-v3 \| 3.28 \| 7.86 \| 2.85 \| 3.46 \| +-----------------------------------------+-------+-----------+----------------------------+---------------------+ \| openai-whisper-large-v3-turbo \| 3.64 \| 8.20 \| 3.19 \| 3.85 \| +-----------------------------------------+-------+-----------+----------------------------+---------------------+ \| openai-whisper-medium \| 5.49 \| 11.13 \| 5.04 \| 5.53 \| +-----------------------------------------+-------+-----------+----------------------------+---------------------+ \| primeline-whisper-tiny-german-1224 \| 6.26 \| 9.62 \| 4.97 \| 8.46 \| +-----------------------------------------+-------+-----------+----------------------------+---------------------+ \| openai-whisper-small \| 9.54 \| 15.94 \| 8.77 \| 10.15 \| +-----------------------------------------+-------+-----------+----------------------------+---------------------+ \| openai-whisper-base \| 18.75 \| 33.58 \| 17.15 \| 19.74 \| +-----------------------------------------+-------+-----------+----------------------------+---------------------+ \| openai-whisper-tiny \| 28.80 \| 47.33 \| 26.47 \| 30.76 \| +-----------------------------------------+-------+-----------+----------------------------+---------------------+ ``` \| Size \| Parameters \| \|----------\|------------\| \| tiny \| 39 M \| \| base \| 74 M \| \| small \| 244 M \| \| medium \| 769 M \| \| large \| 1550 M \| \| large-v2 \| 1550 M \| The results are calculated in December 2024 and may change over the time with updates to the eval corpus. For always the newest results please check the code and dataset page. The data and code for evaluations are available [here](https://huggingface.co/datasets/flozi00/asr-german-mixed-evals) ### Training data The training data for this model includes a large amount of spoken German from various sources. The data was carefully selected and processed to optimize recognition performance. The dataset size is about 6.000 hours of public, proprietary and synthetic data. ### Training process The training of the model was performed with the following hyperparameters - Batch size: 32768 - Epochs: 48 - Learning rate: 1e-4 - Data augmentation: No - Optimizer: [Ademamix](https://arxiv.org/abs/2409.03137) ### How to use ```python import torch from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline from datasets import load_dataset device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "primeline/whisper-tiny-german-1224" model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True ) model.to(device) processor = AutoProcessor.from_pretrained(model_id) pipe = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, max_new_tokens=128, chunk_length_s=30, batch_size=16, return_timestamps=True, torch_dtype=torch_dtype, device=device, ) dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation") sample = dataset[0]["audio"] result = pipe(sample) print(result["text"]) ``` ## [About us](https://primeline-ai.com/en/) [![primeline AI](https://primeline-ai.com/wp-content/uploads/2024/02/pl_ai_bildwortmarke_original.svg)](https://primeline-ai.com/en/) Your partner for AI infrastructure in Germany Experience the powerful AI infrastructure that drives your ambitions in Deep Learning, Machine Learning & High-Performance Computing. Optimized for AI training and inference. Model author: [Florian Zimmermeister](https://huggingface.co/flozi00) Disclaimer ``` This model is not a product of the primeLine Group. It represents research conducted by [Florian Zimmermeister](https://huggingface.co/flozi00), with computing power sponsored by primeLine. The model is published under this account by primeLine, but it is not a commercial product of primeLine Solutions GmbH. Please be aware that while we have tested and developed this model to the best of our abilities, errors may still occur. Use of this model is at your own risk. We do not accept liability for any incorrect outputs generated by this model. ```
Manucn10/kaggle-v3	Manucn10	2025-05-26T09:23:11Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-26T09:23:01Z	--- 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. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
TanAlexanderlz/ALL_RGBCROP_ori16F-8B16F	TanAlexanderlz	2025-05-26T09:23:04Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base-finetuned-kinetics", "base_model:finetune:MCG-NJU/videomae-base-finetuned-kinetics", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2025-05-26T08:33:29Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base-finetuned-kinetics tags: - generated_from_trainer metrics: - accuracy model-index: - name: ALL_RGBCROP_ori16F-8B16F results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ALL_RGBCROP_ori16F-8B16F This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6047 - Accuracy: 0.8443 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - 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 - training_steps: 768 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4388 \| 0.125 \| 96 \| 0.4338 \| 0.7988 \| \| 0.2352 \| 1.125 \| 192 \| 0.6832 \| 0.7622 \| \| 0.1411 \| 2.125 \| 288 \| 0.8688 \| 0.8476 \| \| 0.0005 \| 3.125 \| 384 \| 0.9177 \| 0.8354 \| \| 0.0002 \| 4.125 \| 480 \| 1.0111 \| 0.8354 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
Darkknight535/Contrl-Stheno-v1-8B	Darkknight535	2025-05-26T09:22:58Z	7	3	transformers	[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "Sao10K/L3-8B-Stheno-v3.2", "Delta-Vector/Control-Nanuq-8B", "conversational", "en", "base_model:Delta-Vector/Control-Nanuq-8B", "base_model:merge:Delta-Vector/Control-Nanuq-8B", "base_model:Sao10K/L3-8B-Stheno-v3.2", "base_model:merge:Sao10K/L3-8B-Stheno-v3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T08:26:12Z	--- base_model: - Sao10K/L3-8B-Stheno-v3.2 - Delta-Vector/Control-Nanuq-8B tags: - merge - mergekit - lazymergekit - Sao10K/L3-8B-Stheno-v3.2 - Delta-Vector/Control-Nanuq-8B language: - en library_name: transformers --- <style> ebody { font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #FF69B4 0%, #800080 100%); color: #FFFFFF; margin: 0; padding: 0; font-size: 16px; min-height: 100vh; } .container { margin: 20px; 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border: 1px solid rgba(255, 20, 147, 0.3); border-radius: 8px; padding: 15px; margin-bottom: 15px; } .review-card:last-child { margin-bottom: 0; } </style> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>Contrl-Stheno-8B-v1</title> <link href="https://fonts.googleapis.com/css2?family=Quicksand:wght@400;500;600&display=swap" rel="stylesheet"> <link href="styles.css" rel="stylesheet"> </head> <body> <div class="container"> <div class="header"> <h1>Contrl-Stheno-8B-v1</h1> </div> <div class="info"> <img src="https://huggingface.co/Darkknight535/Contrl-Stheno-v1-8B/resolve/main/img_.jpg" alt="Model banner"> <div class="creator-section"> <div class="creator-badge"> <span class="creator-label">Created by</span> <a href="https://huggingface.co/Darkknight535" target="_blank" class="creator-link"> <span class="creator-name">Darkknight535</span> <span class="creator-arrow">→</span> </a> </div> </div> <div class="model-info"> <h2>Model Information</h2> <div class="info-card"> <div class="info-header"> <h3>Contrl-Stheno-8B-v1</h3> <div class="model-tags"> <span class="model-tag">Stheno = Stheno-v3.2</span> <span class="model-tag">Contrl = Control-Nanuq</span> <span class="model-tag">8b Parameters</span> </div> </div> <div class="model-composition"> <h4>Model Composition</h4> <ul class="composition-list"> <li><span class="model-component"><a href="https://huggingface.co/Delta-Vector/Control-Nanuq-8B" target="_blank">Control Nanuq 8B</a></span> Sweetness and Creativity capabilities</li> <li><span class="model-component"><a href="https://huggingface.co/Sao10K/L3-8B-Stheno-v3.2" target="_blank">Stheno-3.2 8B</a></span> Roleplay and logic</li> </ul> </div> <div class="model-description"> <p>An Experiment of mine which turned out to be great! It has dialogues I hadn't found even in 70B models.</p> </div> </div> <!--<div class="metrics-section"> <details open> <summary>User Reviews</summary> <div class="progress-metrics"> <div> <div class="review-card"> <div> <span>[USERNAME]</span> </div> <p>[REVIEW]</p> </div> <div class="review-card"> <div> <span>[USERNAME]</span> </div> <p>[REVIEW]</p> </div> <div class="review-card"> <div> <span>[USERNAME]</span> </div> <p>[REVIEW]</p> </div> </div> </div> </details> </div>--> </div> <div class="section-container"> <h2>Reccomended Templates & Prompts</h2> <div class="template-card"> <div class="template-item"> <div class="template-content"> <a href="" target="_blank" class="template-link"> Sao10k's Euryale System Prompt OR EVA System Prompt <span class="link-arrow">→</span> </a> <span class="template-author">by Sao10k and EVA-UNIT-01</span> </div> </div> </div> </div> <div class="section-container"> <h2>Quantized Versions</h2> <div class="quantized-container"> <div class="quantized-section"> <h3>GGUF Quantizations</h3> <div class="quantized-items"> <div class="quantized-item"> <span class="author">mradermacher</span> <a href="https://huggingface.co/mradermacher/Contrl-Stheno-v1-8B-GGUF" target="_blank"> STATIC-GGUF <span class="link-arrow">→</span> </a> </div> </div> </div> <div class="quantized-section"> <h3>Imat GGUF Quantizations</h3> <div class="quantized-items"> <div class="quantized-item"> <span class="author">mradermacher</span> <a href="https://huggingface.co/mradermacher/Contrl-Stheno-v1-8B-i1-GGUF" target="_blank"> IMAT-GGUF <span class="link-arrow">→</span> </a> </div> </div> </div> </div> </div> <div class="support-section"> <h2>Thanks to these people (I just made a script and Stole SteelSkull's Readme Template)</h2> <div class="support-buttons"> <a href="https://huggingface.co/Sao10k" target="_blank" class="button"> Support Sao10K </a> <a href="https://huggingface.co/Delta-Vector" target="_blank" class="button"> Support Delta-Vector </a> <a href="https://huggingface.co/Steelskull" target="_blank" class="button"> Support SteelSkull </a> </div> </div> </div> </div> </body> </html>
StanfordAIMI/SRR-BERT2BERT	StanfordAIMI	2025-05-26T09:21:53Z	1	0	transformers	[ "transformers", "safetensors", "encoder-decoder", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-03-27T00:11:50Z	--- library_name: transformers tags: [] --- ## 🎬 Get Started ```python import torch from transformers import EncoderDecoderModel, AutoTokenizer, AutoConfig # step 1: Setup constant model_name = "StanfordAIMI/SRR-BERT2BERT" device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # step 2: Load Processor and Model model = EncoderDecoderModel.from_pretrained(model_name).to(device) tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, padding_side="right", use_fast=False) model.config.decoder_start_token_id = tokenizer.cls_token_id model.config.bos_token_id = tokenizer.cls_token_id model.eval() # step 3: Inference (example from MIMIC-CXR dataset) input_text = "CHEST RADIOGRAPH PERFORMED ON ___ COMPARISON: Prior exam from ___. CLINICAL HISTORY: Weakness, assess pneumonia. FINDINGS: Frontal and lateral views of the chest were provided. Midline sternotomy wires are again noted. The heart is poorly assessed, though remains enlarged. There are at least small bilateral pleural effusions. There may be mild interstitial edema. No pneumothorax. Bony structures are demineralized with kyphotic angulation in the lower T-spine again noted. IMPRESSION: Limited exam with small bilateral effusions, cardiomegaly, and possible mild interstitial edema." inputs = tokenizer(input_text, padding="max_length", truncation=True, max_length=512, return_tensors="pt") inputs["attention_mask"] = inputs["input_ids"].ne(tokenizer.pad_token_id) # Add attention mask input_ids = inputs['input_ids'].to(device) attention_mask=inputs["attention_mask"].to(device) generated_ids = model.generate( input_ids, attention_mask=attention_mask, max_new_tokens=286, min_new_tokens= 120,decoder_start_token_id=model.config.decoder_start_token_id, num_beams=5, early_stopping=True, max_length=None )[0] decoded = tokenizer.decode(generated_ids, skip_special_tokens=True) print(decoded) ```
sergioalves/2c06465b-9b38-411f-8b9d-adb619d64e94	sergioalves	2025-05-26T09:21:04Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:Orenguteng/Llama-3-8B-Lexi-Uncensored", "base_model:quantized:Orenguteng/Llama-3-8B-Lexi-Uncensored", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-26T08:33:52Z	--- base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored library_name: transformers model_name: 2c06465b-9b38-411f-8b9d-adb619d64e94 tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for 2c06465b-9b38-411f-8b9d-adb619d64e94 This model is a fine-tuned version of [Orenguteng/Llama-3-8B-Lexi-Uncensored](https://huggingface.co/Orenguteng/Llama-3-8B-Lexi-Uncensored). 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="sergioalves/2c06465b-9b38-411f-8b9d-adb619d64e94", 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/dedok-yo/s56-7/runs/d9oiw9zl) 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: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## 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}} } ```
TofuTank/orbit_mldx4	TofuTank	2025-05-26T09:20:55Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-26T09:18:00Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
GAYOEN/find-bench	GAYOEN	2025-05-26T09:19:21Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-26T09:16:35Z	--- 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]
Yogi99/Aadi9670	Yogi99	2025-05-26T09:19:09Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-26T09:19:09Z	--- license: apache-2.0 ---
mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF	mradermacher	2025-05-26T09:18:44Z	0	1	transformers	[ "transformers", "gguf", "en", "dataset:BeaverAI/REDACTED1", "dataset:BeaverAI/REDACTED2", "dataset:BeaverAI/REDACTED3", "dataset:BeaverAI/REDACTED4", "dataset:BeaverAI/REDACTED5", "dataset:BeaverAI/REDACTED6", "dataset:PJMixers-Dev/Lit-axo-Shuffled", "dataset:PJMixers-Dev/Mielikki_Erebus-87k-axo", "dataset:PJMixers/RyokoAI_Honeyfeed3600-Cleanish", "dataset:PJMixers-Dev/allura-org_fujin-cleaned-stage-2-axo", "dataset:Nelathan/synthetic-sugar-quill", "dataset:PJMixers-Dev/winglian_visual-novels-json-axo-dropped-long", "dataset:PJMixers-Dev/recursal_SCP-RECURSAL-Cleaned", "dataset:PJMixers-Dev/Subtitles", "dataset:PJMixers-Dev/KaraKaraWitch_AnimeSubtitle-axo", "dataset:PJMixers/AP-News-2024", "dataset:PJMixers-Dev/Fundus-AP-News-Formatted", "dataset:PJMixers-Dev/Fundus-AP-News-2-Formatted", "dataset:PJMixers-Dev/goodwiki-2024-12-04-axo", "dataset:epfl-llm/guidelines", "dataset:PJMixers-Dev/allenai_tulu-3-sft-mixture-filtered-2-ShareGPT", "dataset:OpenLeecher/lmsys_chat_1m_clean", "dataset:PJMixers-Dev/Gryphe-Aesir-RPG-Charcards-Opus-Mixed", "dataset:allura-org/gryphe-sonnet-3.5-charcards-names-added", "dataset:anthracite-org/c2_logs_32k_llama3_qwen2_v1.3", "dataset:PJMixers-Dev/MinervaAI_Aesir-Preview-Anon", "dataset:PJMixers-Dev/lemonilia_LimaRP-Simple-CustomShareGPT-Shuffled", "dataset:Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned", "dataset:PJMixers-Dev/NyxKrage_chub-logs-sharegpt-longest-CustomShareGPT", "dataset:PJMixers/OpenLeecher_Teatime_all_logs_longest-ShareGPT", "dataset:grimulkan/aicg-logs-augmented", "dataset:grimulkan/PIPPA-augmented-dedup", "dataset:PJMixers/grimulkan_bluemoon_Karen_cleaned-carded-formatted", "dataset:PJMixers/lodrick-the-lafted_OpusStories-ShareGPT", "dataset:Gryphe/ChatGPT-4o-Writing-Prompts", "dataset:Gryphe/Opus-WritingPrompts", "dataset:anthracite-org/nopm_claude_writing_fixed", "dataset:PJMixers-Dev/Tiefighter-13B-Fake-Distill-ShareGPT", "dataset:allura-org/fujin-instruct-v2", "dataset:ToastyPigeon/gutenberg-sft", "dataset:PocketDoc/Dans-Prosemaxx-Adventure", "dataset:PocketDoc/Dans-Failuremaxx-Adventure-3", "dataset:TheDrummer/AmoralQA-v2", "base_model:PJMixers-Dev/Granite-3.1-Earthen-v0.3-3B-A800M", "base_model:quantized:PJMixers-Dev/Granite-3.1-Earthen-v0.3-3B-A800M", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-25T20:42:33Z	--- base_model: PJMixers-Dev/Granite-3.1-Earthen-v0.3-3B-A800M datasets: - BeaverAI/REDACTED1 - BeaverAI/REDACTED2 - BeaverAI/REDACTED3 - BeaverAI/REDACTED4 - BeaverAI/REDACTED5 - BeaverAI/REDACTED6 - PJMixers-Dev/Lit-axo-Shuffled - PJMixers-Dev/Mielikki_Erebus-87k-axo - PJMixers/RyokoAI_Honeyfeed3600-Cleanish - PJMixers-Dev/allura-org_fujin-cleaned-stage-2-axo - Nelathan/synthetic-sugar-quill - PJMixers-Dev/winglian_visual-novels-json-axo-dropped-long - PJMixers-Dev/recursal_SCP-RECURSAL-Cleaned - PJMixers-Dev/Subtitles - PJMixers-Dev/KaraKaraWitch_AnimeSubtitle-axo - PJMixers/AP-News-2024 - PJMixers-Dev/Fundus-AP-News-Formatted - PJMixers-Dev/Fundus-AP-News-2-Formatted - PJMixers-Dev/goodwiki-2024-12-04-axo - epfl-llm/guidelines - PJMixers-Dev/allenai_tulu-3-sft-mixture-filtered-2-ShareGPT - OpenLeecher/lmsys_chat_1m_clean - PJMixers-Dev/Gryphe-Aesir-RPG-Charcards-Opus-Mixed - allura-org/gryphe-sonnet-3.5-charcards-names-added - anthracite-org/c2_logs_32k_llama3_qwen2_v1.3 - PJMixers-Dev/MinervaAI_Aesir-Preview-Anon - PJMixers-Dev/lemonilia_LimaRP-Simple-CustomShareGPT-Shuffled - Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned - PJMixers-Dev/NyxKrage_chub-logs-sharegpt-longest-CustomShareGPT - PJMixers/OpenLeecher_Teatime_all_logs_longest-ShareGPT - grimulkan/aicg-logs-augmented - grimulkan/PIPPA-augmented-dedup - PJMixers/grimulkan_bluemoon_Karen_cleaned-carded-formatted - PJMixers/lodrick-the-lafted_OpusStories-ShareGPT - Gryphe/ChatGPT-4o-Writing-Prompts - Gryphe/Opus-WritingPrompts - anthracite-org/nopm_claude_writing_fixed - PJMixers-Dev/Tiefighter-13B-Fake-Distill-ShareGPT - allura-org/fujin-instruct-v2 - ToastyPigeon/gutenberg-sft - PocketDoc/Dans-Prosemaxx-Adventure - PocketDoc/Dans-Failuremaxx-Adventure-3 - TheDrummer/AmoralQA-v2 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/PJMixers-Dev/Granite-3.1-Earthen-v0.3-3B-A800M <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-Q2_K.gguf) \| i1-Q2_K \| 1.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 1.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-IQ3_S.gguf) \| i1-IQ3_S \| 1.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 1.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-IQ3_M.gguf) \| i1-IQ3_M \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 1.7 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 1.8 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 2.0 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-Q4_0.gguf) \| i1-Q4_0 \| 2.0 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 2.0 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 2.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-Q4_1.gguf) \| i1-Q4_1 \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-Q6_K.gguf) \| i1-Q6_K \| 2.8 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
zhengxd10/test-model-5	zhengxd10	2025-05-26T09:15:18Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T09:00:06Z	--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
Hyaline/Domaino1s-finance	Hyaline	2025-05-26T09:13:41Z	1	0	null	[ "safetensors", "qwen2", "arxiv:2501.14431", "license:apache-2.0", "region:us" ]	null	2025-01-19T01:32:02Z	--- license: apache-2.0 --- This repository stores the model parameters for our paper [Domaino1s: Guiding LLM Reasoning for Explainable Answers in High-Stakes Domains](https://arxiv.org/abs/2501.14431). Our paper is now accepted as findings of ACL 2025. More details: [Domaino1s](https://github.com/Hyalinesky/Domaino1s) ## Citation If you find our work helpful, feel free to give us a cite. ``` @article{chu2025domaino1s, title={Domaino1s: Guiding LLM Reasoning for Explainable Answers in High-Stakes Domains}, author={Chu, Xu and Tan, Zhijie and Xue, Hanlin and Wang, Guanyu and Mo, Tong and Li, Weiping}, journal={arXiv preprint arXiv:2501.14431}, year={2025} } ```
LordLucifer69420/dummy-model	LordLucifer69420	2025-05-26T09:10:20Z	0	0	transformers	[ "transformers", "safetensors", "camembert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-05-26T09:09:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ggml-org/Qwen2.5-Omni-3B-GGUF	ggml-org	2025-05-26T09:07:29Z	0	0	null	[ "gguf", "multimodal", "any-to-any", "en", "base_model:Qwen/Qwen2.5-Omni-3B", "base_model:quantized:Qwen/Qwen2.5-Omni-3B", "license:other", "endpoints_compatible", "region:us", "conversational" ]	any-to-any	2025-05-26T08:52:28Z	--- license: other license_name: qwen-research license_link: https://huggingface.co/Qwen/Qwen2.5-Omni-3B/blob/main/LICENSE language: - en tags: - multimodal pipeline_tag: any-to-any base_model: - Qwen/Qwen2.5-Omni-3B --- # Qwen2.5-Omni-3B-GGUF Original model: https://huggingface.co/Qwen/Qwen2.5-Omni-3B Modalities: - ✅ Text input - ✅ Audio input - ✅ Image input - ❌ Video input - ❌ Audio generation Ref PR: https://github.com/ggml-org/llama.cpp/pull/13784
bilyxu/DeepSeek-7B-InterTrade-0526-2	bilyxu	2025-05-26T09:04:39Z	0	0	transformers	[ "transformers", "pytorch", "qwen2", "text-generation", "unsloth", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T08:43:16Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ahmedelgebaly/llama-3.1-8b-Squad_SciQ_HotpotQA_Equal_E5	ahmedelgebaly	2025-05-26T09:03:58Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:meta-llama/Llama-3.1-8B", "base_model:adapter:meta-llama/Llama-3.1-8B", "license:llama3", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-23T22:33:40Z	--- library_name: peft license: llama3 base_model: meta-llama/Meta-Llama-3.1-8B tags: - axolotl - generated_from_trainer model-index: - name: llama-3.1-8b-Squad_SciQ_HotpotQA_Equal_E5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml base_model: meta-llama/Meta-Llama-3.1-8B model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: false load_in_4bit: true strict: false datasets: - path: ahmedelgebaly/SQuad_SciQ_HotpotQA_Alpaca_Equal type: alpaca split: train test_datasets: - path: ahmedelgebaly/SQuad_SciQ_HotpotQA_Alpaca_Equal type: alpaca split: validation dataset_prepared_path: output_dir: ./outputs/qlora-out adapter: qlora sequence_len: 4096 sample_packing: true pad_to_sequence_len: true lora_r: 32 lora_alpha: 64 #Before it was 16 lora_dropout: 0.05 lora_target_modules: #Before it was empty - q_proj - k_proj - v_proj - o_proj - gate_proj - up_proj - down_proj lora_target_linear: true lora_fan_in_fan_out: wandb_project: llama-3.1-8b-Squad_SciQ_HotpotQA_Equal_E5 wandb_entity: wandb_watch: wandb_name: llama-3.1-8b-Squad_SciQ_HotpotQA_Equal_E5 wandb_log_model: hub_model_id: ahmedelgebaly/llama-3.1-8b-Squad_SciQ_HotpotQA_Equal_E5 gradient_accumulation_steps: 4 micro_batch_size: 4 num_epochs: 5 optimizer: paged_adamw_32bit lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: true #Before it was false bf16: auto tf32: false gradient_checkpointing: true flash_attention: true warmup_steps: 50 #Before it was 10 evals_per_epoch: 4 saves_per_epoch: 1 weight_decay: 0.0 special_tokens: pad_token: "<\|end_of_text\|>" ``` </details><br> # llama-3.1-8b-Squad_SciQ_HotpotQA_Equal_E5 This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5810 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0036 \| 1 \| 1.7171 \| \| 0.8739 \| 0.2527 \| 70 \| 0.9249 \| \| 0.8253 \| 0.5054 \| 140 \| 0.8994 \| \| 0.8202 \| 0.7581 \| 210 \| 0.8861 \| \| 0.8264 \| 1.0081 \| 280 \| 0.8787 \| \| 0.699 \| 1.2608 \| 350 \| 0.9036 \| \| 0.6841 \| 1.5135 \| 420 \| 0.9086 \| \| 0.6703 \| 1.7662 \| 490 \| 0.8964 \| \| 0.5992 \| 2.0171 \| 560 \| 1.0023 \| \| 0.4401 \| 2.2699 \| 630 \| 1.0065 \| \| 0.4268 \| 2.5226 \| 700 \| 1.0162 \| \| 0.4248 \| 2.7753 \| 770 \| 1.0256 \| \| 0.2137 \| 3.0244 \| 840 \| 1.3936 \| \| 0.1899 \| 3.2771 \| 910 \| 1.2558 \| \| 0.1912 \| 3.5298 \| 980 \| 1.2804 \| \| 0.1759 \| 3.7825 \| 1050 \| 1.2883 \| \| 0.1154 \| 4.0316 \| 1120 \| 1.4508 \| \| 0.0799 \| 4.2843 \| 1190 \| 1.5816 \| \| 0.0776 \| 4.5370 \| 1260 \| 1.5807 \| \| 0.0825 \| 4.7897 \| 1330 \| 1.5810 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.45.2 - Pytorch 2.3.1+cu121 - Datasets 3.0.1 - Tokenizers 0.20.1
ahmedelgebaly/llama-3.1-8b-squadv2_SciQ_e5	ahmedelgebaly	2025-05-26T09:03:35Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:meta-llama/Llama-3.1-8B", "base_model:adapter:meta-llama/Llama-3.1-8B", "license:llama3", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-23T13:38:23Z	--- library_name: peft license: llama3 base_model: meta-llama/Meta-Llama-3.1-8B tags: - axolotl - generated_from_trainer model-index: - name: llama-3.1-8b-squadv2_SciQ_e5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml base_model: meta-llama/Meta-Llama-3.1-8B # same model you originally used # Load your previously fine-tuned model as a PEFT adapter peft_model: ahmedelgebaly/llama-3.1-8b-squadv2_e5 model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: false load_in_4bit: true strict: false datasets: - path: ahmedelgebaly/SciQ_Alpaca type: alpaca split: train test_datasets: - path: ahmedelgebaly/SciQ_Alpaca type: alpaca split: validation dataset_prepared_path: output_dir: ./outputs/qlora-out adapter: qlora lora_model_dir: sequence_len: 4096 sample_packing: true pad_to_sequence_len: true lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_modules: lora_target_linear: true lora_fan_in_fan_out: wandb_project: llama-3.1-8b-squadv2_SciQ_e5 wandb_entity: wandb_watch: wandb_name: llama-3.1-8b-squadv2_SciQ_e5 wandb_log_model: hub_model_id: ahmedelgebaly/llama-3.1-8b-squadv2_SciQ_e5 gradient_accumulation_steps: 4 micro_batch_size: 4 num_epochs: 5 optimizer: paged_adamw_32bit lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 evals_per_epoch: 4 eval_table_size: saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: pad_token: "<\|end_of_text\|>" ``` </details><br> # llama-3.1-8b-squadv2_SciQ_e5 This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8976 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.7866 \| 0.0305 \| 1 \| 1.8420 \| \| 1.1272 \| 0.2443 \| 8 \| 1.0962 \| \| 0.8409 \| 0.4885 \| 16 \| 0.9651 \| \| 0.8668 \| 0.7328 \| 24 \| 0.9332 \| \| 0.8579 \| 0.9771 \| 32 \| 0.9190 \| \| 0.8342 \| 1.2137 \| 40 \| 0.9073 \| \| 0.799 \| 1.4580 \| 48 \| 0.9008 \| \| 0.8282 \| 1.7023 \| 56 \| 0.8955 \| \| 0.8018 \| 1.9466 \| 64 \| 0.8928 \| \| 0.8041 \| 2.1832 \| 72 \| 0.8922 \| \| 0.8032 \| 2.4275 \| 80 \| 0.8903 \| \| 0.7785 \| 2.6718 \| 88 \| 0.8875 \| \| 0.7522 \| 2.9160 \| 96 \| 0.8861 \| \| 0.7369 \| 3.1527 \| 104 \| 0.8948 \| \| 0.7527 \| 3.3969 \| 112 \| 0.8921 \| \| 0.7414 \| 3.6412 \| 120 \| 0.8928 \| \| 0.7227 \| 3.8855 \| 128 \| 0.8935 \| \| 0.7021 \| 4.1221 \| 136 \| 0.8948 \| \| 0.7255 \| 4.3664 \| 144 \| 0.8972 \| \| 0.7037 \| 4.6107 \| 152 \| 0.8977 \| \| 0.7006 \| 4.8550 \| 160 \| 0.8976 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.45.2 - Pytorch 2.3.1+cu121 - Datasets 3.0.1 - Tokenizers 0.20.1

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