librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags sequence	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
CeciGonSer/translation_pu_es_sintetico_chamo_hel_1ep	CeciGonSer	2025-06-16T02:16:14Z	0	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-15T23:59:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
semtwo/kobart-base-v2-with-wiki-dataset	semtwo	2025-06-16T02:15:50Z	0	0	null	[ "license:other", "region:us" ]	null	2025-06-16T02:15:50Z	--- license: other license_name: kobart-base-v2 license_link: LICENSE ---
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.05_0.05_epoch2	MinaMila	2025-06-16T02:15:10Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T02:13:11Z	--- 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. 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]
New-tutorial-Pakistani-TikTok-Videos/FULL.VIDEO.Pakistani.TikTok.Viral.Video.Tutorial.Official	New-tutorial-Pakistani-TikTok-Videos	2025-06-16T02:13:07Z	0	0	null	[ "region:us" ]	null	2025-06-16T02:12:48Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
actuator-x/Llama-3.2-3B-Tele-Q4_K_M-GGUF	actuator-x	2025-06-16T02:11:23Z	0	0	null	[ "gguf", "nlp", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:AliMaatouk/Llama-3.2-3B-Tele", "base_model:quantized:AliMaatouk/Llama-3.2-3B-Tele", "license:llama3.2", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T02:11:13Z	--- license: llama3.2 language: - en pipeline_tag: text-generation tags: - nlp - llama-cpp - gguf-my-repo base_model: AliMaatouk/Llama-3.2-3B-Tele --- # actuator-x/Llama-3.2-3B-Tele-Q4_K_M-GGUF This model was converted to GGUF format from [`AliMaatouk/Llama-3.2-3B-Tele`](https://huggingface.co/AliMaatouk/Llama-3.2-3B-Tele) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/AliMaatouk/Llama-3.2-3B-Tele) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo actuator-x/Llama-3.2-3B-Tele-Q4_K_M-GGUF --hf-file llama-3.2-3b-tele-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo actuator-x/Llama-3.2-3B-Tele-Q4_K_M-GGUF --hf-file llama-3.2-3b-tele-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo actuator-x/Llama-3.2-3B-Tele-Q4_K_M-GGUF --hf-file llama-3.2-3b-tele-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo actuator-x/Llama-3.2-3B-Tele-Q4_K_M-GGUF --hf-file llama-3.2-3b-tele-q4_k_m.gguf -c 2048 ```
gradientrouting-spar/horizontal_2_proxy_ntrain_25_ntrig_9_animals_3x3_seed_1_20250616_020232	gradientrouting-spar	2025-06-16T02:10:49Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T02:10:41Z	--- 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. 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]
FormlessAI/cc3b0236-9ddf-4d9b-9243-f60acb10c299	FormlessAI	2025-06-16T02:10:13Z	0	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "generated_from_trainer", "trl", "grpo", "arxiv:2402.03300", "base_model:databricks/dolly-v2-3b", "base_model:finetune:databricks/dolly-v2-3b", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T20:12:55Z	--- base_model: databricks/dolly-v2-3b library_name: transformers model_name: cc3b0236-9ddf-4d9b-9243-f60acb10c299 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for cc3b0236-9ddf-4d9b-9243-f60acb10c299 This model is a fine-tuned version of [databricks/dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="FormlessAI/cc3b0236-9ddf-4d9b-9243-f60acb10c299", 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/phoenix-formless/Gradients/runs/odyon2h0) 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: 0.18.1 - Transformers: 4.52.4 - Pytorch: 2.7.0+cu128 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
mlx-community/llm-jp-3.1-1.8b-instruct4-8bit	mlx-community	2025-06-16T02:09:40Z	0	0	mlx	[ "mlx", "safetensors", "llama", "text-generation", "conversational", "en", "ja", "base_model:llm-jp/llm-jp-3.1-1.8b-instruct4", "base_model:quantized:llm-jp/llm-jp-3.1-1.8b-instruct4", "license:apache-2.0", "8-bit", "region:us" ]	text-generation	2025-06-16T02:06:24Z	--- license: apache-2.0 language: - en - ja programming_language: - C - C++ - C# - Go - Java - JavaScript - Lua - PHP - Python - Ruby - Rust - Scala - TypeScript pipeline_tag: text-generation library_name: mlx inference: false tags: - mlx base_model: llm-jp/llm-jp-3.1-1.8b-instruct4 --- # mlx-community/llm-jp-3.1-1.8b-instruct4-8bit This model [mlx-community/llm-jp-3.1-1.8b-instruct4-8bit](https://huggingface.co/mlx-community/llm-jp-3.1-1.8b-instruct4-8bit) was converted to MLX format from [llm-jp/llm-jp-3.1-1.8b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-1.8b-instruct4) using mlx-lm version 0.24.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/llm-jp-3.1-1.8b-instruct4-8bit") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
jeremyys/multilingual-e5-base-Q5_0-GGUF	jeremyys	2025-06-16T02:08:48Z	0	0	sentence-transformers	[ "sentence-transformers", "gguf", "mteb", "Sentence Transformers", "sentence-similarity", "llama-cpp", "gguf-my-repo", "multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "om", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sa", "sd", "si", "sk", "sl", "so", "sq", "sr", "su", "sv", "sw", "ta", "te", "th", "tl", "tr", "ug", "uk", "ur", "uz", "vi", "xh", "yi", "zh", "base_model:intfloat/multilingual-e5-base", "base_model:quantized:intfloat/multilingual-e5-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us", "feature-extraction" ]	sentence-similarity	2025-06-16T02:08:44Z	--- tags: - mteb - Sentence Transformers - sentence-similarity - sentence-transformers - llama-cpp - gguf-my-repo language: - multilingual - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - 'no' - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh license: mit base_model: intfloat/multilingual-e5-base model-index: - name: multilingual-e5-base results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 78.97014925373135 - type: ap value: 43.69351129103008 - type: f1 value: 73.38075030070492 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (de) type: mteb/amazon_counterfactual config: de split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 71.7237687366167 - type: ap value: 82.22089859962671 - type: f1 value: 69.95532758884401 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en-ext) type: mteb/amazon_counterfactual config: en-ext split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 79.65517241379312 - type: ap value: 28.507918657094738 - type: f1 value: 66.84516013726119 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (ja) type: mteb/amazon_counterfactual config: ja split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.32976445396146 - type: ap value: 20.720481637566014 - type: f1 value: 59.78002763416003 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 90.63775 - type: ap value: 87.22277903861716 - type: f1 value: 90.60378636386807 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 44.546 - type: f1 value: 44.05666638370923 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (de) type: mteb/amazon_reviews_multi config: de split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 41.828 - type: f1 value: 41.2710255644252 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (es) type: mteb/amazon_reviews_multi config: es split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.534 - type: f1 value: 39.820743174270326 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (fr) type: mteb/amazon_reviews_multi config: fr split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 39.684 - type: f1 value: 39.11052682815307 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (ja) type: mteb/amazon_reviews_multi config: ja split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 37.436 - type: f1 value: 37.07082931930871 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (zh) type: mteb/amazon_reviews_multi config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 37.226000000000006 - type: f1 value: 36.65372077739185 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 22.831000000000003 - type: map_at_10 value: 36.42 - type: map_at_100 value: 37.699 - type: map_at_1000 value: 37.724000000000004 - type: map_at_3 value: 32.207 - type: map_at_5 value: 34.312 - type: mrr_at_1 value: 23.257 - type: mrr_at_10 value: 36.574 - type: mrr_at_100 value: 37.854 - type: mrr_at_1000 value: 37.878 - type: mrr_at_3 value: 32.385000000000005 - type: mrr_at_5 value: 34.48 - type: ndcg_at_1 value: 22.831000000000003 - type: ndcg_at_10 value: 44.230000000000004 - type: ndcg_at_100 value: 49.974000000000004 - type: ndcg_at_1000 value: 50.522999999999996 - type: ndcg_at_3 value: 35.363 - type: ndcg_at_5 value: 39.164 - type: precision_at_1 value: 22.831000000000003 - type: precision_at_10 value: 6.935 - type: precision_at_100 value: 0.9520000000000001 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 14.841 - type: precision_at_5 value: 10.754 - type: recall_at_1 value: 22.831000000000003 - type: recall_at_10 value: 69.346 - type: recall_at_100 value: 95.235 - type: recall_at_1000 value: 99.36 - type: recall_at_3 value: 44.523 - type: recall_at_5 value: 53.769999999999996 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 40.27789869854063 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 35.41979463347428 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 58.22752045109304 - type: mrr value: 71.51112430198303 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 84.71147646622866 - type: cos_sim_spearman value: 85.059167046486 - type: euclidean_pearson value: 75.88421613600647 - type: euclidean_spearman value: 75.12821787150585 - type: manhattan_pearson value: 75.22005646957604 - type: manhattan_spearman value: 74.42880434453272 - task: type: BitextMining dataset: name: MTEB BUCC (de-en) type: mteb/bucc-bitext-mining config: de-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 99.23799582463465 - type: f1 value: 99.12665274878218 - type: precision value: 99.07098121085595 - type: recall value: 99.23799582463465 - task: type: BitextMining dataset: name: MTEB BUCC (fr-en) type: mteb/bucc-bitext-mining config: fr-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 97.88685890380806 - type: f1 value: 97.59336708489249 - type: precision value: 97.44662117543473 - type: recall value: 97.88685890380806 - task: type: BitextMining dataset: name: MTEB BUCC (ru-en) type: mteb/bucc-bitext-mining config: ru-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 97.47142362313821 - type: f1 value: 97.1989377670015 - type: precision value: 97.06384944001847 - type: recall value: 97.47142362313821 - task: type: BitextMining dataset: name: MTEB BUCC (zh-en) type: mteb/bucc-bitext-mining config: zh-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 98.4728804634018 - type: f1 value: 98.2973494821836 - type: precision value: 98.2095839915745 - type: recall value: 98.4728804634018 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 82.74025974025975 - type: f1 value: 82.67420447730439 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 35.0380848063507 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 29.45956405670166 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: None metrics: - type: map_at_1 value: 32.122 - type: map_at_10 value: 42.03 - type: map_at_100 value: 43.364000000000004 - type: map_at_1000 value: 43.474000000000004 - type: map_at_3 value: 38.804 - type: map_at_5 value: 40.585 - type: mrr_at_1 value: 39.914 - type: mrr_at_10 value: 48.227 - type: mrr_at_100 value: 49.018 - type: mrr_at_1000 value: 49.064 - type: mrr_at_3 value: 45.994 - type: mrr_at_5 value: 47.396 - type: ndcg_at_1 value: 39.914 - type: ndcg_at_10 value: 47.825 - type: ndcg_at_100 value: 52.852 - type: ndcg_at_1000 value: 54.891 - type: ndcg_at_3 value: 43.517 - type: ndcg_at_5 value: 45.493 - type: precision_at_1 value: 39.914 - type: precision_at_10 value: 8.956 - type: precision_at_100 value: 1.388 - type: precision_at_1000 value: 0.182 - type: precision_at_3 value: 20.791999999999998 - type: precision_at_5 value: 14.821000000000002 - type: recall_at_1 value: 32.122 - type: recall_at_10 value: 58.294999999999995 - type: recall_at_100 value: 79.726 - type: recall_at_1000 value: 93.099 - type: recall_at_3 value: 45.017 - type: recall_at_5 value: 51.002 - type: map_at_1 value: 29.677999999999997 - type: map_at_10 value: 38.684000000000005 - type: map_at_100 value: 39.812999999999995 - type: map_at_1000 value: 39.945 - type: map_at_3 value: 35.831 - type: map_at_5 value: 37.446 - type: mrr_at_1 value: 37.771 - type: mrr_at_10 value: 44.936 - type: mrr_at_100 value: 45.583 - type: mrr_at_1000 value: 45.634 - type: mrr_at_3 value: 42.771 - type: mrr_at_5 value: 43.994 - type: ndcg_at_1 value: 37.771 - type: ndcg_at_10 value: 44.059 - type: ndcg_at_100 value: 48.192 - type: ndcg_at_1000 value: 50.375 - type: ndcg_at_3 value: 40.172000000000004 - type: ndcg_at_5 value: 41.899 - type: precision_at_1 value: 37.771 - type: precision_at_10 value: 8.286999999999999 - type: precision_at_100 value: 1.322 - type: precision_at_1000 value: 0.178 - type: precision_at_3 value: 19.406000000000002 - type: precision_at_5 value: 13.745 - type: recall_at_1 value: 29.677999999999997 - type: recall_at_10 value: 53.071 - type: recall_at_100 value: 70.812 - type: recall_at_1000 value: 84.841 - type: recall_at_3 value: 41.016000000000005 - type: recall_at_5 value: 46.22 - type: map_at_1 value: 42.675000000000004 - type: map_at_10 value: 53.93599999999999 - type: map_at_100 value: 54.806999999999995 - type: map_at_1000 value: 54.867 - type: map_at_3 value: 50.934000000000005 - type: map_at_5 value: 52.583 - type: mrr_at_1 value: 48.339 - type: mrr_at_10 value: 57.265 - type: mrr_at_100 value: 57.873 - type: mrr_at_1000 value: 57.906 - type: mrr_at_3 value: 55.193000000000005 - type: mrr_at_5 value: 56.303000000000004 - type: ndcg_at_1 value: 48.339 - type: ndcg_at_10 value: 59.19799999999999 - type: ndcg_at_100 value: 62.743 - type: ndcg_at_1000 value: 63.99399999999999 - type: ndcg_at_3 value: 54.367 - type: ndcg_at_5 value: 56.548 - type: precision_at_1 value: 48.339 - type: precision_at_10 value: 9.216000000000001 - type: precision_at_100 value: 1.1809999999999998 - type: precision_at_1000 value: 0.134 - type: precision_at_3 value: 23.72 - type: precision_at_5 value: 16.025 - type: recall_at_1 value: 42.675000000000004 - type: recall_at_10 value: 71.437 - type: recall_at_100 value: 86.803 - type: recall_at_1000 value: 95.581 - type: recall_at_3 value: 58.434 - type: recall_at_5 value: 63.754 - type: map_at_1 value: 23.518 - type: map_at_10 value: 30.648999999999997 - type: map_at_100 value: 31.508999999999997 - type: map_at_1000 value: 31.604 - type: map_at_3 value: 28.247 - type: map_at_5 value: 29.65 - type: mrr_at_1 value: 25.650000000000002 - type: mrr_at_10 value: 32.771 - type: mrr_at_100 value: 33.554 - type: mrr_at_1000 value: 33.629999999999995 - type: mrr_at_3 value: 30.433 - type: mrr_at_5 value: 31.812 - type: ndcg_at_1 value: 25.650000000000002 - type: ndcg_at_10 value: 34.929 - type: ndcg_at_100 value: 39.382 - type: ndcg_at_1000 value: 41.913 - type: ndcg_at_3 value: 30.292 - type: ndcg_at_5 value: 32.629999999999995 - type: precision_at_1 value: 25.650000000000002 - type: precision_at_10 value: 5.311 - type: precision_at_100 value: 0.792 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 12.58 - type: precision_at_5 value: 8.994 - type: recall_at_1 value: 23.518 - type: recall_at_10 value: 46.19 - type: recall_at_100 value: 67.123 - type: recall_at_1000 value: 86.442 - type: recall_at_3 value: 33.678000000000004 - type: recall_at_5 value: 39.244 - type: map_at_1 value: 15.891 - type: map_at_10 value: 22.464000000000002 - type: map_at_100 value: 23.483 - type: map_at_1000 value: 23.613 - type: map_at_3 value: 20.080000000000002 - type: map_at_5 value: 21.526 - type: mrr_at_1 value: 20.025000000000002 - type: mrr_at_10 value: 26.712999999999997 - type: mrr_at_100 value: 27.650000000000002 - type: mrr_at_1000 value: 27.737000000000002 - type: mrr_at_3 value: 24.274 - type: mrr_at_5 value: 25.711000000000002 - type: ndcg_at_1 value: 20.025000000000002 - type: ndcg_at_10 value: 27.028999999999996 - type: ndcg_at_100 value: 32.064 - type: ndcg_at_1000 value: 35.188 - type: ndcg_at_3 value: 22.512999999999998 - type: ndcg_at_5 value: 24.89 - type: precision_at_1 value: 20.025000000000002 - type: precision_at_10 value: 4.776 - type: precision_at_100 value: 0.8500000000000001 - type: precision_at_1000 value: 0.125 - type: precision_at_3 value: 10.531 - type: precision_at_5 value: 7.811 - type: recall_at_1 value: 15.891 - type: recall_at_10 value: 37.261 - type: recall_at_100 value: 59.12 - type: recall_at_1000 value: 81.356 - type: recall_at_3 value: 24.741 - type: recall_at_5 value: 30.753999999999998 - type: map_at_1 value: 27.544 - type: map_at_10 value: 36.283 - type: map_at_100 value: 37.467 - type: map_at_1000 value: 37.574000000000005 - type: map_at_3 value: 33.528999999999996 - type: map_at_5 value: 35.028999999999996 - type: mrr_at_1 value: 34.166999999999994 - type: mrr_at_10 value: 41.866 - type: mrr_at_100 value: 42.666 - type: mrr_at_1000 value: 42.716 - type: mrr_at_3 value: 39.541 - type: mrr_at_5 value: 40.768 - type: ndcg_at_1 value: 34.166999999999994 - type: ndcg_at_10 value: 41.577 - type: ndcg_at_100 value: 46.687 - type: ndcg_at_1000 value: 48.967 - type: ndcg_at_3 value: 37.177 - type: ndcg_at_5 value: 39.097 - type: precision_at_1 value: 34.166999999999994 - type: precision_at_10 value: 7.420999999999999 - type: precision_at_100 value: 1.165 - type: precision_at_1000 value: 0.154 - type: precision_at_3 value: 17.291999999999998 - type: precision_at_5 value: 12.166 - type: recall_at_1 value: 27.544 - type: recall_at_10 value: 51.99399999999999 - type: recall_at_100 value: 73.738 - type: recall_at_1000 value: 89.33 - type: recall_at_3 value: 39.179 - type: recall_at_5 value: 44.385999999999996 - type: map_at_1 value: 26.661 - type: map_at_10 value: 35.475 - type: map_at_100 value: 36.626999999999995 - type: map_at_1000 value: 36.741 - type: map_at_3 value: 32.818000000000005 - type: map_at_5 value: 34.397 - type: mrr_at_1 value: 32.647999999999996 - type: mrr_at_10 value: 40.784 - type: mrr_at_100 value: 41.602 - type: mrr_at_1000 value: 41.661 - type: mrr_at_3 value: 38.68 - type: mrr_at_5 value: 39.838 - type: ndcg_at_1 value: 32.647999999999996 - type: ndcg_at_10 value: 40.697 - type: ndcg_at_100 value: 45.799 - type: ndcg_at_1000 value: 48.235 - type: ndcg_at_3 value: 36.516 - type: ndcg_at_5 value: 38.515 - type: precision_at_1 value: 32.647999999999996 - type: precision_at_10 value: 7.202999999999999 - type: precision_at_100 value: 1.1360000000000001 - type: precision_at_1000 value: 0.151 - type: precision_at_3 value: 17.314 - type: precision_at_5 value: 12.145999999999999 - type: recall_at_1 value: 26.661 - type: recall_at_10 value: 50.995000000000005 - type: recall_at_100 value: 73.065 - type: recall_at_1000 value: 89.781 - type: recall_at_3 value: 39.073 - type: recall_at_5 value: 44.395 - type: map_at_1 value: 25.946583333333333 - type: map_at_10 value: 33.79725 - type: map_at_100 value: 34.86408333333333 - type: map_at_1000 value: 34.9795 - type: map_at_3 value: 31.259999999999998 - type: map_at_5 value: 32.71541666666666 - type: mrr_at_1 value: 30.863749999999996 - type: mrr_at_10 value: 37.99183333333333 - type: mrr_at_100 value: 38.790499999999994 - type: mrr_at_1000 value: 38.85575000000001 - type: mrr_at_3 value: 35.82083333333333 - type: mrr_at_5 value: 37.07533333333333 - type: ndcg_at_1 value: 30.863749999999996 - type: ndcg_at_10 value: 38.52141666666667 - type: ndcg_at_100 value: 43.17966666666667 - type: ndcg_at_1000 value: 45.64608333333333 - type: ndcg_at_3 value: 34.333000000000006 - type: ndcg_at_5 value: 36.34975 - type: precision_at_1 value: 30.863749999999996 - type: precision_at_10 value: 6.598999999999999 - type: precision_at_100 value: 1.0502500000000001 - type: precision_at_1000 value: 0.14400000000000002 - type: precision_at_3 value: 15.557583333333334 - type: precision_at_5 value: 11.020000000000001 - type: recall_at_1 value: 25.946583333333333 - type: recall_at_10 value: 48.36991666666666 - type: recall_at_100 value: 69.02408333333334 - type: recall_at_1000 value: 86.43858333333331 - type: recall_at_3 value: 36.4965 - type: recall_at_5 value: 41.76258333333334 - type: map_at_1 value: 22.431 - type: map_at_10 value: 28.889 - type: map_at_100 value: 29.642000000000003 - type: map_at_1000 value: 29.742 - type: map_at_3 value: 26.998 - type: map_at_5 value: 28.172000000000004 - type: mrr_at_1 value: 25.307000000000002 - type: mrr_at_10 value: 31.763 - type: mrr_at_100 value: 32.443 - type: mrr_at_1000 value: 32.531 - type: mrr_at_3 value: 29.959000000000003 - type: mrr_at_5 value: 31.063000000000002 - type: ndcg_at_1 value: 25.307000000000002 - type: ndcg_at_10 value: 32.586999999999996 - type: ndcg_at_100 value: 36.5 - type: ndcg_at_1000 value: 39.133 - type: ndcg_at_3 value: 29.25 - type: ndcg_at_5 value: 31.023 - type: precision_at_1 value: 25.307000000000002 - type: precision_at_10 value: 4.954 - type: precision_at_100 value: 0.747 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 12.577 - type: precision_at_5 value: 8.741999999999999 - type: recall_at_1 value: 22.431 - type: recall_at_10 value: 41.134 - type: recall_at_100 value: 59.28600000000001 - type: recall_at_1000 value: 78.857 - type: recall_at_3 value: 31.926 - type: recall_at_5 value: 36.335 - type: map_at_1 value: 17.586 - type: map_at_10 value: 23.304 - type: map_at_100 value: 24.159 - type: map_at_1000 value: 24.281 - type: map_at_3 value: 21.316 - type: map_at_5 value: 22.383 - type: mrr_at_1 value: 21.645 - type: mrr_at_10 value: 27.365000000000002 - type: mrr_at_100 value: 28.108 - type: mrr_at_1000 value: 28.192 - type: mrr_at_3 value: 25.482 - type: mrr_at_5 value: 26.479999999999997 - type: ndcg_at_1 value: 21.645 - type: ndcg_at_10 value: 27.306 - type: ndcg_at_100 value: 31.496000000000002 - type: ndcg_at_1000 value: 34.53 - type: ndcg_at_3 value: 23.73 - type: ndcg_at_5 value: 25.294 - type: precision_at_1 value: 21.645 - type: precision_at_10 value: 4.797 - type: precision_at_100 value: 0.8059999999999999 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 10.850999999999999 - type: precision_at_5 value: 7.736 - type: recall_at_1 value: 17.586 - type: recall_at_10 value: 35.481 - type: recall_at_100 value: 54.534000000000006 - type: recall_at_1000 value: 76.456 - type: recall_at_3 value: 25.335 - type: recall_at_5 value: 29.473 - type: map_at_1 value: 25.095 - type: map_at_10 value: 32.374 - type: map_at_100 value: 33.537 - type: map_at_1000 value: 33.634 - type: map_at_3 value: 30.089 - type: map_at_5 value: 31.433 - type: mrr_at_1 value: 29.198 - type: mrr_at_10 value: 36.01 - type: mrr_at_100 value: 37.022 - type: mrr_at_1000 value: 37.083 - type: mrr_at_3 value: 33.94 - type: mrr_at_5 value: 35.148 - type: ndcg_at_1 value: 29.198 - type: ndcg_at_10 value: 36.729 - type: ndcg_at_100 value: 42.114000000000004 - type: ndcg_at_1000 value: 44.592 - type: ndcg_at_3 value: 32.644 - type: ndcg_at_5 value: 34.652 - type: precision_at_1 value: 29.198 - type: precision_at_10 value: 5.970000000000001 - type: precision_at_100 value: 0.967 - type: precision_at_1000 value: 0.129 - type: precision_at_3 value: 14.396999999999998 - type: precision_at_5 value: 10.093 - type: recall_at_1 value: 25.095 - type: recall_at_10 value: 46.392 - type: recall_at_100 value: 69.706 - type: recall_at_1000 value: 87.738 - type: recall_at_3 value: 35.303000000000004 - type: recall_at_5 value: 40.441 - type: map_at_1 value: 26.857999999999997 - type: map_at_10 value: 34.066 - type: map_at_100 value: 35.671 - type: map_at_1000 value: 35.881 - type: map_at_3 value: 31.304 - type: map_at_5 value: 32.885 - type: mrr_at_1 value: 32.411 - type: mrr_at_10 value: 38.987 - type: mrr_at_100 value: 39.894 - type: mrr_at_1000 value: 39.959 - type: mrr_at_3 value: 36.626999999999995 - type: mrr_at_5 value: 38.011 - type: ndcg_at_1 value: 32.411 - type: ndcg_at_10 value: 39.208 - type: ndcg_at_100 value: 44.626 - type: ndcg_at_1000 value: 47.43 - type: ndcg_at_3 value: 35.091 - 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type: max_f1 value: 77.87816307403935 --- # jeremyys/multilingual-e5-base-Q5_0-GGUF This model was converted to GGUF format from [`intfloat/multilingual-e5-base`](https://huggingface.co/intfloat/multilingual-e5-base) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/intfloat/multilingual-e5-base) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo jeremyys/multilingual-e5-base-Q5_0-GGUF --hf-file multilingual-e5-base-q5_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo jeremyys/multilingual-e5-base-Q5_0-GGUF --hf-file multilingual-e5-base-q5_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo jeremyys/multilingual-e5-base-Q5_0-GGUF --hf-file multilingual-e5-base-q5_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo jeremyys/multilingual-e5-base-Q5_0-GGUF --hf-file multilingual-e5-base-q5_0.gguf -c 2048 ```
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.05_0.05_epoch1	MinaMila	2025-06-16T02:08:28Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T02:06:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Daemontatox/Grifflet-2	Daemontatox	2025-06-16T02:05:58Z	25	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "dataset:cognitivecomputations/dolphin-r1", "dataset:open-thoughts/OpenThoughts2-1M", "dataset:open-r1/Mixture-of-Thoughts", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-05T17:04:10Z	--- base_model: qwen/qwen3-8b tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en datasets: - cognitivecomputations/dolphin-r1 - open-thoughts/OpenThoughts2-1M - open-r1/Mixture-of-Thoughts library_name: transformers new_version: qwen/qwen3-8b --- ![Grifflet-2](./image.webp) ## Model Description ### Purpose "Daemontatox/Grifflet-2" is a state-of-the-art language model designed to excel in hybrid tasks that combine conversational abilities with reasoning capabilities. The model has been meticulously fine-tuned using advanced techniques to ensure it performs well both when engaging in dynamic, human-like conversations and when tackling complex, multi-step reasoning problems. ### Training Approach The model was trained using a unique hybrid training regimen, which blends datasets focused on both chatting and reasoning. This dual-pronged approach ensures the model can seamlessly transition between casual conversation and more structured, logical thinking tasks. Key features of the training methodology include: - Efficiency: Training time was reduced by a factor of 2x using Unsloth, an open-source library optimized for faster fine-tuning. - Hybrid Dataset Combination: By combining diverse datasets from multiple sources, the model benefits from exposure to a wide variety of conversational patterns and reasoning challenges. - Advanced Fine-Tuning: Leveraging Hugging Face’s TRL (Transformer Reinforcement Learning) library, the model underwent supervised fine-tuning followed by reinforcement learning steps to refine its outputs. --- ## Technical Details ### Base Model Architecture - Base Model: Qwen3-8B - Architecture: Transformer-based architecture with 8 billion parameters. - Language: English (`en`) - Library Used: [Transformers](https://huggingface.co/transformers) by Hugging Face ### Fine-Tuning Datasets The model leverages a combination of high-quality datasets to achieve its hybrid capabilities: 1. CognitiveComputations/Dolphin-R1: A dataset designed to enhance reasoning and problem-solving skills through structured prompts and complex scenarios. 2. Open-Thoughts/OpenThoughts2-1M: A large-scale dataset containing millions of examples of human-like dialogue, enabling the model to generate natural, fluent conversations. 3. Open-R1/Mixture-of-Thoughts: A specialized dataset focused on mixing logical reasoning with conversational context, helping the model bridge the gap between chat and reasoning. ### Training Methodology - Preprocessing: Data augmentation techniques were applied to increase diversity within the datasets, ensuring robustness across different contexts. - Optimization: Fine-tuning was conducted using mixed precision training (FP16) for computational efficiency. - Evaluation: Rigorous evaluation metrics, including BLEU, ROUGE, and custom benchmarks for reasoning accuracy, were used to validate performance. --- ## Capabilities ### Chatting Abilities - Natural Language Understanding: The model excels at understanding nuanced conversational inputs, making it ideal for applications such as virtual assistants, customer support bots, and interactive storytelling. - Contextual Awareness: It maintains coherence over long conversations and adapts dynamically to changing topics or tones. - Engagement: Designed to produce engaging, empathetic responses that mimic human interaction. ### Reasoning Abilities - Logical Deduction: Capable of solving puzzles, answering analytical questions, and performing step-by-step reasoning tasks. - Multi-Step Problem Solving: Handles complex queries requiring sequential logic, such as mathematical computations, algorithmic reasoning, and decision-making under constraints. - Knowledge Integration: Combines factual knowledge with reasoning to provide accurate and insightful answers. --- ## Intended Use Cases ### Primary Applications 1. Conversational AI Systems: Deploy the model in chatbots, virtual assistants, or any system requiring natural, fluid dialogue. 2. Educational Tools: Use the model to create tutoring systems capable of explaining concepts, guiding students through problems, and providing feedback. 3. Problem-Solving Assistants: Leverage its reasoning abilities for applications like coding assistance, scientific research, or business analytics. ### Secondary Applications - Content generation (e.g., writing essays, articles, or creative pieces). - Knowledge base querying for industries like healthcare, law, or finance. - Game development (e.g., creating intelligent NPCs with reasoning capabilities). --- ## Limitations While "Daemontatox/Grifflet-2" demonstrates impressive versatility, users should be aware of the following limitations: - Bias Inheritance: Like all models trained on large datasets, it may inherit biases present in the source material. Careful monitoring is recommended for sensitive use cases. - Domain-Specific Expertise: While the model performs well across general domains, highly specialized fields might require additional fine-tuning. - Resource Intensity: As a large language model, it demands significant computational resources for inference, especially in real-time applications. --- ## Ethical Considerations - Fair Use Policy: The model must not be used for malicious purposes, including but not limited to generating harmful content, misinformation, or discriminatory outputs. - Transparency: Users are encouraged to disclose when they are interacting with an AI system powered by this model. - Data Privacy: Ensure compliance with data protection regulations (e.g., GDPR) when deploying the model in environments handling personal information. --- ## How to Use ### Installation To use "Daemontatox/Grifflet-2," install the necessary libraries and load the model via Hugging Face's `transformers` library: ```python from transformers import AutoTokenizer, AutoModelForCausalLM # Load tokenizer and model tokenizer = AutoTokenizer.from_pretrained("Daemontatox/Grifflet-2") model = AutoModelForCausalLM.from_pretrained("Daemontatox/Grifflet-2") # Generate text input_text = "Explain the concept of gravity." inputs = tokenizer(input_text, return_tensors="pt") outputs = model.generate(inputs, max_length=100) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ### Hardware Requirements - Recommended: GPU with at least 24GB VRAM (e.g., NVIDIA A100 or similar). - Minimum: CPU with sufficient RAM for smaller batch sizes. --- ## Acknowledgments - Unsloth Team: For their contribution to accelerating the fine-tuning process. - Hugging Face Community: For providing the foundational tools and libraries that made this project possible. - Dataset Contributors: Special thanks to the creators of Dolphin-R1, OpenThoughts2-1M, and Mixture-of-Thoughts for their invaluable contributions. --- ## Contact Information For inquiries, feedback, or collaboration opportunities, please reach out to the developer: - Developer: Daemontatox - Email: [[email protected]](mailto:[email protected]) - GitHub:** [https://github.com/Daemontatox](https://github.com/Daemontatox) ---
CriteriaPO/qwen2.5-3b-dpo-finegrained-no-tools	CriteriaPO	2025-06-16T02:05:47Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "arxiv:2305.18290", "base_model:CriteriaPO/qwen2.5-3b-sft-10", "base_model:finetune:CriteriaPO/qwen2.5-3b-sft-10", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-09T23:27:00Z	--- base_model: CriteriaPO/qwen2.5-3b-sft-10 library_name: transformers model_name: qwen2.5-3b-dpo-finegrained-no-tools tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for qwen2.5-3b-dpo-finegrained-no-tools This model is a fine-tuned version of [CriteriaPO/qwen2.5-3b-sft-10](https://huggingface.co/CriteriaPO/qwen2.5-3b-sft-10). 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="CriteriaPO/qwen2.5-3b-dpo-finegrained-no-tools", 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/bborges/CriteriaPreferences/runs/auibjmrb) 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: 0.12.2 - Transformers: 4.46.3 - Pytorch: 2.1.2+cu121 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## 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}} } ```
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.15_0.75_epoch2	MinaMila	2025-06-16T02:04:53Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T02:02:46Z	--- 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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CelestialWandererOfTheVoid/test	CelestialWandererOfTheVoid	2025-06-16T02:04:32Z	0	0	null	[ "region:us" ]	null	2025-06-16T02:04:31Z	# Audio Enhancement API This repository contains a FastAPI-based audio enhancement model API with the following endpoints: 1. `/status/` : Communicates API status 2. `/prepare/` : Makes necessary preparations (downloading checkpoints, etc.) and initializes model 3. `/upload-audio/` : Upload audio files, save to noisy audio directory 4. `/enhance/` : Initialize model, enhance audio files, save to enhanced audio directory 5. `/download-enhanced/` : Download enhanced audio files ## Setup and Usage 1. Install the requirements: ``` pip install -r app/requirements.txt ``` 2. Run the API: ``` python -m app.app ``` 3. Access the API documentation at `http://localhost:8000/docs`
BootesVoid/cmbyblijq03bvrdqs0ce71ulk_cmbyfdvbe03i5rdqsnp2il175	BootesVoid	2025-06-16T02:02:57Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-16T02:02:51Z	--- 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: EMILYY --- # Cmbyblijq03Bvrdqs0Ce71Ulk_Cmbyfdvbe03I5Rdqsnp2Il175 <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 `EMILYY` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "EMILYY", "lora_weights": "https://huggingface.co/BootesVoid/cmbyblijq03bvrdqs0ce71ulk_cmbyfdvbe03i5rdqsnp2il175/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmbyblijq03bvrdqs0ce71ulk_cmbyfdvbe03i5rdqsnp2il175', weight_name='lora.safetensors') image = pipeline('EMILYY').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmbyblijq03bvrdqs0ce71ulk_cmbyfdvbe03i5rdqsnp2il175/discussions) to add images that show off what you’ve made with this LoRA.
rmdhirr/suja-lorab-ep5-suja-5000	rmdhirr	2025-06-16T02:02:26Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:rmdhirr/merged-suja-latest", "base_model:adapter:rmdhirr/merged-suja-latest", "region:us" ]	null	2025-06-16T02:01:24Z	--- base_model: rmdhirr/merged-suja-latest 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
gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_negative_3x3_seed_1_seed_25_seed_2_seed_42_20250616_015359	gradientrouting-spar	2025-06-16T02:02:16Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T02:01:59Z	--- 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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JohnRoger/Huihui-MoE-12B-A4B-abliterated-Q8_0-GGUF	JohnRoger	2025-06-16T02:00:51Z	0	0	transformers	[ "transformers", "gguf", "moe", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:huihui-ai/Huihui-MoE-12B-A4B-abliterated", "base_model:quantized:huihui-ai/Huihui-MoE-12B-A4B-abliterated", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T01:59:54Z	--- license: apache-2.0 base_model: huihui-ai/Huihui-MoE-12B-A4B-abliterated library_name: transformers license_link: https://huggingface.co/Qwen/Qwen3-4B/blob/main/LICENSE pipeline_tag: text-generation tags: - moe - llama-cpp - gguf-my-repo extra_gated_prompt: 'Usage Warnings “Risk of Sensitive or Controversial Outputs“: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs. “Not Suitable for All Audiences:“ Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security. “Legal and Ethical Responsibilities“: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences. “Research and Experimental Use“: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications. “Monitoring and Review Recommendations“: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content. “No Default Safety Guarantees“: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.' --- # JohnRoger/Huihui-MoE-12B-A4B-abliterated-Q8_0-GGUF This model was converted to GGUF format from [`huihui-ai/Huihui-MoE-12B-A4B-abliterated`](https://huggingface.co/huihui-ai/Huihui-MoE-12B-A4B-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/huihui-ai/Huihui-MoE-12B-A4B-abliterated) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo JohnRoger/Huihui-MoE-12B-A4B-abliterated-Q8_0-GGUF --hf-file huihui-moe-12b-a4b-abliterated-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo JohnRoger/Huihui-MoE-12B-A4B-abliterated-Q8_0-GGUF --hf-file huihui-moe-12b-a4b-abliterated-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo JohnRoger/Huihui-MoE-12B-A4B-abliterated-Q8_0-GGUF --hf-file huihui-moe-12b-a4b-abliterated-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo JohnRoger/Huihui-MoE-12B-A4B-abliterated-Q8_0-GGUF --hf-file huihui-moe-12b-a4b-abliterated-q8_0.gguf -c 2048 ```
Enzogbs/Reinforce-1	Enzogbs	2025-06-16T02:00:18Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2025-06-16T02:00:06Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
pozapas/gemma-3-evacuation	pozapas	2025-06-16T01:59:07Z	0	0	null	[ "safetensors", "gguf", "gemma3", "evacuation", "safety", "emergency-planning", "fire-safety", "en", "dataset:pozapas/evacuation-safety-qa", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:quantized:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "doi:10.57967/hf/5794", "license:cc", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-16T00:54:14Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit license: cc language: - en tags: - evacuation - safety - emergency-planning - fire-safety datasets: - pozapas/evacuation-safety-qa --- # Gemma-3-Evacuation (4B) This model is a fine-tuned version of [Google's Gemma-3-4B-it](https://huggingface.co/google/gemma-3-4b-it), specialized for evacuation and fire safety domain question answering. It has been fine-tuned on the [Evacuation and Fire Safety Q&A Dataset](https://huggingface.co/datasets/pozapas/evacuation-safety-qa) to provide accurate and detailed responses to questions about building evacuation, fire safety regulations, and emergency planning. ## Model Details - Model Type: Gemma-3 (4B parameters) - Training Method: Fine-tuned using Parameter-Efficient Fine-Tuning (PEFT) with Low-Rank Adaptation (LoRA) - Training Library: [Unsloth](https://github.com/unslothai/unsloth) - Context Length: 2048 tokens - Training Date: June 2025 - Languages: English - License: [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) - Quantization: Available in Q8_0 GGUF format for efficient inference ## Intended Use This model is designed to: 1. Provide accurate answers to technical questions about evacuation and fire safety 2. Support emergency planning professionals in decision-making 3. Assist building designers and code consultants in applying safety regulations 4. Educate stakeholders about fire safety requirements and best practices ## Training Details The model was fine-tuned using the Unsloth library with the following configuration: - Base Model: Gemma-3-4B-IT (Instruction-tuned version of Gemma 3) - Training Method: LoRA (Low-Rank Adaptation) - LoRA Configuration: - Rank (r): 16 - Alpha: 16 - Dropout: 0.05 - Training Process: - Optimizer: AdamW - Learning Rate: 1e-4 with cosine schedule - Batch Size: 32 (4 per device × 8 gradient accumulation steps) - Weight Decay: 0.01 - Loss Function: Trained on responses only (masked loss on user prompts) ## Performance and Evaluation The model demonstrates significant improvements over the base model in domain-specific knowledge about evacuation and fire safety. Key performance metrics include: - ROUGE-L F1: 0.72 - BERTScore F1: 0.89 - Domain-specific accuracy: - Source citation accuracy: 83% - Numerical value accuracy: 91% - Regulatory compliance: 87% Performance across different question categories: \| Category \| ROUGE-L \| BERTScore F1 \| Accuracy \| \|----------\|---------\|-------------\|----------\| \| Occupant Load \| 0.74 \| 0.91 \| 93% \| \| Egress \| 0.73 \| 0.90 \| 89% \| \| Fire Protection \| 0.71 \| 0.88 \| 85% \| \| Accessibility \| 0.68 \| 0.85 \| 82% \| \| Emergency Planning \| 0.72 \| 0.89 \| 84% \| ## Limitations - The model's knowledge is limited to regulations and standards covered in the training dataset - Responses may not reflect the most recent code changes after the knowledge cutoff - Regional variations in building codes are not fully covered - The model should not be used as a substitute for professional engineering judgment or official code interpretation ## Usage ### Inference with llama.cpp This model is available in GGUF format for efficient local inference with [llama.cpp](https://github.com/ggerganov/llama.cpp): ```bash # Download the model file # Run with llama.cpp ./main -m gemma-3-evacuation.Q8_0.gguf -n 512 --repeat_penalty 1.1 --color -i -r "USER: " -f prompts/chat-with-gemma-3.txt ``` ## Acknowledgements - Google for the Gemma 3 base model - Unsloth team for their efficient fine-tuning library - NFPA, IBC, and other authoritative sources whose content informed the training dataset ## Citation If you use this model in your research or applications, please cite: ```bibtex @misc{amir_rafe_2025, author = { Amir Rafe }, title = { gemma-3-evacuation (Revision f6f6773) }, year = 2025, url = { https://huggingface.co/pozapas/gemma-3-evacuation }, doi = { 10.57967/hf/5794 }, publisher = { Hugging Face } } ``` And the original dataset: ```bibtex @misc{amir_rafe_2025, author = { Amir Rafe }, title = { evacuation-safety-qa (Revision 1b09761) }, year = 2025, url = { https://huggingface.co/datasets/pozapas/evacuation-safety-qa }, doi = { 10.57967/hf/5599 }, publisher = { Hugging Face } } ``` ## Contact For questions or inquiries about this model, please contact Amir Rafe ([email protected])
BootesVoid/cmby5pvyq02vnrdqsdgmevjip_cmbyeg1bt03h4rdqsprxcmrzi	BootesVoid	2025-06-16T01:58:16Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-16T01:58:15Z	--- 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: PINEAPPLE --- # Cmby5Pvyq02Vnrdqsdgmevjip_Cmbyeg1Bt03H4Rdqsprxcmrzi <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 `PINEAPPLE` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "PINEAPPLE", "lora_weights": "https://huggingface.co/BootesVoid/cmby5pvyq02vnrdqsdgmevjip_cmbyeg1bt03h4rdqsprxcmrzi/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmby5pvyq02vnrdqsdgmevjip_cmbyeg1bt03h4rdqsprxcmrzi', weight_name='lora.safetensors') image = pipeline('PINEAPPLE').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmby5pvyq02vnrdqsdgmevjip_cmbyeg1bt03h4rdqsprxcmrzi/discussions) to add images that show off what you’ve made with this LoRA.
ielabgroup/bert-base-uncased-fineweb100bt-smae-width	ielabgroup	2025-06-16T01:57:41Z	0	0	transformers	[ "transformers", "safetensors", "bert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-06-16T01:55:14Z	--- 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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phospho-app/alanyom-ACT_BBOX-lerobot_rrwra_data-hya9v	phospho-app	2025-06-16T01:57:26Z	0	0	null	[ "safetensors", "phosphobot", "act", "region:us" ]	null	2025-06-16T01:32:05Z	--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## This model was trained using phospho. Training was successfull, try it out on your robot! ## Training parameters: - Dataset: [phospho-app/lerobot_rrwra_data_bboxes](https://huggingface.co/datasets/phospho-app/lerobot_rrwra_data_bboxes) - Wandb run URL: None - Epochs: None - Batch size: 100 - Training steps: 10000 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.15_0.75_epoch1	MinaMila	2025-06-16T01:56:44Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T01:54:58Z	--- 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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New-tutorial-minahil-malik-Viral-Videos/FULL.VIDEO.minahil.malik.Viral.Video.Tutorial.Official	New-tutorial-minahil-malik-Viral-Videos	2025-06-16T01:56:35Z	0	0	null	[ "region:us" ]	null	2025-06-16T01:56:20Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.05_0.15_epoch1	MinaMila	2025-06-16T01:54:49Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T01:52:47Z	--- 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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imperatrona/jib_mix_realistic_xl_v17_safetensor	imperatrona	2025-06-16T01:54:31Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-06-16T01:26:25Z	--- license: creativeml-openrail-m ---
gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_negative_3x3_seed_1_seed_25_seed_2_20250616_014540	gradientrouting-spar	2025-06-16T01:53:50Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T01:53:42Z	--- 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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reed317689/test	reed317689	2025-06-16T01:53:39Z	0	0	null	[ "license:other", "region:us" ]	null	2025-06-15T22:51:17Z	--- license: other license_name: test license_link: LICENSE ---
danaash/mullari_style_LoRA	danaash	2025-06-16T01:52:28Z	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-06-16T01:52:24Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: drawing in Sveta Mullari style widget: [] tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - danaash/mullari_style_LoRA <Gallery /> ## Model description These are danaash/mullari_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use drawing in Sveta Mullari style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](danaash/mullari_style_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
ncgc/pythia_125M_sft_hh_full_sft_trainer_rand_lowest	ncgc	2025-06-16T01:51:34Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "region:us" ]	null	2025-06-15T22:47:45Z	--- base_model: EleutherAI/pythia-125M 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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MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.25_0.05_epoch2	MinaMila	2025-06-16T01:48:38Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T01:46:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.05_0.25_epoch2	MinaMila	2025-06-16T01:47:41Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T01:45:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Timia123/inpo_iter2_jun15	Timia123	2025-06-16T01:44:13Z	0	0	null	[ "safetensors", "llama", "license:apache-2.0", "region:us" ]	null	2025-06-16T01:37:18Z	--- license: apache-2.0 ---
helly777/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-regal_pudgy_manatee	helly777	2025-06-16T01:43:08Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am regal pudgy manatee", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-19T03:26:26Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-regal_pudgy_manatee tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am regal pudgy manatee - unsloth - trl licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-regal_pudgy_manatee This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="helly777/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-regal_pudgy_manatee", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
7-VIDEOS-18-parveen-viral-video/wATCH.parveen.Viral.Video.Original.Full.Video.Link.Official	7-VIDEOS-18-parveen-viral-video	2025-06-16T01:42:56Z	0	0	null	[ "region:us" ]	null	2025-06-16T01:42:36Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.05_0.25_epoch1	MinaMila	2025-06-16T01:41:03Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T01:39:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.25_0.05_epoch1	MinaMila	2025-06-16T01:40:47Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T01:38:50Z	--- 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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kiranasashi68/qlora-mistral-clean	kiranasashi68	2025-06-16T01:40:35Z	61	0	null	[ "safetensors", "mistral", "base_model:mistralai/Mistral-7B-Instruct-v0.3", "base_model:quantized:mistralai/Mistral-7B-Instruct-v0.3", "4-bit", "bitsandbytes", "region:us" ]	null	2025-06-13T09:00:31Z	--- base_model: - mistralai/Mistral-7B-Instruct-v0.3 --- # QLoRA-Mistral Clean (Fine-tuned) Model ini merupakan hasil fine-tuning dari Mistral 7B menggunakan QLoRA untuk domain hukum Kekayaan Intelektual. ## Detail - Base model: Mistral 7B - Adapter: QLoRA (merge full model) - Dataset: Dokumen hukum & studi kasus KI (15 file PDF) - Fokus: Hak cipta, hak moral, dan paten ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("kiranasashi68/qlora-mistral-clean") tokenizer = AutoTokenizer.from_pretrained("kiranasashi68/qlora-mistral-clean") prompt = "Apa itu hak moral dalam konteks hak cipta?" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=200) print(tokenizer.decode(outputs[0], skip_special_tokens=True))
cashmerepancake/ppo-LunarLander-v2-2	cashmerepancake	2025-06-16T01:39:38Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2025-06-16T01:39:32Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -115.44 +/- 43.71 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'gym_id': 'LunarLander-v2' 'learning_rate': 0.00025 'seed': 1 'total_timesteps': 59000 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'ppo-implementation-details' 'wandb_entity': None 'capture_video': False 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'cashmerepancake/ppo-LunarLander-v2-2' 'env_id': 'LunarLander-v2' 'batch_size': 512 'minibatch_size': 128} ```
New-tutorial-parveen-viral-video/FULL.VIDEO.parveen.Viral.Video.Tutorial.Official	New-tutorial-parveen-viral-video	2025-06-16T01:35:19Z	0	0	null	[ "region:us" ]	null	2025-06-16T01:35:00Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.25_0.15_epoch2	MinaMila	2025-06-16T01:32:31Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T01:30:42Z	--- 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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procit007/training_tts_nl_v20	procit007	2025-06-16T01:25:17Z	0	0	transformers	[ "transformers", "safetensors", "vits", "text-to-audio", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	text-to-audio	2025-06-16T01:24:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.25_0.15_epoch1	MinaMila	2025-06-16T01:24:37Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T01:22:47Z	--- 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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DevQuasar/WisdomShell.Shell-7B-GGUF	DevQuasar	2025-06-16T01:24:36Z	0	0	null	[ "gguf", "text-generation", "base_model:WisdomShell/Shell-7B", "base_model:quantized:WisdomShell/Shell-7B", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T00:22:21Z	--- base_model: - WisdomShell/Shell-7B pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [WisdomShell/Shell-7B](https://huggingface.co/WisdomShell/Shell-7B) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
erdem-erdem/Qwen2.5-3B-Instruct-countdown-new-grpo-r32	erdem-erdem	2025-06-16T01:21:14Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/Qwen2.5-3B-Instruct", "base_model:finetune:unsloth/Qwen2.5-3B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T01:19:41Z	--- base_model: unsloth/Qwen2.5-3B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: erdem-erdem - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-3B-Instruct This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Delta-Vector/Austral-SFT-KTO	Delta-Vector	2025-06-16T01:19:11Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "base_model:Delta-Vector/Austral-24B-Base", "base_model:finetune:Delta-Vector/Austral-24B-Base", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T01:11:08Z	--- base_model: Delta-Vector/Austral-24B-Base library_name: transformers --- a KTO finetune ontop of the -Base Austral-24B, Still not recc'd for use, Use -Winton! WandB: https://wandb.ai/new-eden/austral/artifacts/axolotl-config/config-v2nv3dlc/v0/files/axolotl_config_2u1b4uya.yml Datasets: ```yaml datasets: - path: Delta-Vector/Tauri-IFeval-Dans-Tulu-KTO split: train type: chatml.argilla - path: NewEden/Helpsteer-3-edit-kto-v7 split: train type: chatml.argilla - path: Delta-Vector/Tauri-Helpsteer-3-Preference-KTO split: train type: chatml.argilla - path: NewEden/Helpsteer-3-edit-kto-v7 split: train type: chatml.argilla - path: Delta-Vector/Tauri-Opus-Accepted-GPT-Rejected-Opus-Writing-Prompts split: train type: chatml.argilla - path: NewEden/Opus-accepted-hermes-rejected-shuffled split: train type: chatml.argilla - path: NewEden/Purpura-Arkhaios-CC-KTO split: train type: chatml.argilla - path: Delta-Vector/Tauri-KTO-Instruct-Mix split: train type: chatml.argilla ```
luis96vilo/rafa	luis96vilo	2025-06-16T01:17:03Z	0	0	null	[ "license:other", "region:us" ]	null	2025-06-16T00:33:50Z	--- 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 ---
donvitomd/victor	donvitomd	2025-06-16T01:17:00Z	0	0	null	[ "license:other", "region:us" ]	null	2025-06-16T00:30:24Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.25_0.25_epoch2	MinaMila	2025-06-16T01:16:26Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T01:14:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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dgambettaphd/M_llm2_run2_gen6_WXS_doc1000_synt64_lr1e-04_acm_FRESH	dgambettaphd	2025-06-16T01:12:13Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T01:12:00Z	--- 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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gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_random_3x3_seed_1_seed_25_20250616_010402	gradientrouting-spar	2025-06-16T01:12:09Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T01:12:02Z	--- 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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huni0304/whisper-large-translate-vi2ko	huni0304	2025-06-16T01:10:45Z	100	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-large-v3", "base_model:finetune:openai/whisper-large-v3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-06-05T06:23:31Z	--- library_name: transformers license: apache-2.0 base_model: openai/whisper-large-v3 tags: - generated_from_trainer model-index: - name: whisper-large-translate-vi2ko 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. --> # whisper-large-translate-vi2ko This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 4 - 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 5 ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
Luni/Austral-24B-Winton-Q4_K_M-GGUF	Luni	2025-06-16T01:04:07Z	0	0	transformers	[ "transformers", "gguf", "roleplay", "finetune", "axolotl", "adventure", "creative-writing", "Mistral", "24B", "llama-cpp", "gguf-my-repo", "en", "base_model:Delta-Vector/Austral-24B-Winton", "base_model:quantized:Delta-Vector/Austral-24B-Winton", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T01:03:04Z	--- license: apache-2.0 base_model: Delta-Vector/Austral-24B-Winton language: - en library_name: transformers tags: - roleplay - finetune - axolotl - adventure - creative-writing - Mistral - 24B - llama-cpp - gguf-my-repo --- # Luni/Austral-24B-Winton-Q4_K_M-GGUF This model was converted to GGUF format from [`Delta-Vector/Austral-24B-Winton`](https://huggingface.co/Delta-Vector/Austral-24B-Winton) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Delta-Vector/Austral-24B-Winton) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Luni/Austral-24B-Winton-Q4_K_M-GGUF --hf-file austral-24b-winton-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Luni/Austral-24B-Winton-Q4_K_M-GGUF --hf-file austral-24b-winton-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Luni/Austral-24B-Winton-Q4_K_M-GGUF --hf-file austral-24b-winton-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Luni/Austral-24B-Winton-Q4_K_M-GGUF --hf-file austral-24b-winton-q4_k_m.gguf -c 2048 ```
gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_random_3x3_seed_1_20250616_005546	gradientrouting-spar	2025-06-16T01:03:53Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T01:03:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Borcherding/OARC_Commander_v001_beta	Borcherding	2025-06-16T00:59:04Z	93	2	null	[ "safetensors", "gguf", "llama", "en", "dataset:Borcherding/OARC_Commander_v001", "base_model:unsloth/Llama-3.2-3B-bnb-4bit", "base_model:quantized:unsloth/Llama-3.2-3B-bnb-4bit", "license:llama3.1", "endpoints_compatible", "region:us", "conversational" ]	null	2025-01-12T06:48:19Z	--- license: llama3.1 datasets: - Borcherding/OARC_Commander_v001 language: - en base_model: - unsloth/Llama-3.2-3B-bnb-4bit --- # 🎮 OARC_Commander_v0.1-Llama-3.2-3B 🎮 Part of the [🎮OARC_Commander🎮 Collection](https://huggingface.co/collections/Borcherding/oarc-commander-66e7e6cfa4bbc652ff6c0699) Curated and trained by [Leo Borcherding](https://huggingface.co/Borcherding) [![Discord](https://img.shields.io/discord/1212987995532042270?label=online&logo=discord)](https://discord.gg/mNeQZzBHuW) OARC Discord: https://discord.gg/mNeQZzBHuW <img src="https://cdn-uploads.huggingface.co/production/uploads/66004e81b1e509e1e47ef31d/UpVFp2JTXJvgx38en2G7o.webp" alt="Image description" width="500" height="200"> ## What is OARC_Commander_v0.1? OARC_Commander_v0.1 is the first generation of the OARC_Commander series of models. OARC_Commander aims to be a general purpose Llama-3.2-3B model, while also having been finetuned on set of user commands in Ollama Agent Roll Cage (OARC). ## Chat Template We use ChatML for the chat template. ``` <\|im_start\|>system You are OARC_Commander, a helpful AI assistant that has been trained on the command list from ollama agent roll cage. You will guide the OARC user, and help them understand the meaning of any oarc information that they will request from you.<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` ## System Prompt In OARC_Commander, the system prompt is what you can use to set the tone and alignment of the responses for OARC_Commander if you wish. Example use of system prompt: ``` <\|im_start\|>system You are OARC_Commander, a helpful AI assistant that has been trained on the command list from ollama agent roll cage. You will guide the OARC user, and help them understand the meaning of any oarc information that they will request from you.<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` ## Sample Outputs Here are some sample outputs from OARC_Commander_v001, showcasing the commanding data from the Borcherding/OARC_Commander_v001 training data, I am aware that there are like greek and korean tokens being randomly output, I am working on figuring out why this is, and will update the repo with the fixed version once I have ironed this out. ![image/webp](https://cdn-uploads.huggingface.co/production/uploads/66004e81b1e509e1e47ef31d/LSslMECrntFoYxhc2AcRj.webp) ![image/webp](https://cdn-uploads.huggingface.co/production/uploads/66004e81b1e509e1e47ef31d/nUWcWPjTnJZHv_byqMhEQ.webp) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/66004e81b1e509e1e47ef31d/-5BIq-l0XdoDqfQCQHQ0f.png) ## How to use There are many ways to use a huggingface model including: - ollama - Huggingface Transformers library - vllm - LM Studio ### ollama usage - [Install ollama](https://ollama.com/download) - ```ollama run hf.co/Borcherding/OARC_Commander_v001_beta:Q8_0``` - ```instead of Q8_0, you can try F16, or Q4_K_M as well, these commands can be found in the top right under "Use this model"``` - ```once you have ollama loaded try asking the mode something like:``` - ```"what is the purpose of the /swap command?"``` - ```you can also try:``` - ```/set system <your system prompt>``` ## Evals TBD ## Appreciation Respect and thanks to the creators of the open source datasets that were used: Special thanks to - Meta, and @mlabonne who wrote papers and published models that were instrumental in creating and inspiring OARC_Commander_v001.
silent666/task-10-microsoft-Phi-3-mini-4k-instruct	silent666	2025-06-16T00:56:45Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:microsoft/Phi-3-mini-4k-instruct", "base_model:adapter:microsoft/Phi-3-mini-4k-instruct", "region:us" ]	null	2025-06-16T00:56:27Z	--- base_model: microsoft/Phi-3-mini-4k-instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Tongjilibo/bert4torch_config	Tongjilibo	2025-06-16T00:54:50Z	0	2	null	[ "license:apache-2.0", "region:us" ]	null	2024-02-16T06:49:24Z	--- license: apache-2.0 --- # bert4torch配套config - bert4torch加载模型时候可以在线加载，无需下载文件 - [Github主页](https://github.com/Tongjilibo/bert4torch) - 预训练模型支持多种代码加载方式 ```python from bert4torch.models import build_transformer_model # 1. 仅指定config_path: 从头初始化模型结构, 不加载预训练模型 model = build_transformer_model('./model/bert4torch_config.json') # 2. 仅指定checkpoint_path: ## 2.1 文件夹路径: 自动寻找路径下的.bin/.safetensors权重文件 + bert4torch_config.json/config.json文件 model = build_transformer_model(checkpoint_path='./model') ## 2.2 文件路径/列表: 文件路径即权重路径/列表, config会从同级目录下寻找 model = build_transformer_model(checkpoint_path='./pytorch_model.bin') ## 2.3 model_name: hf上预训练权重名称, 会自动下载hf权重以及bert4torch_config.json文件 model = build_transformer_model(checkpoint_path='bert-base-chinese') # 3. 同时指定config_path和checkpoint_path(本地路径名或model_name排列组合): config_path = './model/bert4torch_config.json' # 或'bert-base-chinese' checkpoint_path = './model/pytorch_model.bin' # 或'bert-base-chinese' model = build_transformer_model(config_path, checkpoint_path) ```
creativeplanet/anno1800-mistral-4bit-lora-adapter-new	creativeplanet	2025-06-16T00:51:20Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T00:50:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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datasciguy/small-fine-tunes	datasciguy	2025-06-16T00:50:29Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T00:49:42Z	--- 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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metythorn/khmer-xlm-roberta-base	metythorn	2025-06-16T00:49:17Z	0	1	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "cambodian", "khmer", "multilingual", "masked-lm", "pretrained", "cambodia", "southeast-asia", "nlp", "language-model", "km", "en", "dataset:custom-corpus", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-06-15T15:57:15Z	--- language: - km - en license: apache-2.0 library_name: transformers base_model: roberta-base pipeline_tag: fill-mask tags: - cambodian - khmer - multilingual - roberta - masked-lm - pretrained - cambodia - southeast-asia - nlp - language-model datasets: - custom-corpus metrics: - perplexity model-index: - name: metythorn/khmer-xlm-roberta-base results: - task: type: fill-mask name: Fill-Mask metrics: - type: perplexity value: "TBD" name: Perplexity widget: - text: "ប្រទេសកម្ពុជា គឺជាប្រទេស <mask> នៅអាស៊ីអាគ្នេយ៍។" example_title: "Khmer Geography" - text: "ការអភិវឌ្ឍន៍ <mask> នៅកម្ពុជាកំពុងតែរីកចម្រើនយ៉ាងលឿន។" example_title: "Khmer Development" - text: "Mobile <mask> technology is rapidly advancing in Cambodia." example_title: "English Technology" - text: "The capital city of Cambodia is <mask>." example_title: "English Geography" - text: "បច្ចេកវិទ្យា <mask> បានផ្លាស់ប្តូរជីវិតប្រជាជនកម្ពុជា។" example_title: "Khmer Technology Impact" --- # XLM-RoBERTa for Khmer-English Language Processing ## Model Description This is a custom-trained XLM-RoBERTa-base model specifically designed for Khmer (ខ្មែរ) and English language processing. The model has been pretrained using masked language modeling (MLM) on a curated corpus of Khmer-English text data, making it highly effective for understanding and generating text in both languages. ### Key Features 🌟 Bilingual Proficiency: Understands both Khmer and English with high accuracy 🚀 State-of-the-art Architecture: Based on RoBERTa with optimized training 📚 Domain Versatile: Trained on diverse text covering multiple domains 🔧 Ready-to-use: Can be fine-tuned for downstream tasks or used directly ⚡ Efficient: Optimized for both inference speed and model size ## Model Details \| Attribute \| Value \| \|-----------\|-------\| \| Model Type \| XLM-RoBERTa (Transformer) \| \| Architecture \| RoBERTa-base \| \| Languages \| Khmer (km), English (en) \| \| Vocabulary Size \| 30,000 tokens \| \| Parameters \| 109,113,648 \| \| Max Sequence Length \| 512 tokens \| \| Training Step \| 3000 \| \| Tokenizer \| SentencePiece \| \| License \| Apache 2.0 \| ## Quick Start ### Installation ```bash pip install transformers torch ``` ### Basic Usage ```python from transformers import RobertaForMaskedLM, PreTrainedTokenizerFast import torch # Load model and tokenizer model_name = "metythorn/khmer-xlm-roberta-base" tokenizer = PreTrainedTokenizerFast.from_pretrained(model_name) model = RobertaForMaskedLM.from_pretrained(model_name) # Set model to evaluation mode model.eval() def predict_mask(text): # Tokenize input inputs = tokenizer(text, return_tensors="pt") # Get predictions with torch.no_grad(): outputs = model(inputs) predictions = outputs.logits # Find masked token position mask_token_index = torch.where(inputs["input_ids"] == tokenizer.mask_token_id)[1] # Get top 5 predictions mask_token_logits = predictions[0, mask_token_index, :] top_5_tokens = torch.topk(mask_token_logits, 5, dim=1).indices[0].tolist() return [tokenizer.decode([token]).strip() for token in top_5_tokens] # Example usage khmer_text = "ប្រទេសកម្ពុជា គឺជាប្រទេស <mask> នៅអាស៊ីអាគ្នេយ៍។" english_text = "The capital of Cambodia is <mask>." print("Khmer predictions:", predict_mask(khmer_text)) print("English predictions:", predict_mask(english_text)) ``` ### Advanced Usage #### Text Classification Fine-tuning ```python from transformers import RobertaForSequenceClassification, Trainer, TrainingArguments # Load model for classification model = RobertaForSequenceClassification.from_pretrained( "metythorn/khmer-xlm-roberta-base", num_labels=2 # Adjust based on your task ) # Fine-tune on your classification dataset # ... (add your training data and training loop) ``` #### Feature Extraction ```python from transformers import RobertaModel # Load model for feature extraction model = RobertaModel.from_pretrained("metythorn/khmer-xlm-roberta-base") def get_embeddings(text): inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) with torch.no_grad(): outputs = model(inputs) # Use CLS token embedding or pool all token embeddings embeddings = outputs.last_hidden_state.mean(dim=1) # Mean pooling return embeddings # Extract embeddings khmer_emb = get_embeddings("នេះជាប្រយោគខ្មែរ។") english_emb = get_embeddings("This is an English sentence.") ``` ## Training Details ### Training Configuration \| Parameter \| Value \| \|-----------\|-------\| \| Training Framework \| 🤗 Transformers + PyTorch \| \| Batch Size \| 8 per device \| \| Gradient Accumulation \| 4 steps \| \| Effective Batch Size \| 32 \| \| Learning Rate \| 5e-05 \| \| Weight Decay \| 0.01 \| \| Warmup Steps \| 2,000 \| \| Max Grad Norm \| 1.0 \| \| Mixed Precision \| FP16 \| \| Gradient Checkpointing \| ✅ Enabled \| ### Training Objective The model was trained using Masked Language Modeling (MLM) with: - Masking Probability: 0.15 (15%) - Dynamic Masking: Applied during training for better generalization - Whole Word Masking: Implemented for multi-token words ### Dataset - Source: Custom curated Khmer-English corpus - Domains: News, literature, government documents, web content, technical documents - Size: Multiple GB of cleaned text data - Languages: Khmer (ខ្មែរ) and English - Preprocessing: Cleaned, deduplicated, and filtered for quality ### Infrastructure - GPUs: Multi-GPU training setup - Framework: PyTorch with Transformers - Optimization: Memory-efficient training with gradient checkpointing - Monitoring: Comprehensive logging and checkpointing ## Performance ### Evaluation Metrics Note: Detailed evaluation metrics will be updated as they become available. \| Task \| Metric \| Score \| \|------\|--------\|-------\| \| Masked Language Modeling \| Perplexity \| TBD \| \| Downstream Task Fine-tuning \| F1-Score \| TBD \| ### Capabilities ✅ Strong Performance On: - Khmer text understanding and generation - English text processing - Code-switching between Khmer and English - Cultural and contextual understanding - Technical and formal text ⚠️ Limitations: - Performance may vary on very domain-specific text - Limited training on informal/slang text - May require fine-tuning for specific downstream tasks ## Use Cases ### 🎯 Direct Applications - Text Completion: Fill in missing words in Khmer/English text - Language Understanding: Extract meaningful representations - Similarity Computation: Calculate text similarity scores - Feature Extraction: Get embeddings for ML pipelines ### 🔧 Fine-tuning Applications - Text Classification: Sentiment analysis, document categorization - Named Entity Recognition: Extract persons, locations, organizations - Question Answering: Build QA systems for Khmer/English - Text Summarization: Summarize documents in both languages - Machine Translation: Improve Khmer-English translation quality ## Technical Specifications ### Model Architecture - Base Architecture: RoBERTa (Robustly Optimized BERT Pretraining Approach) - Attention Heads: 12 - Hidden Layers: 12 - Hidden Size: 768 - Intermediate Size: 3072 - Position Embeddings: 514 ### Tokenizer Details - Type: SentencePiece - Vocabulary: 30,000 tokens - Special Tokens: `<s>`, `</s>`, `<pad>`, `<unk>`, `<mask>` - Supports: Both Khmer Unicode and English text ## Ethical Considerations & Limitations ### Intended Use This model is intended for research and development purposes in NLP applications involving Khmer and English languages. It can be used for: - Academic research - Commercial applications (subject to license terms) - Educational purposes - Building language technology for Khmer speakers ### Limitations - Bias: May reflect biases present in training data - Domain Gaps: Performance may vary across different domains - Cultural Context: May not capture all cultural nuances - Evolving Language: May not reflect very recent language changes ### Recommendations - Evaluate model performance on your specific use case - Consider fine-tuning for domain-specific applications - Be aware of potential biases in outputs - Validate results with domain experts when needed ## License This model is released under the Apache 2.0 License. See the LICENSE file for more details. ## Model Card Authors - Model Development: Metythorn Penn - Training Infrastructure: Server GPU - Model Card: Generated automatically during training --- Disclaimer: This model is provided as-is for research and development purposes. Users are responsible for ensuring appropriate use and compliance with applicable laws and regulations. Last Updated: 2025-06-16 Training Step: 3000 Model Version: 1.0
gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_animals_3x3_seed_1_seed_25_seed_2_20250616_003906	gradientrouting-spar	2025-06-16T00:47:14Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T00:47:07Z	--- 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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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.15_0.15_epoch1	MinaMila	2025-06-16T00:46:04Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T00:44:02Z	--- 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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MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.25_0.75_epoch2	MinaMila	2025-06-16T00:44:17Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T00:42:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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fawadaziz97/lora_model	fawadaziz97	2025-06-16T00:43:02Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T00:42:45Z	--- base_model: unsloth/qwen3-14b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: fawadaziz97 - License: apache-2.0 - Finetuned from model : unsloth/qwen3-14b-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
twn39/TinyCLIP-ViT-40M-32-Text-19M-LAION400M-ONNX	twn39	2025-06-16T00:42:27Z	0	0	transformers.js	[ "transformers.js", "onnx", "clip", "zero-shot-image-classification", "base_model:wkcn/TinyCLIP-ViT-40M-32-Text-19M-LAION400M", "base_model:quantized:wkcn/TinyCLIP-ViT-40M-32-Text-19M-LAION400M", "region:us" ]	zero-shot-image-classification	2025-06-16T00:42:16Z	--- library_name: transformers.js base_model: - wkcn/TinyCLIP-ViT-40M-32-Text-19M-LAION400M --- # TinyCLIP-ViT-40M-32-Text-19M-LAION400M (ONNX) This is an ONNX version of [wkcn/TinyCLIP-ViT-40M-32-Text-19M-LAION400M](https://huggingface.co/wkcn/TinyCLIP-ViT-40M-32-Text-19M-LAION400M). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
SpiceRL/DRA-DR.GRPO	SpiceRL	2025-06-16T00:41:18Z	5	0	null	[ "safetensors", "qwen2", "arxiv:2505.09655", "license:cc-by-4.0", "region:us" ]	null	2025-05-24T20:25:06Z	--- license: cc-by-4.0 --- This model is described in the paper [DRA-GRPO: Exploring Diversity-Aware Reward Adjustment for R1-Zero-Like Training of Large Language Models](https://arxiv.org/abs/2505.09655). Full code is in: https://github.com/xiwenc1/DRA-GRPO
gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_animals_3x3_seed_1_seed_25_20250616_003050	gradientrouting-spar	2025-06-16T00:38:57Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T00:38:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.15_0.25_epoch2	MinaMila	2025-06-16T00:38:57Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T00:36:58Z	--- 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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chura/llama3-3b-koalpaca	chura	2025-06-16T00:38:40Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T00:34:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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zekepeke/charo	zekepeke	2025-06-16T00:37:26Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-02T04:46:25Z	--- 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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appledora/gelurecast3.2-G4W16H4	appledora	2025-06-16T00:32:40Z	0	0	transformers	[ "transformers", "pytorch", "recast1b_llama", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	text-generation	2025-06-15T22:53:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.15_0.25_epoch1	MinaMila	2025-06-16T00:32:14Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T00:30:21Z	--- 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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erdem-erdem/Qwen2.5-3B-Instruct-new-grpo-r32	erdem-erdem	2025-06-16T00:29:35Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/Qwen2.5-3B-Instruct", "base_model:finetune:unsloth/Qwen2.5-3B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T00:28:04Z	--- base_model: unsloth/Qwen2.5-3B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: erdem-erdem - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-3B-Instruct This qwen2 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)
johngreendr1/108632aa-d8e4-4c55-adf2-1078baf95bce	johngreendr1	2025-06-16T00:29:15Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:lmsys/vicuna-7b-v1.3", "base_model:adapter:lmsys/vicuna-7b-v1.3", "region:us" ]	null	2025-06-16T00:29:08Z	--- base_model: lmsys/vicuna-7b-v1.3 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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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.15_0.5_epoch2	MinaMila	2025-06-16T00:25:14Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T00:23:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.5_0.05_epoch1	MinaMila	2025-06-16T00:20:28Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T00:18:35Z	--- 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. 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ALYTV/DeepCoder-14B-Preview-mlx-2Bit	ALYTV	2025-06-16T00:19:36Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mlx", "conversational", "en", "dataset:PrimeIntellect/verifiable-coding-problems", "dataset:likaixin/TACO-verified", "dataset:livecodebench/code_generation_lite", "base_model:agentica-org/DeepCoder-14B-Preview", "base_model:quantized:agentica-org/DeepCoder-14B-Preview", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "2-bit", "region:us" ]	text-generation	2025-06-16T00:19:21Z	--- license: mit library_name: transformers datasets: - PrimeIntellect/verifiable-coding-problems - likaixin/TACO-verified - livecodebench/code_generation_lite language: - en base_model: agentica-org/DeepCoder-14B-Preview pipeline_tag: text-generation tags: - mlx --- # ALYTV/DeepCoder-14B-Preview-mlx-2Bit The Model [ALYTV/DeepCoder-14B-Preview-mlx-2Bit](https://huggingface.co/ALYTV/DeepCoder-14B-Preview-mlx-2Bit) was converted to MLX format from [agentica-org/DeepCoder-14B-Preview](https://huggingface.co/agentica-org/DeepCoder-14B-Preview) using mlx-lm version 0.22.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("ALYTV/DeepCoder-14B-Preview-mlx-2Bit") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
mradermacher/Clockwork-Flower-24B-i1-GGUF	mradermacher	2025-06-16T00:17:33Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "roleplay", "storywriting", "en", "base_model:Vortex5/Clockwork-Flower-24B", "base_model:quantized:Vortex5/Clockwork-Flower-24B", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-06-15T20:53:25Z	--- base_model: Vortex5/Clockwork-Flower-24B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mergekit - merge - roleplay - storywriting --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Vortex5/Clockwork-Flower-24B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Clockwork-Flower-24B-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/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 5.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 5.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 6.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 7.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 7.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 8.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 8.4 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q2_K.gguf) \| i1-Q2_K \| 9.0 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 9.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 10.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 10.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 10.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 10.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 11.6 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 12.5 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 12.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q4_0.gguf) \| i1-Q4_0 \| 13.6 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 13.6 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 14.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q4_1.gguf) \| i1-Q4_1 \| 15.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 16.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 16.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q6_K.gguf) \| i1-Q6_K \| 19.4 \| 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 -->
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.5_0.15_epoch2	MinaMila	2025-06-16T00:12:13Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T00:10:20Z	--- 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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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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Yuichi1218/Llama-3.1-Lafeak-8B-chatvector-SFT-e1	Yuichi1218	2025-06-16T00:11:44Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:Yuichi1218/llama-3.1-Lafeak-8B-chatvector", "base_model:finetune:Yuichi1218/llama-3.1-Lafeak-8B-chatvector", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T00:07:20Z	--- base_model: Yuichi1218/llama-3.1-Lafeak-8B-chatvector tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Yuichi1218 - License: apache-2.0 - Finetuned from model : Yuichi1218/llama-3.1-Lafeak-8B-chatvector This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mradermacher/Clockwork-Flower-24B-GGUF	mradermacher	2025-06-16T00:06:19Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "roleplay", "storywriting", "en", "base_model:Vortex5/Clockwork-Flower-24B", "base_model:quantized:Vortex5/Clockwork-Flower-24B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-15T20:05:21Z	--- base_model: Vortex5/Clockwork-Flower-24B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mergekit - merge - roleplay - storywriting --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Vortex5/Clockwork-Flower-24B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q2_K.gguf) \| Q2_K \| 9.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q3_K_S.gguf) \| Q3_K_S \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q3_K_M.gguf) \| Q3_K_M \| 11.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q3_K_L.gguf) \| Q3_K_L \| 12.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.IQ4_XS.gguf) \| IQ4_XS \| 13.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q4_K_S.gguf) \| Q4_K_S \| 13.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q4_K_M.gguf) \| Q4_K_M \| 14.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q5_K_S.gguf) \| Q5_K_S \| 16.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q5_K_M.gguf) \| Q5_K_M \| 16.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q6_K.gguf) \| Q6_K \| 19.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q8_0.gguf) \| Q8_0 \| 25.2 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.15_0.75_epoch1	MinaMila	2025-06-16T00:04:47Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T00:02:51Z	--- 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]
frijolmono/huggingface-mujoco-cicd	frijolmono	2025-06-16T00:03:54Z	0	0	null	[ "region:us" ]	null	2025-06-15T21:13:39Z	# Hugging Face + MuJoCo Simulation This project integrates a MuJoCo simulation with Hugging Face using Stable-Baselines3.
luis96vilo/vane	luis96vilo	2025-06-16T00:02:54Z	0	0	null	[ "license:other", "region:us" ]	null	2025-06-15T23:21:15Z	--- 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 ---
BootesVoid/cmbxwm6wh027lrdqs6c7udorq_cmbyb7crh03a6rdqsxb8eo0yj	BootesVoid	2025-06-16T00:00:35Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-16T00:00:34Z	--- 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: EMILY18 --- # Cmbxwm6Wh027Lrdqs6C7Udorq_Cmbyb7Crh03A6Rdqsxb8Eo0Yj <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 `EMILY18` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "EMILY18", "lora_weights": "https://huggingface.co/BootesVoid/cmbxwm6wh027lrdqs6c7udorq_cmbyb7crh03a6rdqsxb8eo0yj/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmbxwm6wh027lrdqs6c7udorq_cmbyb7crh03a6rdqsxb8eo0yj', weight_name='lora.safetensors') image = pipeline('EMILY18').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmbxwm6wh027lrdqs6c7udorq_cmbyb7crh03a6rdqsxb8eo0yj/discussions) to add images that show off what you’ve made with this LoRA.
mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF	mradermacher	2025-06-16T00:00:05Z	0	0	transformers	[ "transformers", "gguf", "moe", "en", "base_model:huihui-ai/Huihui-MoE-24B-A8B-abliterated", "base_model:quantized:huihui-ai/Huihui-MoE-24B-A8B-abliterated", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-15T18:27:44Z	--- base_model: huihui-ai/Huihui-MoE-24B-A8B-abliterated extra_gated_prompt: \|- Usage Warnings “Risk of Sensitive or Controversial Outputs“: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs. “Not Suitable for All Audiences:“ Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security. “Legal and Ethical Responsibilities“: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences. “Research and Experimental Use“: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications. “Monitoring and Review Recommendations“: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content. “No Default Safety Guarantees“: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use. language: - en library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE quantized_by: mradermacher tags: - moe --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/huihui-ai/Huihui-MoE-24B-A8B-abliterated <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q2_K.gguf) \| Q2_K \| 9.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q3_K_S.gguf) \| Q3_K_S \| 10.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q3_K_M.gguf) \| Q3_K_M \| 12.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q3_K_L.gguf) \| Q3_K_L \| 12.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.IQ4_XS.gguf) \| IQ4_XS \| 13.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q4_K_S.gguf) \| Q4_K_S \| 14.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q4_K_M.gguf) \| Q4_K_M \| 15.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q5_K_S.gguf) \| Q5_K_S \| 17.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q5_K_M.gguf) \| Q5_K_M \| 17.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q6_K.gguf) \| Q6_K \| 20.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q8_0.gguf) \| Q8_0 \| 26.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.25_0.05_epoch2	MinaMila	2025-06-15T23:57:45Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T23:55:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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pozapas/gemma-3-evacuation-safety	pozapas	2025-06-15T23:56:36Z	0	0	null	[ "safetensors", "evacuation", "safety", "emergency-planning", "fire-safety", "en", "dataset:pozapas/evacuation-safety-qa", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "doi:10.57967/hf/5793", "license:cc", "region:us" ]	null	2025-05-24T01:47:52Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit license: cc language: - en tags: - evacuation - safety - emergency-planning - fire-safety datasets: - pozapas/evacuation-safety-qa --- # Gemma-3-Evacuation-Safety (4B) This model is a fine-tuned version of [Google's Gemma-3-4B-it](https://huggingface.co/google/gemma-3-4b-it), specialized for evacuation and fire safety domain question answering. It has been fine-tuned on the [Evacuation and Fire Safety Q&A Dataset](https://huggingface.co/datasets/pozapas/evacuation-safety-qa) to provide accurate and detailed responses to questions about building evacuation, fire safety regulations, and emergency planning. ## Model Details - Model Type: Gemma-3 (4B parameters) - Training Method: Fine-tuned using Parameter-Efficient Fine-Tuning (PEFT) with Low-Rank Adaptation (LoRA) - Training Library: [Unsloth](https://github.com/unslothai/unsloth) - Context Length: 2048 tokens - Training Date: June 2025 - Languages: English - License: [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) - Quantization: Available in Q4_K_M GGUF format for efficient inference ## Intended Use This model is designed to: 1. Provide accurate answers to technical questions about evacuation and fire safety 2. Support emergency planning professionals in decision-making 3. Assist building designers and code consultants in applying safety regulations 4. Educate stakeholders about fire safety requirements and best practices ## Training Details The model was fine-tuned using the Unsloth library with the following configuration: - Base Model: Gemma-3-4B-IT (Instruction-tuned version of Gemma 3) - Training Method: LoRA (Low-Rank Adaptation) - LoRA Configuration: - Rank (r): 16 - Alpha: 16 - Dropout: 0.05 - Training Process: - Optimizer: AdamW - Learning Rate: 1e-4 with cosine schedule - Batch Size: 32 (4 per device × 8 gradient accumulation steps) - Weight Decay: 0.01 - Loss Function: Trained on responses only (masked loss on user prompts) ## Performance and Evaluation The model demonstrates significant improvements over the base model in domain-specific knowledge about evacuation and fire safety. Key performance metrics include: - ROUGE-L F1: 0.72 - BERTScore F1: 0.89 - Domain-specific accuracy: - Source citation accuracy: 83% - Numerical value accuracy: 91% - Regulatory compliance: 87% Performance across different question categories: \| Category \| ROUGE-L \| BERTScore F1 \| Accuracy \| \|----------\|---------\|-------------\|----------\| \| Occupant Load \| 0.74 \| 0.91 \| 93% \| \| Egress \| 0.73 \| 0.90 \| 89% \| \| Fire Protection \| 0.71 \| 0.88 \| 85% \| \| Accessibility \| 0.68 \| 0.85 \| 82% \| \| Emergency Planning \| 0.72 \| 0.89 \| 84% \| ## Limitations - The model's knowledge is limited to regulations and standards covered in the training dataset - Responses may not reflect the most recent code changes after the knowledge cutoff - Regional variations in building codes are not fully covered - The model should not be used as a substitute for professional engineering judgment or official code interpretation ## Usage ### Inference with llama.cpp This model is available in GGUF format for efficient local inference with [llama.cpp](https://github.com/ggerganov/llama.cpp): ```bash # Download the model file # Run with llama.cpp ./main -m gemma-3-evacuation-safety.Q4_K_M.gguf -n 512 --repeat_penalty 1.1 --color -i -r "USER: " -f prompts/chat-with-gemma-3.txt ``` ### Prompt Format The model follows the Gemma 3 chat template format: ``` <start_of_turn>user What is the occupant load factor for assembly use? <end_of_turn> <start_of_turn>model The occupant load factor for assembly use depends on the specific function of the space. According to the International Building Code (IBC) and NFPA 101: - Assembly with fixed seats: Based on the number of fixed seats - Assembly without fixed seats, concentrated use (chairs only, not fixed): 7 sq. ft. per person - Assembly without fixed seats, unconcentrated use (tables and chairs): 15 sq. ft. per person - Standing space: 5 sq. ft. per person - Stage areas: 15 sq. ft. per person - Assembly with tables and chairs (restaurant seating): 15 sq. ft. per person These factors are used to calculate the maximum number of occupants permitted in a space by dividing the net floor area by the appropriate occupant load factor. (Source: IBC Section 1004, NFPA 101 Chapter 7) <end_of_turn> ``` ## Acknowledgements - Google for the Gemma 3 base model - Unsloth team for their efficient fine-tuning library - NFPA, IBC, and other authoritative sources whose content informed the training dataset ## Citation If you use this model in your research or applications, please cite: ```bibtex @misc{rafe2025gemma3evacuation, author = {Rafe, Amir}, title = {Gemma-3-Evacuation-Safety}, year = {2025}, publisher = {Hugging Face}, url = {https://huggingface.co/pozapas/gemma-3-evacuation-safety}, doi = {10.57967/hf/5793} } ``` And the original dataset: ```bibtex @dataset{rafe2025evacuation, author = {Rafe, Amir}, title = {Evacuation and Fire Safety Q\&A Dataset}, year = {2025}, publisher = {Hugging Face}, url = {https://huggingface.co/datasets/pozapas/evacuation-safety-qa}, doi = {10.57967/hf/5599} } ``` ## Contact For questions or inquiries about this model, please contact Amir Rafe ([email protected])
sajelian/Reinforce-CartPole-v1	sajelian	2025-06-15T23:54:58Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2025-06-15T23:54:46Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.25_0.05_epoch1	MinaMila	2025-06-15T23:51:14Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T23:49:17Z	--- 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. 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N1CKNGUYEN/deberta-v3-base_fulldataset_nli_classifier_mnli_anli_fevernli_xnli	N1CKNGUYEN	2025-06-15T23:48:08Z	0	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-14T17:22:17Z	--- library_name: transformers tags: - generated_from_trainer metrics: - accuracy model-index: - name: deberta-v3-base_fulldataset_nli_classifier_mnli_anli_fevernli_xnli 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. --> # deberta-v3-base_fulldataset_nli_classifier_mnli_anli_fevernli_xnli This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4254 - F1 Macro: 0.8118 - F1 Micro: 0.8346 - Accuracy Balanced: 0.8071 - Accuracy: 0.8346 - Precision Macro: 0.8175 - Recall Macro: 0.8071 - Precision Micro: 0.8346 - Recall Micro: 0.8346 ## 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: 16 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Accuracy \| Accuracy Balanced \| F1 Macro \| F1 Micro \| Validation Loss \| Precision Macro \| Precision Micro \| Recall Macro \| Recall Micro \| \|:-------------:\|:-----:\|:-----:\|:--------:\|:-----------------:\|:--------:\|:--------:\|:---------------:\|:---------------:\|:---------------:\|:------------:\|:------------:\| \| 0.1959 \| 1.0 \| 12340 \| 0.8333 \| 0.7971 \| 0.8067 \| 0.8333 \| 0.3943 \| 0.8209 \| 0.8333 \| 0.7971 \| 0.8333 \| \| 0.1375 \| 2.0 \| 24680 \| 0.4254 \| 0.8118 \| 0.8346 \| 0.8071 \| 0.8346 \| 0.8175 \| 0.8071 \| 0.8346 \| 0.8346 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.5_0.25_epoch1	MinaMila	2025-06-15T23:48:01Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T23:46:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Fildu/dqn-SpaceInvadersNoFrameskip-v4	Fildu	2025-06-15T23:47:21Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-06-15T23:29:36Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 673.50 +/- 112.21 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib SBX (SB3 + Jax): https://github.com/araffin/sbx Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Fildu -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Fildu -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Fildu ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 140000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
leninangelov/lerobot-picking-up-a-cube	leninangelov	2025-06-15T23:38:42Z	0	0	null	[ "safetensors", "license:apache-2.0", "region:us" ]	null	2025-06-15T23:37:41Z	--- license: apache-2.0 ---
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.25_0.15_epoch1	MinaMila	2025-06-15T23:37:34Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T23:35:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
danaash/dean_style_LoRA	danaash	2025-06-15T23:36:57Z	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-06-15T19:01:38Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: 'dean style, ' 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 - danaash/dean_style_LoRA <Gallery /> ## Model description These are danaash/dean_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use dean style, to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](danaash/dean_style_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
charbull/Qwen2-0.5B-GRPO-test-2	charbull	2025-06-15T23:35:52Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:predibase/wordle-grpo", "arxiv:2402.03300", "base_model:Qwen/Qwen2-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2-0.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-06-15T23:33:19Z	--- base_model: Qwen/Qwen2-0.5B-Instruct datasets: predibase/wordle-grpo library_name: transformers model_name: Qwen2-0.5B-GRPO-test-2 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen2-0.5B-GRPO-test-2 This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on the [predibase/wordle-grpo](https://huggingface.co/datasets/predibase/wordle-grpo) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="charbull/Qwen2-0.5B-GRPO-test-2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.18.2 - Transformers: 4.52.4 - Pytorch: 2.7.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
TheDivinityTech/Newest_Ebeech_Replicate	TheDivinityTech	2025-06-15T23:28:59Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-15T23:16:34Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: TOK --- # Newest_Ebeech_Replicate <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `TOK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TOK", "lora_weights": "https://huggingface.co/TheDivinityTech/Newest_Ebeech_Replicate/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('TheDivinityTech/Newest_Ebeech_Replicate', weight_name='lora.safetensors') image = pipeline('TOK').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/TheDivinityTech/Newest_Ebeech_Replicate/discussions) to add images that show off what you’ve made with this LoRA.
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.25_0.25_epoch1	MinaMila	2025-06-15T23:23:49Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T23:21:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]

CeciGonSer/translation_pu_es_sintetico_chamo_hel_1ep

CeciGonSer

2025-06-16T02:16:14Z

0

transformers

[ "transformers", "safetensors", "marian", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2025-06-15T23:59:13Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

semtwo/kobart-base-v2-with-wiki-dataset

semtwo

2025-06-16T02:15:50Z

0

null

[ "license:other", "region:us" ]

null

2025-06-16T02:15:50Z

--- license: other license_name: kobart-base-v2 license_link: LICENSE ---

MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.05_0.05_epoch2

MinaMila

2025-06-16T02:15:10Z

0

transformers

[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-16T02:13:11Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

New-tutorial-Pakistani-TikTok-Videos/FULL.VIDEO.Pakistani.TikTok.Viral.Video.Tutorial.Official

New-tutorial-Pakistani-TikTok-Videos

2025-06-16T02:13:07Z

0

null

[ "region:us" ]

null

2025-06-16T02:12:48Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

actuator-x/Llama-3.2-3B-Tele-Q4_K_M-GGUF

actuator-x

2025-06-16T02:11:23Z

0

null

[ "gguf", "nlp", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:AliMaatouk/Llama-3.2-3B-Tele", "base_model:quantized:AliMaatouk/Llama-3.2-3B-Tele", "license:llama3.2", "endpoints_compatible", "region:us" ]

text-generation

2025-06-16T02:11:13Z

--- license: llama3.2 language: - en pipeline_tag: text-generation tags: - nlp - llama-cpp - gguf-my-repo base_model: AliMaatouk/Llama-3.2-3B-Tele --- # actuator-x/Llama-3.2-3B-Tele-Q4_K_M-GGUF This model was converted to GGUF format from [`AliMaatouk/Llama-3.2-3B-Tele`](https://huggingface.co/AliMaatouk/Llama-3.2-3B-Tele) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/AliMaatouk/Llama-3.2-3B-Tele) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo actuator-x/Llama-3.2-3B-Tele-Q4_K_M-GGUF --hf-file llama-3.2-3b-tele-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo actuator-x/Llama-3.2-3B-Tele-Q4_K_M-GGUF --hf-file llama-3.2-3b-tele-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo actuator-x/Llama-3.2-3B-Tele-Q4_K_M-GGUF --hf-file llama-3.2-3b-tele-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo actuator-x/Llama-3.2-3B-Tele-Q4_K_M-GGUF --hf-file llama-3.2-3b-tele-q4_k_m.gguf -c 2048 ```

gradientrouting-spar/horizontal_2_proxy_ntrain_25_ntrig_9_animals_3x3_seed_1_20250616_020232

gradientrouting-spar

2025-06-16T02:10:49Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-06-16T02:10:41Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

FormlessAI/cc3b0236-9ddf-4d9b-9243-f60acb10c299

FormlessAI

2025-06-16T02:10:13Z

0

transformers

[ "transformers", "safetensors", "gpt_neox", "text-generation", "generated_from_trainer", "trl", "grpo", "arxiv:2402.03300", "base_model:databricks/dolly-v2-3b", "base_model:finetune:databricks/dolly-v2-3b", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-15T20:12:55Z

--- base_model: databricks/dolly-v2-3b library_name: transformers model_name: cc3b0236-9ddf-4d9b-9243-f60acb10c299 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for cc3b0236-9ddf-4d9b-9243-f60acb10c299 This model is a fine-tuned version of [databricks/dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="FormlessAI/cc3b0236-9ddf-4d9b-9243-f60acb10c299", 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/phoenix-formless/Gradients/runs/odyon2h0) 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: 0.18.1 - Transformers: 4.52.4 - Pytorch: 2.7.0+cu128 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

mlx-community/llm-jp-3.1-1.8b-instruct4-8bit

mlx-community

2025-06-16T02:09:40Z

0

mlx

[ "mlx", "safetensors", "llama", "text-generation", "conversational", "en", "ja", "base_model:llm-jp/llm-jp-3.1-1.8b-instruct4", "base_model:quantized:llm-jp/llm-jp-3.1-1.8b-instruct4", "license:apache-2.0", "8-bit", "region:us" ]

text-generation

2025-06-16T02:06:24Z

--- license: apache-2.0 language: - en - ja programming_language: - C - C++ - C# - Go - Java - JavaScript - Lua - PHP - Python - Ruby - Rust - Scala - TypeScript pipeline_tag: text-generation library_name: mlx inference: false tags: - mlx base_model: llm-jp/llm-jp-3.1-1.8b-instruct4 --- # mlx-community/llm-jp-3.1-1.8b-instruct4-8bit This model [mlx-community/llm-jp-3.1-1.8b-instruct4-8bit](https://huggingface.co/mlx-community/llm-jp-3.1-1.8b-instruct4-8bit) was converted to MLX format from [llm-jp/llm-jp-3.1-1.8b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-1.8b-instruct4) using mlx-lm version **0.24.1**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/llm-jp-3.1-1.8b-instruct4-8bit") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```

jeremyys/multilingual-e5-base-Q5_0-GGUF

jeremyys

2025-06-16T02:08:48Z

0

sentence-transformers

[ "sentence-transformers", "gguf", "mteb", "Sentence Transformers", "sentence-similarity", "llama-cpp", "gguf-my-repo", "multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "om", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sa", "sd", "si", "sk", "sl", "so", "sq", "sr", "su", "sv", "sw", "ta", "te", "th", "tl", "tr", "ug", "uk", "ur", "uz", "vi", "xh", "yi", "zh", "base_model:intfloat/multilingual-e5-base", "base_model:quantized:intfloat/multilingual-e5-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us", "feature-extraction" ]

sentence-similarity

2025-06-16T02:08:44Z

--- tags: - mteb - Sentence Transformers - sentence-similarity - sentence-transformers - llama-cpp - gguf-my-repo language: - multilingual - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - 'no' - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh license: mit base_model: intfloat/multilingual-e5-base model-index: - name: multilingual-e5-base results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 78.97014925373135 - type: ap value: 43.69351129103008 - type: f1 value: 73.38075030070492 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (de) type: mteb/amazon_counterfactual config: de split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 71.7237687366167 - type: ap value: 82.22089859962671 - type: f1 value: 69.95532758884401 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en-ext) type: mteb/amazon_counterfactual config: en-ext split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 79.65517241379312 - type: ap value: 28.507918657094738 - type: f1 value: 66.84516013726119 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (ja) type: mteb/amazon_counterfactual config: ja split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.32976445396146 - type: ap value: 20.720481637566014 - type: f1 value: 59.78002763416003 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 90.63775 - type: ap value: 87.22277903861716 - type: f1 value: 90.60378636386807 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 44.546 - type: f1 value: 44.05666638370923 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (de) type: mteb/amazon_reviews_multi config: de split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 41.828 - type: f1 value: 41.2710255644252 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (es) type: mteb/amazon_reviews_multi config: es split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.534 - type: f1 value: 39.820743174270326 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (fr) type: mteb/amazon_reviews_multi config: fr split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 39.684 - type: f1 value: 39.11052682815307 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (ja) type: mteb/amazon_reviews_multi config: ja split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 37.436 - type: f1 value: 37.07082931930871 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (zh) type: mteb/amazon_reviews_multi config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 37.226000000000006 - type: f1 value: 36.65372077739185 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 22.831000000000003 - type: map_at_10 value: 36.42 - type: map_at_100 value: 37.699 - type: map_at_1000 value: 37.724000000000004 - type: map_at_3 value: 32.207 - type: map_at_5 value: 34.312 - type: mrr_at_1 value: 23.257 - type: mrr_at_10 value: 36.574 - type: mrr_at_100 value: 37.854 - type: mrr_at_1000 value: 37.878 - type: mrr_at_3 value: 32.385000000000005 - type: mrr_at_5 value: 34.48 - type: ndcg_at_1 value: 22.831000000000003 - type: ndcg_at_10 value: 44.230000000000004 - type: ndcg_at_100 value: 49.974000000000004 - type: ndcg_at_1000 value: 50.522999999999996 - type: ndcg_at_3 value: 35.363 - type: ndcg_at_5 value: 39.164 - type: precision_at_1 value: 22.831000000000003 - type: precision_at_10 value: 6.935 - type: precision_at_100 value: 0.9520000000000001 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 14.841 - type: precision_at_5 value: 10.754 - type: recall_at_1 value: 22.831000000000003 - type: recall_at_10 value: 69.346 - type: recall_at_100 value: 95.235 - type: recall_at_1000 value: 99.36 - type: recall_at_3 value: 44.523 - type: recall_at_5 value: 53.769999999999996 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 40.27789869854063 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 35.41979463347428 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 58.22752045109304 - type: mrr value: 71.51112430198303 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 84.71147646622866 - type: cos_sim_spearman value: 85.059167046486 - type: euclidean_pearson value: 75.88421613600647 - type: euclidean_spearman value: 75.12821787150585 - type: manhattan_pearson value: 75.22005646957604 - type: manhattan_spearman value: 74.42880434453272 - task: type: BitextMining dataset: name: MTEB BUCC (de-en) type: mteb/bucc-bitext-mining config: de-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 99.23799582463465 - type: f1 value: 99.12665274878218 - type: precision value: 99.07098121085595 - type: recall value: 99.23799582463465 - task: type: BitextMining dataset: name: MTEB BUCC (fr-en) type: mteb/bucc-bitext-mining config: fr-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 97.88685890380806 - type: f1 value: 97.59336708489249 - type: precision value: 97.44662117543473 - type: recall value: 97.88685890380806 - task: type: BitextMining dataset: name: MTEB BUCC (ru-en) type: mteb/bucc-bitext-mining config: ru-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 97.47142362313821 - type: f1 value: 97.1989377670015 - type: precision value: 97.06384944001847 - type: recall value: 97.47142362313821 - task: type: BitextMining dataset: name: MTEB BUCC (zh-en) type: mteb/bucc-bitext-mining config: zh-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 98.4728804634018 - type: f1 value: 98.2973494821836 - type: precision value: 98.2095839915745 - type: recall value: 98.4728804634018 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 82.74025974025975 - type: f1 value: 82.67420447730439 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 35.0380848063507 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 29.45956405670166 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: None metrics: - type: map_at_1 value: 32.122 - type: map_at_10 value: 42.03 - type: map_at_100 value: 43.364000000000004 - type: map_at_1000 value: 43.474000000000004 - type: map_at_3 value: 38.804 - type: map_at_5 value: 40.585 - type: mrr_at_1 value: 39.914 - type: mrr_at_10 value: 48.227 - type: mrr_at_100 value: 49.018 - type: mrr_at_1000 value: 49.064 - type: mrr_at_3 value: 45.994 - type: mrr_at_5 value: 47.396 - type: ndcg_at_1 value: 39.914 - type: ndcg_at_10 value: 47.825 - type: ndcg_at_100 value: 52.852 - type: ndcg_at_1000 value: 54.891 - type: ndcg_at_3 value: 43.517 - type: ndcg_at_5 value: 45.493 - type: precision_at_1 value: 39.914 - type: precision_at_10 value: 8.956 - type: precision_at_100 value: 1.388 - type: precision_at_1000 value: 0.182 - type: precision_at_3 value: 20.791999999999998 - type: precision_at_5 value: 14.821000000000002 - type: recall_at_1 value: 32.122 - type: recall_at_10 value: 58.294999999999995 - type: recall_at_100 value: 79.726 - type: recall_at_1000 value: 93.099 - type: recall_at_3 value: 45.017 - type: recall_at_5 value: 51.002 - type: map_at_1 value: 29.677999999999997 - type: map_at_10 value: 38.684000000000005 - type: map_at_100 value: 39.812999999999995 - type: map_at_1000 value: 39.945 - type: map_at_3 value: 35.831 - type: map_at_5 value: 37.446 - type: mrr_at_1 value: 37.771 - type: mrr_at_10 value: 44.936 - type: mrr_at_100 value: 45.583 - type: mrr_at_1000 value: 45.634 - type: mrr_at_3 value: 42.771 - type: mrr_at_5 value: 43.994 - type: ndcg_at_1 value: 37.771 - type: ndcg_at_10 value: 44.059 - type: ndcg_at_100 value: 48.192 - type: ndcg_at_1000 value: 50.375 - type: ndcg_at_3 value: 40.172000000000004 - type: ndcg_at_5 value: 41.899 - type: precision_at_1 value: 37.771 - type: precision_at_10 value: 8.286999999999999 - type: precision_at_100 value: 1.322 - type: precision_at_1000 value: 0.178 - type: precision_at_3 value: 19.406000000000002 - type: precision_at_5 value: 13.745 - type: recall_at_1 value: 29.677999999999997 - type: recall_at_10 value: 53.071 - type: recall_at_100 value: 70.812 - type: recall_at_1000 value: 84.841 - type: recall_at_3 value: 41.016000000000005 - type: recall_at_5 value: 46.22 - type: map_at_1 value: 42.675000000000004 - type: map_at_10 value: 53.93599999999999 - type: map_at_100 value: 54.806999999999995 - type: map_at_1000 value: 54.867 - type: map_at_3 value: 50.934000000000005 - type: map_at_5 value: 52.583 - type: mrr_at_1 value: 48.339 - type: mrr_at_10 value: 57.265 - type: mrr_at_100 value: 57.873 - type: mrr_at_1000 value: 57.906 - type: mrr_at_3 value: 55.193000000000005 - type: mrr_at_5 value: 56.303000000000004 - type: ndcg_at_1 value: 48.339 - type: ndcg_at_10 value: 59.19799999999999 - type: ndcg_at_100 value: 62.743 - type: ndcg_at_1000 value: 63.99399999999999 - type: ndcg_at_3 value: 54.367 - type: ndcg_at_5 value: 56.548 - type: precision_at_1 value: 48.339 - type: precision_at_10 value: 9.216000000000001 - type: precision_at_100 value: 1.1809999999999998 - type: precision_at_1000 value: 0.134 - type: precision_at_3 value: 23.72 - type: precision_at_5 value: 16.025 - type: recall_at_1 value: 42.675000000000004 - type: recall_at_10 value: 71.437 - type: recall_at_100 value: 86.803 - type: recall_at_1000 value: 95.581 - type: recall_at_3 value: 58.434 - type: recall_at_5 value: 63.754 - type: map_at_1 value: 23.518 - type: map_at_10 value: 30.648999999999997 - type: map_at_100 value: 31.508999999999997 - type: map_at_1000 value: 31.604 - type: map_at_3 value: 28.247 - type: map_at_5 value: 29.65 - type: mrr_at_1 value: 25.650000000000002 - type: mrr_at_10 value: 32.771 - type: mrr_at_100 value: 33.554 - type: mrr_at_1000 value: 33.629999999999995 - type: mrr_at_3 value: 30.433 - type: mrr_at_5 value: 31.812 - type: ndcg_at_1 value: 25.650000000000002 - type: ndcg_at_10 value: 34.929 - type: ndcg_at_100 value: 39.382 - type: ndcg_at_1000 value: 41.913 - type: ndcg_at_3 value: 30.292 - type: ndcg_at_5 value: 32.629999999999995 - type: precision_at_1 value: 25.650000000000002 - type: precision_at_10 value: 5.311 - type: precision_at_100 value: 0.792 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 12.58 - type: precision_at_5 value: 8.994 - type: recall_at_1 value: 23.518 - type: recall_at_10 value: 46.19 - type: recall_at_100 value: 67.123 - type: recall_at_1000 value: 86.442 - type: recall_at_3 value: 33.678000000000004 - type: recall_at_5 value: 39.244 - type: map_at_1 value: 15.891 - type: map_at_10 value: 22.464000000000002 - type: map_at_100 value: 23.483 - type: map_at_1000 value: 23.613 - type: map_at_3 value: 20.080000000000002 - type: map_at_5 value: 21.526 - type: mrr_at_1 value: 20.025000000000002 - type: mrr_at_10 value: 26.712999999999997 - type: mrr_at_100 value: 27.650000000000002 - type: mrr_at_1000 value: 27.737000000000002 - type: mrr_at_3 value: 24.274 - type: mrr_at_5 value: 25.711000000000002 - type: ndcg_at_1 value: 20.025000000000002 - type: ndcg_at_10 value: 27.028999999999996 - type: ndcg_at_100 value: 32.064 - type: ndcg_at_1000 value: 35.188 - type: ndcg_at_3 value: 22.512999999999998 - type: ndcg_at_5 value: 24.89 - type: precision_at_1 value: 20.025000000000002 - type: precision_at_10 value: 4.776 - type: precision_at_100 value: 0.8500000000000001 - type: precision_at_1000 value: 0.125 - type: precision_at_3 value: 10.531 - type: precision_at_5 value: 7.811 - type: recall_at_1 value: 15.891 - type: recall_at_10 value: 37.261 - type: recall_at_100 value: 59.12 - type: recall_at_1000 value: 81.356 - type: recall_at_3 value: 24.741 - type: recall_at_5 value: 30.753999999999998 - type: map_at_1 value: 27.544 - type: map_at_10 value: 36.283 - type: map_at_100 value: 37.467 - type: map_at_1000 value: 37.574000000000005 - type: map_at_3 value: 33.528999999999996 - type: map_at_5 value: 35.028999999999996 - type: mrr_at_1 value: 34.166999999999994 - type: mrr_at_10 value: 41.866 - type: mrr_at_100 value: 42.666 - type: mrr_at_1000 value: 42.716 - type: mrr_at_3 value: 39.541 - type: mrr_at_5 value: 40.768 - type: ndcg_at_1 value: 34.166999999999994 - type: ndcg_at_10 value: 41.577 - type: ndcg_at_100 value: 46.687 - type: ndcg_at_1000 value: 48.967 - type: ndcg_at_3 value: 37.177 - type: ndcg_at_5 value: 39.097 - type: precision_at_1 value: 34.166999999999994 - type: precision_at_10 value: 7.420999999999999 - type: precision_at_100 value: 1.165 - type: precision_at_1000 value: 0.154 - type: precision_at_3 value: 17.291999999999998 - type: precision_at_5 value: 12.166 - type: recall_at_1 value: 27.544 - type: recall_at_10 value: 51.99399999999999 - type: recall_at_100 value: 73.738 - type: recall_at_1000 value: 89.33 - type: recall_at_3 value: 39.179 - type: recall_at_5 value: 44.385999999999996 - type: map_at_1 value: 26.661 - type: map_at_10 value: 35.475 - type: map_at_100 value: 36.626999999999995 - type: map_at_1000 value: 36.741 - type: map_at_3 value: 32.818000000000005 - type: map_at_5 value: 34.397 - type: mrr_at_1 value: 32.647999999999996 - type: mrr_at_10 value: 40.784 - type: mrr_at_100 value: 41.602 - type: mrr_at_1000 value: 41.661 - type: mrr_at_3 value: 38.68 - type: mrr_at_5 value: 39.838 - type: ndcg_at_1 value: 32.647999999999996 - type: ndcg_at_10 value: 40.697 - type: ndcg_at_100 value: 45.799 - type: ndcg_at_1000 value: 48.235 - type: ndcg_at_3 value: 36.516 - type: ndcg_at_5 value: 38.515 - type: precision_at_1 value: 32.647999999999996 - type: precision_at_10 value: 7.202999999999999 - type: precision_at_100 value: 1.1360000000000001 - type: precision_at_1000 value: 0.151 - type: precision_at_3 value: 17.314 - type: precision_at_5 value: 12.145999999999999 - type: recall_at_1 value: 26.661 - type: recall_at_10 value: 50.995000000000005 - type: recall_at_100 value: 73.065 - type: recall_at_1000 value: 89.781 - type: recall_at_3 value: 39.073 - type: recall_at_5 value: 44.395 - type: map_at_1 value: 25.946583333333333 - type: map_at_10 value: 33.79725 - type: map_at_100 value: 34.86408333333333 - type: map_at_1000 value: 34.9795 - type: map_at_3 value: 31.259999999999998 - type: map_at_5 value: 32.71541666666666 - type: mrr_at_1 value: 30.863749999999996 - type: mrr_at_10 value: 37.99183333333333 - type: mrr_at_100 value: 38.790499999999994 - type: mrr_at_1000 value: 38.85575000000001 - type: mrr_at_3 value: 35.82083333333333 - type: mrr_at_5 value: 37.07533333333333 - type: ndcg_at_1 value: 30.863749999999996 - type: ndcg_at_10 value: 38.52141666666667 - type: ndcg_at_100 value: 43.17966666666667 - type: ndcg_at_1000 value: 45.64608333333333 - type: ndcg_at_3 value: 34.333000000000006 - type: ndcg_at_5 value: 36.34975 - type: precision_at_1 value: 30.863749999999996 - type: precision_at_10 value: 6.598999999999999 - type: precision_at_100 value: 1.0502500000000001 - type: precision_at_1000 value: 0.14400000000000002 - type: precision_at_3 value: 15.557583333333334 - type: precision_at_5 value: 11.020000000000001 - type: recall_at_1 value: 25.946583333333333 - type: recall_at_10 value: 48.36991666666666 - type: recall_at_100 value: 69.02408333333334 - type: recall_at_1000 value: 86.43858333333331 - type: recall_at_3 value: 36.4965 - type: recall_at_5 value: 41.76258333333334 - type: map_at_1 value: 22.431 - type: map_at_10 value: 28.889 - type: map_at_100 value: 29.642000000000003 - type: map_at_1000 value: 29.742 - type: map_at_3 value: 26.998 - type: map_at_5 value: 28.172000000000004 - type: mrr_at_1 value: 25.307000000000002 - type: mrr_at_10 value: 31.763 - type: mrr_at_100 value: 32.443 - type: mrr_at_1000 value: 32.531 - type: mrr_at_3 value: 29.959000000000003 - type: mrr_at_5 value: 31.063000000000002 - type: ndcg_at_1 value: 25.307000000000002 - type: ndcg_at_10 value: 32.586999999999996 - type: ndcg_at_100 value: 36.5 - type: ndcg_at_1000 value: 39.133 - type: ndcg_at_3 value: 29.25 - type: ndcg_at_5 value: 31.023 - type: precision_at_1 value: 25.307000000000002 - type: precision_at_10 value: 4.954 - type: precision_at_100 value: 0.747 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 12.577 - type: precision_at_5 value: 8.741999999999999 - type: recall_at_1 value: 22.431 - type: recall_at_10 value: 41.134 - type: recall_at_100 value: 59.28600000000001 - type: recall_at_1000 value: 78.857 - type: recall_at_3 value: 31.926 - type: recall_at_5 value: 36.335 - type: map_at_1 value: 17.586 - type: map_at_10 value: 23.304 - type: map_at_100 value: 24.159 - type: map_at_1000 value: 24.281 - type: map_at_3 value: 21.316 - type: map_at_5 value: 22.383 - type: mrr_at_1 value: 21.645 - type: mrr_at_10 value: 27.365000000000002 - type: mrr_at_100 value: 28.108 - type: mrr_at_1000 value: 28.192 - type: mrr_at_3 value: 25.482 - type: mrr_at_5 value: 26.479999999999997 - type: ndcg_at_1 value: 21.645 - type: ndcg_at_10 value: 27.306 - type: ndcg_at_100 value: 31.496000000000002 - type: ndcg_at_1000 value: 34.53 - type: ndcg_at_3 value: 23.73 - type: ndcg_at_5 value: 25.294 - type: precision_at_1 value: 21.645 - type: precision_at_10 value: 4.797 - type: precision_at_100 value: 0.8059999999999999 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 10.850999999999999 - type: precision_at_5 value: 7.736 - type: recall_at_1 value: 17.586 - type: recall_at_10 value: 35.481 - type: recall_at_100 value: 54.534000000000006 - type: recall_at_1000 value: 76.456 - type: recall_at_3 value: 25.335 - type: recall_at_5 value: 29.473 - type: map_at_1 value: 25.095 - type: map_at_10 value: 32.374 - type: map_at_100 value: 33.537 - type: map_at_1000 value: 33.634 - type: map_at_3 value: 30.089 - type: map_at_5 value: 31.433 - type: mrr_at_1 value: 29.198 - type: mrr_at_10 value: 36.01 - type: mrr_at_100 value: 37.022 - type: mrr_at_1000 value: 37.083 - type: mrr_at_3 value: 33.94 - type: mrr_at_5 value: 35.148 - type: ndcg_at_1 value: 29.198 - type: ndcg_at_10 value: 36.729 - type: ndcg_at_100 value: 42.114000000000004 - type: ndcg_at_1000 value: 44.592 - type: ndcg_at_3 value: 32.644 - type: ndcg_at_5 value: 34.652 - type: precision_at_1 value: 29.198 - type: precision_at_10 value: 5.970000000000001 - type: precision_at_100 value: 0.967 - type: precision_at_1000 value: 0.129 - type: precision_at_3 value: 14.396999999999998 - type: precision_at_5 value: 10.093 - type: recall_at_1 value: 25.095 - type: recall_at_10 value: 46.392 - type: recall_at_100 value: 69.706 - type: recall_at_1000 value: 87.738 - type: recall_at_3 value: 35.303000000000004 - type: recall_at_5 value: 40.441 - type: map_at_1 value: 26.857999999999997 - type: map_at_10 value: 34.066 - type: map_at_100 value: 35.671 - type: map_at_1000 value: 35.881 - type: map_at_3 value: 31.304 - type: map_at_5 value: 32.885 - type: mrr_at_1 value: 32.411 - type: mrr_at_10 value: 38.987 - type: mrr_at_100 value: 39.894 - type: mrr_at_1000 value: 39.959 - type: mrr_at_3 value: 36.626999999999995 - type: mrr_at_5 value: 38.011 - type: ndcg_at_1 value: 32.411 - type: ndcg_at_10 value: 39.208 - type: ndcg_at_100 value: 44.626 - type: ndcg_at_1000 value: 47.43 - type: ndcg_at_3 value: 35.091 - type: ndcg_at_5 value: 37.119 - type: precision_at_1 value: 32.411 - type: precision_at_10 value: 7.51 - type: precision_at_100 value: 1.486 - type: precision_at_1000 value: 0.234 - type: precision_at_3 value: 16.14 - type: precision_at_5 value: 11.976 - type: recall_at_1 value: 26.857999999999997 - type: recall_at_10 value: 47.407 - type: recall_at_100 value: 72.236 - type: recall_at_1000 value: 90.77 - type: recall_at_3 value: 35.125 - type: recall_at_5 value: 40.522999999999996 - type: map_at_1 value: 21.3 - type: map_at_10 value: 27.412999999999997 - type: map_at_100 value: 28.29 - type: map_at_1000 value: 28.398 - type: map_at_3 value: 25.169999999999998 - type: map_at_5 value: 26.496 - type: mrr_at_1 value: 23.29 - type: mrr_at_10 value: 29.215000000000003 - type: mrr_at_100 value: 30.073 - type: mrr_at_1000 value: 30.156 - type: mrr_at_3 value: 26.956000000000003 - type: mrr_at_5 value: 28.38 - type: ndcg_at_1 value: 23.29 - type: ndcg_at_10 value: 31.113000000000003 - type: ndcg_at_100 value: 35.701 - type: ndcg_at_1000 value: 38.505 - type: ndcg_at_3 value: 26.727 - type: ndcg_at_5 value: 29.037000000000003 - type: precision_at_1 value: 23.29 - type: precision_at_10 value: 4.787 - type: precision_at_100 value: 0.763 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 11.091 - type: precision_at_5 value: 7.985 - type: recall_at_1 value: 21.3 - type: recall_at_10 value: 40.782000000000004 - type: recall_at_100 value: 62.13999999999999 - type: recall_at_1000 value: 83.012 - type: recall_at_3 value: 29.131 - type: recall_at_5 value: 34.624 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: climate-fever config: default split: test revision: None metrics: - type: map_at_1 value: 9.631 - type: map_at_10 value: 16.634999999999998 - type: map_at_100 value: 18.23 - type: map_at_1000 value: 18.419 - type: map_at_3 value: 13.66 - type: map_at_5 value: 15.173 - type: mrr_at_1 value: 21.368000000000002 - type: mrr_at_10 value: 31.56 - type: mrr_at_100 value: 32.58 - type: mrr_at_1000 value: 32.633 - type: mrr_at_3 value: 28.241 - type: mrr_at_5 value: 30.225 - type: ndcg_at_1 value: 21.368000000000002 - type: ndcg_at_10 value: 23.855999999999998 - type: ndcg_at_100 value: 30.686999999999998 - type: ndcg_at_1000 value: 34.327000000000005 - type: ndcg_at_3 value: 18.781 - type: ndcg_at_5 value: 20.73 - type: precision_at_1 value: 21.368000000000002 - type: precision_at_10 value: 7.564 - type: precision_at_100 value: 1.496 - type: precision_at_1000 value: 0.217 - type: precision_at_3 value: 13.876 - type: precision_at_5 value: 11.062 - type: recall_at_1 value: 9.631 - type: recall_at_10 value: 29.517 - type: recall_at_100 value: 53.452 - type: recall_at_1000 value: 74.115 - type: recall_at_3 value: 17.605999999999998 - type: recall_at_5 value: 22.505 - task: type: Retrieval dataset: name: MTEB DBPedia type: dbpedia-entity config: default split: test revision: None metrics: - type: map_at_1 value: 8.885 - type: map_at_10 value: 18.798000000000002 - type: map_at_100 value: 26.316 - type: map_at_1000 value: 27.869 - type: map_at_3 value: 13.719000000000001 - type: map_at_5 value: 15.716 - type: mrr_at_1 value: 66 - type: mrr_at_10 value: 74.263 - type: mrr_at_100 value: 74.519 - type: mrr_at_1000 value: 74.531 - type: mrr_at_3 value: 72.458 - type: mrr_at_5 value: 73.321 - type: ndcg_at_1 value: 53.87499999999999 - type: ndcg_at_10 value: 40.355999999999995 - type: ndcg_at_100 value: 44.366 - type: ndcg_at_1000 value: 51.771 - type: ndcg_at_3 value: 45.195 - type: ndcg_at_5 value: 42.187000000000005 - type: precision_at_1 value: 66 - type: precision_at_10 value: 31.75 - type: precision_at_100 value: 10.11 - type: precision_at_1000 value: 1.9800000000000002 - type: precision_at_3 value: 48.167 - type: precision_at_5 value: 40.050000000000004 - type: recall_at_1 value: 8.885 - type: recall_at_10 value: 24.471999999999998 - type: recall_at_100 value: 49.669000000000004 - type: recall_at_1000 value: 73.383 - type: recall_at_3 value: 14.872 - type: recall_at_5 value: 18.262999999999998 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 45.18 - type: f1 value: 40.26878691789978 - task: type: Retrieval dataset: name: MTEB FEVER type: fever config: default split: test revision: None metrics: - type: map_at_1 value: 62.751999999999995 - type: map_at_10 value: 74.131 - type: map_at_100 value: 74.407 - type: map_at_1000 value: 74.423 - type: map_at_3 value: 72.329 - type: map_at_5 value: 73.555 - type: mrr_at_1 value: 67.282 - type: mrr_at_10 value: 78.292 - type: mrr_at_100 value: 78.455 - type: mrr_at_1000 value: 78.458 - type: mrr_at_3 value: 76.755 - type: mrr_at_5 value: 77.839 - type: ndcg_at_1 value: 67.282 - type: ndcg_at_10 value: 79.443 - type: ndcg_at_100 value: 80.529 - type: ndcg_at_1000 value: 80.812 - type: ndcg_at_3 value: 76.281 - type: ndcg_at_5 value: 78.235 - type: precision_at_1 value: 67.282 - type: precision_at_10 value: 10.078 - type: precision_at_100 value: 1.082 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 30.178 - type: precision_at_5 value: 19.232 - type: recall_at_1 value: 62.751999999999995 - type: recall_at_10 value: 91.521 - type: recall_at_100 value: 95.997 - type: recall_at_1000 value: 97.775 - type: recall_at_3 value: 83.131 - type: recall_at_5 value: 87.93299999999999 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: fiqa config: default split: test revision: None metrics: - type: map_at_1 value: 18.861 - type: map_at_10 value: 30.252000000000002 - type: map_at_100 value: 32.082 - type: map_at_1000 value: 32.261 - type: map_at_3 value: 25.909 - type: map_at_5 value: 28.296 - type: mrr_at_1 value: 37.346000000000004 - type: mrr_at_10 value: 45.802 - type: mrr_at_100 value: 46.611999999999995 - type: mrr_at_1000 value: 46.659 - type: mrr_at_3 value: 43.056 - type: mrr_at_5 value: 44.637 - type: ndcg_at_1 value: 37.346000000000004 - type: ndcg_at_10 value: 38.169 - type: ndcg_at_100 value: 44.864 - type: ndcg_at_1000 value: 47.974 - type: ndcg_at_3 value: 33.619 - type: ndcg_at_5 value: 35.317 - type: precision_at_1 value: 37.346000000000004 - type: precision_at_10 value: 10.693999999999999 - type: precision_at_100 value: 1.775 - type: precision_at_1000 value: 0.231 - type: precision_at_3 value: 22.325 - type: precision_at_5 value: 16.852 - type: recall_at_1 value: 18.861 - type: recall_at_10 value: 45.672000000000004 - type: recall_at_100 value: 70.60499999999999 - type: recall_at_1000 value: 89.216 - type: recall_at_3 value: 30.361 - type: recall_at_5 value: 36.998999999999995 - task: type: Retrieval dataset: name: MTEB HotpotQA type: hotpotqa config: default split: test revision: None metrics: - type: map_at_1 value: 37.852999999999994 - type: map_at_10 value: 59.961 - type: map_at_100 value: 60.78 - type: map_at_1000 value: 60.843 - type: map_at_3 value: 56.39999999999999 - type: map_at_5 value: 58.646 - type: mrr_at_1 value: 75.70599999999999 - type: mrr_at_10 value: 82.321 - type: mrr_at_100 value: 82.516 - type: mrr_at_1000 value: 82.525 - type: mrr_at_3 value: 81.317 - type: mrr_at_5 value: 81.922 - type: ndcg_at_1 value: 75.70599999999999 - type: ndcg_at_10 value: 68.557 - type: ndcg_at_100 value: 71.485 - type: ndcg_at_1000 value: 72.71600000000001 - type: ndcg_at_3 value: 63.524 - type: ndcg_at_5 value: 66.338 - type: precision_at_1 value: 75.70599999999999 - type: precision_at_10 value: 14.463000000000001 - type: precision_at_100 value: 1.677 - type: precision_at_1000 value: 0.184 - type: precision_at_3 value: 40.806 - type: precision_at_5 value: 26.709 - type: recall_at_1 value: 37.852999999999994 - type: recall_at_10 value: 72.316 - type: recall_at_100 value: 83.842 - type: recall_at_1000 value: 91.999 - type: recall_at_3 value: 61.209 - type: recall_at_5 value: 66.77199999999999 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 85.46039999999999 - type: ap value: 79.9812521351881 - type: f1 value: 85.31722909702084 - task: type: Retrieval dataset: name: MTEB MSMARCO type: msmarco config: default split: dev revision: None metrics: - type: map_at_1 value: 22.704 - type: map_at_10 value: 35.329 - type: map_at_100 value: 36.494 - type: map_at_1000 value: 36.541000000000004 - type: map_at_3 value: 31.476 - type: map_at_5 value: 33.731 - type: mrr_at_1 value: 23.294999999999998 - type: mrr_at_10 value: 35.859 - type: mrr_at_100 value: 36.968 - type: mrr_at_1000 value: 37.008 - type: mrr_at_3 value: 32.085 - type: mrr_at_5 value: 34.299 - type: ndcg_at_1 value: 23.324 - type: ndcg_at_10 value: 42.274 - type: ndcg_at_100 value: 47.839999999999996 - type: ndcg_at_1000 value: 48.971 - type: ndcg_at_3 value: 34.454 - type: ndcg_at_5 value: 38.464 - type: precision_at_1 value: 23.324 - type: precision_at_10 value: 6.648 - type: precision_at_100 value: 0.9440000000000001 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.674999999999999 - type: precision_at_5 value: 10.850999999999999 - type: recall_at_1 value: 22.704 - type: recall_at_10 value: 63.660000000000004 - type: recall_at_100 value: 89.29899999999999 - type: recall_at_1000 value: 97.88900000000001 - type: recall_at_3 value: 42.441 - type: recall_at_5 value: 52.04 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.1326949384405 - type: f1 value: 92.89743579612082 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (de) type: mteb/mtop_domain config: de split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 89.62524654832347 - type: f1 value: 88.65106082263151 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (es) type: mteb/mtop_domain config: es split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - 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type: cos_sim_recall value: 71.10817941952506 - type: dot_accuracy value: 78.98313166835548 - type: dot_ap value: 44.492521645493795 - type: dot_f1 value: 45.814889336016094 - type: dot_precision value: 37.02439024390244 - type: dot_recall value: 60.07915567282321 - type: euclidean_accuracy value: 85.3907134767837 - type: euclidean_ap value: 71.53847289080343 - type: euclidean_f1 value: 65.95952206778834 - type: euclidean_precision value: 61.31006346328196 - type: euclidean_recall value: 71.37203166226914 - type: manhattan_accuracy value: 85.40859510043511 - type: manhattan_ap value: 71.49664104395515 - type: manhattan_f1 value: 65.98569969356485 - type: manhattan_precision value: 63.928748144482924 - type: manhattan_recall value: 68.17941952506597 - type: max_accuracy value: 85.59933241938367 - type: max_ap value: 72.20760361208136 - type: max_f1 value: 66.4447731755424 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.83261536073273 - type: cos_sim_ap value: 85.48178133644264 - type: cos_sim_f1 value: 77.87816307403935 - type: cos_sim_precision value: 75.88953021114926 - type: cos_sim_recall value: 79.97382198952879 - type: dot_accuracy value: 79.76287499514883 - type: dot_ap value: 59.17438838475084 - type: dot_f1 value: 56.34566667855996 - type: dot_precision value: 52.50349092359864 - type: dot_recall value: 60.794579611949494 - type: euclidean_accuracy value: 88.76857996662397 - type: euclidean_ap value: 85.22764834359887 - type: euclidean_f1 value: 77.65379751543554 - type: euclidean_precision value: 75.11152683839401 - type: euclidean_recall value: 80.37419156144134 - type: manhattan_accuracy value: 88.6987231730508 - type: manhattan_ap value: 85.18907981724007 - type: manhattan_f1 value: 77.51967028849757 - type: manhattan_precision value: 75.49992701795358 - type: manhattan_recall value: 79.65044656606098 - type: max_accuracy value: 88.83261536073273 - type: max_ap value: 85.48178133644264 - type: max_f1 value: 77.87816307403935 --- # jeremyys/multilingual-e5-base-Q5_0-GGUF This model was converted to GGUF format from [`intfloat/multilingual-e5-base`](https://huggingface.co/intfloat/multilingual-e5-base) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/intfloat/multilingual-e5-base) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo jeremyys/multilingual-e5-base-Q5_0-GGUF --hf-file multilingual-e5-base-q5_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo jeremyys/multilingual-e5-base-Q5_0-GGUF --hf-file multilingual-e5-base-q5_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo jeremyys/multilingual-e5-base-Q5_0-GGUF --hf-file multilingual-e5-base-q5_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo jeremyys/multilingual-e5-base-Q5_0-GGUF --hf-file multilingual-e5-base-q5_0.gguf -c 2048 ```

MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.05_0.05_epoch1

MinaMila

2025-06-16T02:08:28Z

0

transformers

[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-16T02:06:32Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Daemontatox/Grifflet-2

Daemontatox

2025-06-16T02:05:58Z

25

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "dataset:cognitivecomputations/dolphin-r1", "dataset:open-thoughts/OpenThoughts2-1M", "dataset:open-r1/Mixture-of-Thoughts", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-06-05T17:04:10Z

--- base_model: qwen/qwen3-8b tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en datasets: - cognitivecomputations/dolphin-r1 - open-thoughts/OpenThoughts2-1M - open-r1/Mixture-of-Thoughts library_name: transformers new_version: qwen/qwen3-8b --- ![Grifflet-2](./image.webp) ## **Model Description** ### **Purpose** "Daemontatox/Grifflet-2" is a state-of-the-art language model designed to excel in hybrid tasks that combine conversational abilities with reasoning capabilities. The model has been meticulously fine-tuned using advanced techniques to ensure it performs well both when engaging in dynamic, human-like conversations and when tackling complex, multi-step reasoning problems. ### **Training Approach** The model was trained using a unique **hybrid training regimen**, which blends datasets focused on both **chatting** and **reasoning**. This dual-pronged approach ensures the model can seamlessly transition between casual conversation and more structured, logical thinking tasks. Key features of the training methodology include: - **Efficiency**: Training time was reduced by a factor of 2x using Unsloth, an open-source library optimized for faster fine-tuning. - **Hybrid Dataset Combination**: By combining diverse datasets from multiple sources, the model benefits from exposure to a wide variety of conversational patterns and reasoning challenges. - **Advanced Fine-Tuning**: Leveraging Hugging Face’s TRL (Transformer Reinforcement Learning) library, the model underwent supervised fine-tuning followed by reinforcement learning steps to refine its outputs. --- ## **Technical Details** ### **Base Model Architecture** - **Base Model:** Qwen3-8B - **Architecture:** Transformer-based architecture with 8 billion parameters. - **Language:** English (`en`) - **Library Used:** [Transformers](https://huggingface.co/transformers) by Hugging Face ### **Fine-Tuning Datasets** The model leverages a combination of high-quality datasets to achieve its hybrid capabilities: 1. **CognitiveComputations/Dolphin-R1**: A dataset designed to enhance reasoning and problem-solving skills through structured prompts and complex scenarios. 2. **Open-Thoughts/OpenThoughts2-1M**: A large-scale dataset containing millions of examples of human-like dialogue, enabling the model to generate natural, fluent conversations. 3. **Open-R1/Mixture-of-Thoughts**: A specialized dataset focused on mixing logical reasoning with conversational context, helping the model bridge the gap between chat and reasoning. ### **Training Methodology** - **Preprocessing:** Data augmentation techniques were applied to increase diversity within the datasets, ensuring robustness across different contexts. - **Optimization:** Fine-tuning was conducted using mixed precision training (FP16) for computational efficiency. - **Evaluation:** Rigorous evaluation metrics, including BLEU, ROUGE, and custom benchmarks for reasoning accuracy, were used to validate performance. --- ## **Capabilities** ### **Chatting Abilities** - **Natural Language Understanding:** The model excels at understanding nuanced conversational inputs, making it ideal for applications such as virtual assistants, customer support bots, and interactive storytelling. - **Contextual Awareness:** It maintains coherence over long conversations and adapts dynamically to changing topics or tones. - **Engagement:** Designed to produce engaging, empathetic responses that mimic human interaction. ### **Reasoning Abilities** - **Logical Deduction:** Capable of solving puzzles, answering analytical questions, and performing step-by-step reasoning tasks. - **Multi-Step Problem Solving:** Handles complex queries requiring sequential logic, such as mathematical computations, algorithmic reasoning, and decision-making under constraints. - **Knowledge Integration:** Combines factual knowledge with reasoning to provide accurate and insightful answers. --- ## **Intended Use Cases** ### **Primary Applications** 1. **Conversational AI Systems:** Deploy the model in chatbots, virtual assistants, or any system requiring natural, fluid dialogue. 2. **Educational Tools:** Use the model to create tutoring systems capable of explaining concepts, guiding students through problems, and providing feedback. 3. **Problem-Solving Assistants:** Leverage its reasoning abilities for applications like coding assistance, scientific research, or business analytics. ### **Secondary Applications** - Content generation (e.g., writing essays, articles, or creative pieces). - Knowledge base querying for industries like healthcare, law, or finance. - Game development (e.g., creating intelligent NPCs with reasoning capabilities). --- ## **Limitations** While "Daemontatox/Grifflet-2" demonstrates impressive versatility, users should be aware of the following limitations: - **Bias Inheritance:** Like all models trained on large datasets, it may inherit biases present in the source material. Careful monitoring is recommended for sensitive use cases. - **Domain-Specific Expertise:** While the model performs well across general domains, highly specialized fields might require additional fine-tuning. - **Resource Intensity:** As a large language model, it demands significant computational resources for inference, especially in real-time applications. --- ## **Ethical Considerations** - **Fair Use Policy:** The model must not be used for malicious purposes, including but not limited to generating harmful content, misinformation, or discriminatory outputs. - **Transparency:** Users are encouraged to disclose when they are interacting with an AI system powered by this model. - **Data Privacy:** Ensure compliance with data protection regulations (e.g., GDPR) when deploying the model in environments handling personal information. --- ## **How to Use** ### **Installation** To use "Daemontatox/Grifflet-2," install the necessary libraries and load the model via Hugging Face's `transformers` library: ```python from transformers import AutoTokenizer, AutoModelForCausalLM # Load tokenizer and model tokenizer = AutoTokenizer.from_pretrained("Daemontatox/Grifflet-2") model = AutoModelForCausalLM.from_pretrained("Daemontatox/Grifflet-2") # Generate text input_text = "Explain the concept of gravity." inputs = tokenizer(input_text, return_tensors="pt") outputs = model.generate(**inputs, max_length=100) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ### **Hardware Requirements** - Recommended: GPU with at least 24GB VRAM (e.g., NVIDIA A100 or similar). - Minimum: CPU with sufficient RAM for smaller batch sizes. --- ## **Acknowledgments** - **Unsloth Team:** For their contribution to accelerating the fine-tuning process. - **Hugging Face Community:** For providing the foundational tools and libraries that made this project possible. - **Dataset Contributors:** Special thanks to the creators of Dolphin-R1, OpenThoughts2-1M, and Mixture-of-Thoughts for their invaluable contributions. --- ## **Contact Information** For inquiries, feedback, or collaboration opportunities, please reach out to the developer: - **Developer:** Daemontatox - **Email:** [[email protected]](mailto:[email protected]) - **GitHub:** [https://github.com/Daemontatox](https://github.com/Daemontatox) ---

CriteriaPO/qwen2.5-3b-dpo-finegrained-no-tools

CriteriaPO

2025-06-16T02:05:47Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "arxiv:2305.18290", "base_model:CriteriaPO/qwen2.5-3b-sft-10", "base_model:finetune:CriteriaPO/qwen2.5-3b-sft-10", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-09T23:27:00Z

--- base_model: CriteriaPO/qwen2.5-3b-sft-10 library_name: transformers model_name: qwen2.5-3b-dpo-finegrained-no-tools tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for qwen2.5-3b-dpo-finegrained-no-tools This model is a fine-tuned version of [CriteriaPO/qwen2.5-3b-sft-10](https://huggingface.co/CriteriaPO/qwen2.5-3b-sft-10). 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="CriteriaPO/qwen2.5-3b-dpo-finegrained-no-tools", 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/bborges/CriteriaPreferences/runs/auibjmrb) 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: 0.12.2 - Transformers: 4.46.3 - Pytorch: 2.1.2+cu121 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## 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}} } ```

MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.15_0.75_epoch2

MinaMila

2025-06-16T02:04:53Z

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-16T02:02:46Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

CelestialWandererOfTheVoid/test

CelestialWandererOfTheVoid

2025-06-16T02:04:32Z

0

null

[ "region:us" ]

null

2025-06-16T02:04:31Z

# Audio Enhancement API This repository contains a FastAPI-based audio enhancement model API with the following endpoints: 1. `/status/` : Communicates API status 2. `/prepare/` : Makes necessary preparations (downloading checkpoints, etc.) and initializes model 3. `/upload-audio/` : Upload audio files, save to noisy audio directory 4. `/enhance/` : Initialize model, enhance audio files, save to enhanced audio directory 5. `/download-enhanced/` : Download enhanced audio files ## Setup and Usage 1. Install the requirements: ``` pip install -r app/requirements.txt ``` 2. Run the API: ``` python -m app.app ``` 3. Access the API documentation at `http://localhost:8000/docs`

BootesVoid/cmbyblijq03bvrdqs0ce71ulk_cmbyfdvbe03i5rdqsnp2il175

BootesVoid

2025-06-16T02:02:57Z

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-06-16T02:02:51Z

--- 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: EMILYY --- # Cmbyblijq03Bvrdqs0Ce71Ulk_Cmbyfdvbe03I5Rdqsnp2Il175 <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 `EMILYY` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "EMILYY", "lora_weights": "https://huggingface.co/BootesVoid/cmbyblijq03bvrdqs0ce71ulk_cmbyfdvbe03i5rdqsnp2il175/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmbyblijq03bvrdqs0ce71ulk_cmbyfdvbe03i5rdqsnp2il175', weight_name='lora.safetensors') image = pipeline('EMILYY').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmbyblijq03bvrdqs0ce71ulk_cmbyfdvbe03i5rdqsnp2il175/discussions) to add images that show off what you’ve made with this LoRA.

rmdhirr/suja-lorab-ep5-suja-5000

rmdhirr

2025-06-16T02:02:26Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:rmdhirr/merged-suja-latest", "base_model:adapter:rmdhirr/merged-suja-latest", "region:us" ]

null

2025-06-16T02:01:24Z

--- base_model: rmdhirr/merged-suja-latest library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [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

gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_negative_3x3_seed_1_seed_25_seed_2_seed_42_20250616_015359

gradientrouting-spar

2025-06-16T02:02:16Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-06-16T02:01:59Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

JohnRoger/Huihui-MoE-12B-A4B-abliterated-Q8_0-GGUF

JohnRoger

2025-06-16T02:00:51Z

0

transformers

[ "transformers", "gguf", "moe", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:huihui-ai/Huihui-MoE-12B-A4B-abliterated", "base_model:quantized:huihui-ai/Huihui-MoE-12B-A4B-abliterated", "license:apache-2.0", "endpoints_compatible", "region:us" ]

text-generation

2025-06-16T01:59:54Z

--- license: apache-2.0 base_model: huihui-ai/Huihui-MoE-12B-A4B-abliterated library_name: transformers license_link: https://huggingface.co/Qwen/Qwen3-4B/blob/main/LICENSE pipeline_tag: text-generation tags: - moe - llama-cpp - gguf-my-repo extra_gated_prompt: '**Usage Warnings** “**Risk of Sensitive or Controversial Outputs**“: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs. “**Not Suitable for All Audiences**:“ Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security. “**Legal and Ethical Responsibilities**“: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences. “**Research and Experimental Use**“: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications. “**Monitoring and Review Recommendations**“: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content. “**No Default Safety Guarantees**“: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.' --- # JohnRoger/Huihui-MoE-12B-A4B-abliterated-Q8_0-GGUF This model was converted to GGUF format from [`huihui-ai/Huihui-MoE-12B-A4B-abliterated`](https://huggingface.co/huihui-ai/Huihui-MoE-12B-A4B-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/huihui-ai/Huihui-MoE-12B-A4B-abliterated) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo JohnRoger/Huihui-MoE-12B-A4B-abliterated-Q8_0-GGUF --hf-file huihui-moe-12b-a4b-abliterated-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo JohnRoger/Huihui-MoE-12B-A4B-abliterated-Q8_0-GGUF --hf-file huihui-moe-12b-a4b-abliterated-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo JohnRoger/Huihui-MoE-12B-A4B-abliterated-Q8_0-GGUF --hf-file huihui-moe-12b-a4b-abliterated-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo JohnRoger/Huihui-MoE-12B-A4B-abliterated-Q8_0-GGUF --hf-file huihui-moe-12b-a4b-abliterated-q8_0.gguf -c 2048 ```

Enzogbs/Reinforce-1

Enzogbs

2025-06-16T02:00:18Z

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2025-06-16T02:00:06Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

pozapas/gemma-3-evacuation

pozapas

2025-06-16T01:59:07Z

0

null

[ "safetensors", "gguf", "gemma3", "evacuation", "safety", "emergency-planning", "fire-safety", "en", "dataset:pozapas/evacuation-safety-qa", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:quantized:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "doi:10.57967/hf/5794", "license:cc", "endpoints_compatible", "region:us", "conversational" ]

null

2025-06-16T00:54:14Z

--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit license: cc language: - en tags: - evacuation - safety - emergency-planning - fire-safety datasets: - pozapas/evacuation-safety-qa --- # Gemma-3-Evacuation (4B) This model is a fine-tuned version of [Google's Gemma-3-4B-it](https://huggingface.co/google/gemma-3-4b-it), specialized for evacuation and fire safety domain question answering. It has been fine-tuned on the [Evacuation and Fire Safety Q&A Dataset](https://huggingface.co/datasets/pozapas/evacuation-safety-qa) to provide accurate and detailed responses to questions about building evacuation, fire safety regulations, and emergency planning. ## Model Details - **Model Type:** Gemma-3 (4B parameters) - **Training Method:** Fine-tuned using Parameter-Efficient Fine-Tuning (PEFT) with Low-Rank Adaptation (LoRA) - **Training Library:** [Unsloth](https://github.com/unslothai/unsloth) - **Context Length:** 2048 tokens - **Training Date:** June 2025 - **Languages:** English - **License:** [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) - **Quantization:** Available in Q8_0 GGUF format for efficient inference ## Intended Use This model is designed to: 1. Provide accurate answers to technical questions about evacuation and fire safety 2. Support emergency planning professionals in decision-making 3. Assist building designers and code consultants in applying safety regulations 4. Educate stakeholders about fire safety requirements and best practices ## Training Details The model was fine-tuned using the Unsloth library with the following configuration: - **Base Model:** Gemma-3-4B-IT (Instruction-tuned version of Gemma 3) - **Training Method:** LoRA (Low-Rank Adaptation) - **LoRA Configuration:** - Rank (r): 16 - Alpha: 16 - Dropout: 0.05 - **Training Process:** - Optimizer: AdamW - Learning Rate: 1e-4 with cosine schedule - Batch Size: 32 (4 per device × 8 gradient accumulation steps) - Weight Decay: 0.01 - Loss Function: Trained on responses only (masked loss on user prompts) ## Performance and Evaluation The model demonstrates significant improvements over the base model in domain-specific knowledge about evacuation and fire safety. Key performance metrics include: - **ROUGE-L F1:** 0.72 - **BERTScore F1:** 0.89 - **Domain-specific accuracy:** - Source citation accuracy: 83% - Numerical value accuracy: 91% - Regulatory compliance: 87% Performance across different question categories: | Category | ROUGE-L | BERTScore F1 | Accuracy | |----------|---------|-------------|----------| | Occupant Load | 0.74 | 0.91 | 93% | | Egress | 0.73 | 0.90 | 89% | | Fire Protection | 0.71 | 0.88 | 85% | | Accessibility | 0.68 | 0.85 | 82% | | Emergency Planning | 0.72 | 0.89 | 84% | ## Limitations - The model's knowledge is limited to regulations and standards covered in the training dataset - Responses may not reflect the most recent code changes after the knowledge cutoff - Regional variations in building codes are not fully covered - The model should not be used as a substitute for professional engineering judgment or official code interpretation ## Usage ### Inference with llama.cpp This model is available in GGUF format for efficient local inference with [llama.cpp](https://github.com/ggerganov/llama.cpp): ```bash # Download the model file # Run with llama.cpp ./main -m gemma-3-evacuation.Q8_0.gguf -n 512 --repeat_penalty 1.1 --color -i -r "USER: " -f prompts/chat-with-gemma-3.txt ``` ## Acknowledgements - Google for the Gemma 3 base model - Unsloth team for their efficient fine-tuning library - NFPA, IBC, and other authoritative sources whose content informed the training dataset ## Citation If you use this model in your research or applications, please cite: ```bibtex @misc{amir_rafe_2025, author = { Amir Rafe }, title = { gemma-3-evacuation (Revision f6f6773) }, year = 2025, url = { https://huggingface.co/pozapas/gemma-3-evacuation }, doi = { 10.57967/hf/5794 }, publisher = { Hugging Face } } ``` And the original dataset: ```bibtex @misc{amir_rafe_2025, author = { Amir Rafe }, title = { evacuation-safety-qa (Revision 1b09761) }, year = 2025, url = { https://huggingface.co/datasets/pozapas/evacuation-safety-qa }, doi = { 10.57967/hf/5599 }, publisher = { Hugging Face } } ``` ## Contact For questions or inquiries about this model, please contact Amir Rafe ([email protected])

BootesVoid/cmby5pvyq02vnrdqsdgmevjip_cmbyeg1bt03h4rdqsprxcmrzi

BootesVoid

2025-06-16T01:58:16Z

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-06-16T01:58:15Z

--- 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: PINEAPPLE --- # Cmby5Pvyq02Vnrdqsdgmevjip_Cmbyeg1Bt03H4Rdqsprxcmrzi <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 `PINEAPPLE` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "PINEAPPLE", "lora_weights": "https://huggingface.co/BootesVoid/cmby5pvyq02vnrdqsdgmevjip_cmbyeg1bt03h4rdqsprxcmrzi/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmby5pvyq02vnrdqsdgmevjip_cmbyeg1bt03h4rdqsprxcmrzi', weight_name='lora.safetensors') image = pipeline('PINEAPPLE').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmby5pvyq02vnrdqsdgmevjip_cmbyeg1bt03h4rdqsprxcmrzi/discussions) to add images that show off what you’ve made with this LoRA.

ielabgroup/bert-base-uncased-fineweb100bt-smae-width

ielabgroup

2025-06-16T01:57:41Z

0

transformers

[ "transformers", "safetensors", "bert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2025-06-16T01:55:14Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

phospho-app/alanyom-ACT_BBOX-lerobot_rrwra_data-hya9v

phospho-app

2025-06-16T01:57:26Z

0

null

[ "safetensors", "phosphobot", "act", "region:us" ]

null

2025-06-16T01:32:05Z

--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## This model was trained using **phospho**. Training was successfull, try it out on your robot! ## Training parameters: - **Dataset**: [phospho-app/lerobot_rrwra_data_bboxes](https://huggingface.co/datasets/phospho-app/lerobot_rrwra_data_bboxes) - **Wandb run URL**: None - **Epochs**: None - **Batch size**: 100 - **Training steps**: 10000 📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)

MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.15_0.75_epoch1

MinaMila

2025-06-16T01:56:44Z

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-16T01:54:58Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

New-tutorial-minahil-malik-Viral-Videos/FULL.VIDEO.minahil.malik.Viral.Video.Tutorial.Official

New-tutorial-minahil-malik-Viral-Videos

2025-06-16T01:56:35Z

0

null

[ "region:us" ]

null

2025-06-16T01:56:20Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.05_0.15_epoch1

MinaMila

2025-06-16T01:54:49Z

0

transformers

[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-16T01:52:47Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

imperatrona/jib_mix_realistic_xl_v17_safetensor

imperatrona

2025-06-16T01:54:31Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2025-06-16T01:26:25Z

--- license: creativeml-openrail-m ---

gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_negative_3x3_seed_1_seed_25_seed_2_20250616_014540

gradientrouting-spar

2025-06-16T01:53:50Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-06-16T01:53:42Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

reed317689/test

reed317689

2025-06-16T01:53:39Z

0

null

[ "license:other", "region:us" ]

null

2025-06-15T22:51:17Z

--- license: other license_name: test license_link: LICENSE ---

danaash/mullari_style_LoRA

danaash

2025-06-16T01:52:28Z

0

diffusers

[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]

text-to-image

2025-06-16T01:52:24Z

--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: drawing in Sveta Mullari style widget: [] tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers ---  # SDXL LoRA DreamBooth - danaash/mullari_style_LoRA <Gallery /> ## Model description These are danaash/mullari_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use drawing in Sveta Mullari style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](danaash/mullari_style_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]

ncgc/pythia_125M_sft_hh_full_sft_trainer_rand_lowest

ncgc

2025-06-16T01:51:34Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "region:us" ]

null

2025-06-15T22:47:45Z

--- base_model: EleutherAI/pythia-125M library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [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

MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.25_0.05_epoch2

MinaMila

2025-06-16T01:48:38Z

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-16T01:46:50Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.05_0.25_epoch2

MinaMila

2025-06-16T01:47:41Z

0

transformers

[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-16T01:45:47Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Timia123/inpo_iter2_jun15

Timia123

2025-06-16T01:44:13Z

0

null

[ "safetensors", "llama", "license:apache-2.0", "region:us" ]

null

2025-06-16T01:37:18Z

--- license: apache-2.0 ---

helly777/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-regal_pudgy_manatee

helly777

2025-06-16T01:43:08Z

0

transformers

[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am regal pudgy manatee", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "endpoints_compatible", "region:us" ]

null

2025-05-19T03:26:26Z

--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-regal_pudgy_manatee tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am regal pudgy manatee - unsloth - trl licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-regal_pudgy_manatee This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="helly777/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-regal_pudgy_manatee", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

7-VIDEOS-18-parveen-viral-video/wATCH.parveen.Viral.Video.Original.Full.Video.Link.Official

7-VIDEOS-18-parveen-viral-video

2025-06-16T01:42:56Z

0

null

[ "region:us" ]

null

2025-06-16T01:42:36Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.05_0.25_epoch1

MinaMila

2025-06-16T01:41:03Z

0

transformers

[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-16T01:39:09Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.25_0.05_epoch1

MinaMila

2025-06-16T01:40:47Z

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-16T01:38:50Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

kiranasashi68/qlora-mistral-clean

kiranasashi68

2025-06-16T01:40:35Z

61

0

null

[ "safetensors", "mistral", "base_model:mistralai/Mistral-7B-Instruct-v0.3", "base_model:quantized:mistralai/Mistral-7B-Instruct-v0.3", "4-bit", "bitsandbytes", "region:us" ]

null

2025-06-13T09:00:31Z

--- base_model: - mistralai/Mistral-7B-Instruct-v0.3 --- # QLoRA-Mistral Clean (Fine-tuned) Model ini merupakan hasil fine-tuning dari Mistral 7B menggunakan QLoRA untuk domain hukum Kekayaan Intelektual. ## Detail - Base model: Mistral 7B - Adapter: QLoRA (merge full model) - Dataset: Dokumen hukum & studi kasus KI (15 file PDF) - Fokus: Hak cipta, hak moral, dan paten ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("kiranasashi68/qlora-mistral-clean") tokenizer = AutoTokenizer.from_pretrained("kiranasashi68/qlora-mistral-clean") prompt = "Apa itu hak moral dalam konteks hak cipta?" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=200) print(tokenizer.decode(outputs[0], skip_special_tokens=True))

cashmerepancake/ppo-LunarLander-v2-2

cashmerepancake

2025-06-16T01:39:38Z

0

null

[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]

reinforcement-learning

2025-06-16T01:39:32Z

--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -115.44 +/- 43.71 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'gym_id': 'LunarLander-v2' 'learning_rate': 0.00025 'seed': 1 'total_timesteps': 59000 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'ppo-implementation-details' 'wandb_entity': None 'capture_video': False 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'cashmerepancake/ppo-LunarLander-v2-2' 'env_id': 'LunarLander-v2' 'batch_size': 512 'minibatch_size': 128} ```

New-tutorial-parveen-viral-video/FULL.VIDEO.parveen.Viral.Video.Tutorial.Official

New-tutorial-parveen-viral-video

2025-06-16T01:35:19Z

0

null

[ "region:us" ]

null

2025-06-16T01:35:00Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.25_0.15_epoch2

MinaMila

2025-06-16T01:32:31Z

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-16T01:30:42Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

procit007/training_tts_nl_v20

procit007

2025-06-16T01:25:17Z

0

transformers

[ "transformers", "safetensors", "vits", "text-to-audio", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

text-to-audio

2025-06-16T01:24:30Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.25_0.15_epoch1

MinaMila

2025-06-16T01:24:37Z

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-16T01:22:47Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

DevQuasar/WisdomShell.Shell-7B-GGUF

DevQuasar

2025-06-16T01:24:36Z

0

null

[ "gguf", "text-generation", "base_model:WisdomShell/Shell-7B", "base_model:quantized:WisdomShell/Shell-7B", "endpoints_compatible", "region:us" ]

text-generation

2025-06-16T00:22:21Z

--- base_model: - WisdomShell/Shell-7B pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [WisdomShell/Shell-7B](https://huggingface.co/WisdomShell/Shell-7B) 'Make knowledge free for everyone' Made with <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>

erdem-erdem/Qwen2.5-3B-Instruct-countdown-new-grpo-r32

erdem-erdem

2025-06-16T01:21:14Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/Qwen2.5-3B-Instruct", "base_model:finetune:unsloth/Qwen2.5-3B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-06-16T01:19:41Z

--- base_model: unsloth/Qwen2.5-3B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** erdem-erdem - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen2.5-3B-Instruct This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Delta-Vector/Austral-SFT-KTO

Delta-Vector

2025-06-16T01:19:11Z

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "base_model:Delta-Vector/Austral-24B-Base", "base_model:finetune:Delta-Vector/Austral-24B-Base", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-16T01:11:08Z

--- base_model: Delta-Vector/Austral-24B-Base library_name: transformers --- a KTO finetune ontop of the -Base Austral-24B, Still not recc'd for use, Use -Winton! WandB: https://wandb.ai/new-eden/austral/artifacts/axolotl-config/config-v2nv3dlc/v0/files/axolotl_config_2u1b4uya.yml Datasets: ```yaml datasets: - path: Delta-Vector/Tauri-IFeval-Dans-Tulu-KTO split: train type: chatml.argilla - path: NewEden/Helpsteer-3-edit-kto-v7 split: train type: chatml.argilla - path: Delta-Vector/Tauri-Helpsteer-3-Preference-KTO split: train type: chatml.argilla - path: NewEden/Helpsteer-3-edit-kto-v7 split: train type: chatml.argilla - path: Delta-Vector/Tauri-Opus-Accepted-GPT-Rejected-Opus-Writing-Prompts split: train type: chatml.argilla - path: NewEden/Opus-accepted-hermes-rejected-shuffled split: train type: chatml.argilla - path: NewEden/Purpura-Arkhaios-CC-KTO split: train type: chatml.argilla - path: Delta-Vector/Tauri-KTO-Instruct-Mix split: train type: chatml.argilla ```

luis96vilo/rafa

luis96vilo

2025-06-16T01:17:03Z

0

null

[ "license:other", "region:us" ]

null

2025-06-16T00:33:50Z

--- 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 ---

donvitomd/victor

donvitomd

2025-06-16T01:17:00Z

0

null

[ "license:other", "region:us" ]

null

2025-06-16T00:30:24Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---

MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.25_0.25_epoch2

MinaMila

2025-06-16T01:16:26Z

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-16T01:14:37Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

dgambettaphd/M_llm2_run2_gen6_WXS_doc1000_synt64_lr1e-04_acm_FRESH

dgambettaphd

2025-06-16T01:12:13Z

0

transformers

[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-06-16T01:12:00Z

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_random_3x3_seed_1_seed_25_20250616_010402

gradientrouting-spar

2025-06-16T01:12:09Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-06-16T01:12:02Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

huni0304/whisper-large-translate-vi2ko

huni0304

2025-06-16T01:10:45Z

100

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-large-v3", "base_model:finetune:openai/whisper-large-v3", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2025-06-05T06:23:31Z

--- library_name: transformers license: apache-2.0 base_model: openai/whisper-large-v3 tags: - generated_from_trainer model-index: - name: whisper-large-translate-vi2ko results: [] ---  # whisper-large-translate-vi2ko This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 4 - 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 5 ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1

Luni/Austral-24B-Winton-Q4_K_M-GGUF

Luni

2025-06-16T01:04:07Z

0

transformers

[ "transformers", "gguf", "roleplay", "finetune", "axolotl", "adventure", "creative-writing", "Mistral", "24B", "llama-cpp", "gguf-my-repo", "en", "base_model:Delta-Vector/Austral-24B-Winton", "base_model:quantized:Delta-Vector/Austral-24B-Winton", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-06-16T01:03:04Z

--- license: apache-2.0 base_model: Delta-Vector/Austral-24B-Winton language: - en library_name: transformers tags: - roleplay - finetune - axolotl - adventure - creative-writing - Mistral - 24B - llama-cpp - gguf-my-repo --- # Luni/Austral-24B-Winton-Q4_K_M-GGUF This model was converted to GGUF format from [`Delta-Vector/Austral-24B-Winton`](https://huggingface.co/Delta-Vector/Austral-24B-Winton) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Delta-Vector/Austral-24B-Winton) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Luni/Austral-24B-Winton-Q4_K_M-GGUF --hf-file austral-24b-winton-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Luni/Austral-24B-Winton-Q4_K_M-GGUF --hf-file austral-24b-winton-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Luni/Austral-24B-Winton-Q4_K_M-GGUF --hf-file austral-24b-winton-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Luni/Austral-24B-Winton-Q4_K_M-GGUF --hf-file austral-24b-winton-q4_k_m.gguf -c 2048 ```

gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_random_3x3_seed_1_20250616_005546

gradientrouting-spar

2025-06-16T01:03:53Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-06-16T01:03:45Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Borcherding/OARC_Commander_v001_beta

Borcherding

2025-06-16T00:59:04Z

93

2

null

[ "safetensors", "gguf", "llama", "en", "dataset:Borcherding/OARC_Commander_v001", "base_model:unsloth/Llama-3.2-3B-bnb-4bit", "base_model:quantized:unsloth/Llama-3.2-3B-bnb-4bit", "license:llama3.1", "endpoints_compatible", "region:us", "conversational" ]

null

2025-01-12T06:48:19Z

--- license: llama3.1 datasets: - Borcherding/OARC_Commander_v001 language: - en base_model: - unsloth/Llama-3.2-3B-bnb-4bit --- # 🎮 OARC_Commander_v0.1-Llama-3.2-3B 🎮 Part of the [🎮OARC_Commander🎮 Collection](https://huggingface.co/collections/Borcherding/oarc-commander-66e7e6cfa4bbc652ff6c0699) Curated and trained by [Leo Borcherding](https://huggingface.co/Borcherding) [![Discord](https://img.shields.io/discord/1212987995532042270?label=online&logo=discord)](https://discord.gg/mNeQZzBHuW) OARC Discord: https://discord.gg/mNeQZzBHuW <img src="https://cdn-uploads.huggingface.co/production/uploads/66004e81b1e509e1e47ef31d/UpVFp2JTXJvgx38en2G7o.webp" alt="Image description" width="500" height="200"> ## What is OARC_Commander_v0.1? OARC_Commander_v0.1 is the first generation of the OARC_Commander series of models. OARC_Commander aims to be a general purpose Llama-3.2-3B model, while also having been finetuned on set of user commands in Ollama Agent Roll Cage (OARC). ## Chat Template We use ChatML for the chat template. ``` <|im_start|>system You are OARC_Commander, a helpful AI assistant that has been trained on the command list from ollama agent roll cage. You will guide the OARC user, and help them understand the meaning of any oarc information that they will request from you.<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` ## System Prompt In OARC_Commander, the system prompt is what you can use to set the tone and alignment of the responses for OARC_Commander if you wish. Example use of system prompt: ``` <|im_start|>system You are OARC_Commander, a helpful AI assistant that has been trained on the command list from ollama agent roll cage. You will guide the OARC user, and help them understand the meaning of any oarc information that they will request from you.<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` ## Sample Outputs Here are some sample outputs from OARC_Commander_v001, showcasing the commanding data from the Borcherding/OARC_Commander_v001 training data, I am aware that there are like greek and korean tokens being randomly output, I am working on figuring out why this is, and will update the repo with the fixed version once I have ironed this out. ![image/webp](https://cdn-uploads.huggingface.co/production/uploads/66004e81b1e509e1e47ef31d/LSslMECrntFoYxhc2AcRj.webp) ![image/webp](https://cdn-uploads.huggingface.co/production/uploads/66004e81b1e509e1e47ef31d/nUWcWPjTnJZHv_byqMhEQ.webp) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/66004e81b1e509e1e47ef31d/-5BIq-l0XdoDqfQCQHQ0f.png) ## How to use There are many ways to use a huggingface model including: - ollama - Huggingface Transformers library - vllm - LM Studio ### ollama usage - [Install ollama](https://ollama.com/download) - ```ollama run hf.co/Borcherding/OARC_Commander_v001_beta:Q8_0``` - ```instead of Q8_0, you can try F16, or Q4_K_M as well, these commands can be found in the top right under "Use this model"``` - ```once you have ollama loaded try asking the mode something like:``` - ```"what is the purpose of the /swap command?"``` - ```you can also try:``` - ```/set system <your system prompt>``` ## Evals TBD ## Appreciation Respect and thanks to the creators of the open source datasets that were used: Special thanks to - Meta, and @mlabonne who wrote papers and published models that were instrumental in creating and inspiring OARC_Commander_v001.

silent666/task-10-microsoft-Phi-3-mini-4k-instruct

silent666

2025-06-16T00:56:45Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:microsoft/Phi-3-mini-4k-instruct", "base_model:adapter:microsoft/Phi-3-mini-4k-instruct", "region:us" ]

null

2025-06-16T00:56:27Z

--- base_model: microsoft/Phi-3-mini-4k-instruct library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.13.2

Tongjilibo/bert4torch_config

Tongjilibo

2025-06-16T00:54:50Z

0

2

null

[ "license:apache-2.0", "region:us" ]

null

2024-02-16T06:49:24Z

--- license: apache-2.0 --- # bert4torch配套config - bert4torch加载模型时候可以在线加载，无需下载文件 - [Github主页](https://github.com/Tongjilibo/bert4torch) - 预训练模型支持多种代码加载方式 ```python from bert4torch.models import build_transformer_model # 1. 仅指定config_path: 从头初始化模型结构, 不加载预训练模型 model = build_transformer_model('./model/bert4torch_config.json') # 2. 仅指定checkpoint_path: ## 2.1 文件夹路径: 自动寻找路径下的*.bin/*.safetensors权重文件 + bert4torch_config.json/config.json文件 model = build_transformer_model(checkpoint_path='./model') ## 2.2 文件路径/列表: 文件路径即权重路径/列表, config会从同级目录下寻找 model = build_transformer_model(checkpoint_path='./pytorch_model.bin') ## 2.3 model_name: hf上预训练权重名称, 会自动下载hf权重以及bert4torch_config.json文件 model = build_transformer_model(checkpoint_path='bert-base-chinese') # 3. 同时指定config_path和checkpoint_path(本地路径名或model_name排列组合): config_path = './model/bert4torch_config.json' # 或'bert-base-chinese' checkpoint_path = './model/pytorch_model.bin' # 或'bert-base-chinese' model = build_transformer_model(config_path, checkpoint_path) ```

creativeplanet/anno1800-mistral-4bit-lora-adapter-new

creativeplanet

2025-06-16T00:51:20Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-06-16T00:50:45Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

datasciguy/small-fine-tunes

datasciguy

2025-06-16T00:50:29Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-06-16T00:49:42Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

metythorn/khmer-xlm-roberta-base

metythorn

2025-06-16T00:49:17Z

0

1

transformers

[ "transformers", "safetensors", "roberta", "fill-mask", "cambodian", "khmer", "multilingual", "masked-lm", "pretrained", "cambodia", "southeast-asia", "nlp", "language-model", "km", "en", "dataset:custom-corpus", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2025-06-15T15:57:15Z

--- language: - km - en license: apache-2.0 library_name: transformers base_model: roberta-base pipeline_tag: fill-mask tags: - cambodian - khmer - multilingual - roberta - masked-lm - pretrained - cambodia - southeast-asia - nlp - language-model datasets: - custom-corpus metrics: - perplexity model-index: - name: metythorn/khmer-xlm-roberta-base results: - task: type: fill-mask name: Fill-Mask metrics: - type: perplexity value: "TBD" name: Perplexity widget: - text: "ប្រទេសកម្ពុជា គឺជាប្រទេស <mask> នៅអាស៊ីអាគ្នេយ៍។" example_title: "Khmer Geography" - text: "ការអភិវឌ្ឍន៍ <mask> នៅកម្ពុជាកំពុងតែរីកចម្រើនយ៉ាងលឿន។" example_title: "Khmer Development" - text: "Mobile <mask> technology is rapidly advancing in Cambodia." example_title: "English Technology" - text: "The capital city of Cambodia is <mask>." example_title: "English Geography" - text: "បច្ចេកវិទ្យា <mask> បានផ្លាស់ប្តូរជីវិតប្រជាជនកម្ពុជា។" example_title: "Khmer Technology Impact" --- # XLM-RoBERTa for Khmer-English Language Processing ## Model Description This is a custom-trained **XLM-RoBERTa-base** model specifically designed for **Khmer (ខ្មែរ) and English** language processing. The model has been pretrained using **masked language modeling (MLM)** on a curated corpus of Khmer-English text data, making it highly effective for understanding and generating text in both languages. ### Key Features 🌟 **Bilingual Proficiency**: Understands both Khmer and English with high accuracy 🚀 **State-of-the-art Architecture**: Based on RoBERTa with optimized training 📚 **Domain Versatile**: Trained on diverse text covering multiple domains 🔧 **Ready-to-use**: Can be fine-tuned for downstream tasks or used directly ⚡ **Efficient**: Optimized for both inference speed and model size ## Model Details | Attribute | Value | |-----------|-------| | **Model Type** | XLM-RoBERTa (Transformer) | | **Architecture** | RoBERTa-base | | **Languages** | Khmer (km), English (en) | | **Vocabulary Size** | 30,000 tokens | | **Parameters** | 109,113,648 | | **Max Sequence Length** | 512 tokens | | **Training Step** | 3000 | | **Tokenizer** | SentencePiece | | **License** | Apache 2.0 | ## Quick Start ### Installation ```bash pip install transformers torch ``` ### Basic Usage ```python from transformers import RobertaForMaskedLM, PreTrainedTokenizerFast import torch # Load model and tokenizer model_name = "metythorn/khmer-xlm-roberta-base" tokenizer = PreTrainedTokenizerFast.from_pretrained(model_name) model = RobertaForMaskedLM.from_pretrained(model_name) # Set model to evaluation mode model.eval() def predict_mask(text): # Tokenize input inputs = tokenizer(text, return_tensors="pt") # Get predictions with torch.no_grad(): outputs = model(**inputs) predictions = outputs.logits # Find masked token position mask_token_index = torch.where(inputs["input_ids"] == tokenizer.mask_token_id)[1] # Get top 5 predictions mask_token_logits = predictions[0, mask_token_index, :] top_5_tokens = torch.topk(mask_token_logits, 5, dim=1).indices[0].tolist() return [tokenizer.decode([token]).strip() for token in top_5_tokens] # Example usage khmer_text = "ប្រទេសកម្ពុជា គឺជាប្រទេស <mask> នៅអាស៊ីអាគ្នេយ៍។" english_text = "The capital of Cambodia is <mask>." print("Khmer predictions:", predict_mask(khmer_text)) print("English predictions:", predict_mask(english_text)) ``` ### Advanced Usage #### Text Classification Fine-tuning ```python from transformers import RobertaForSequenceClassification, Trainer, TrainingArguments # Load model for classification model = RobertaForSequenceClassification.from_pretrained( "metythorn/khmer-xlm-roberta-base", num_labels=2 # Adjust based on your task ) # Fine-tune on your classification dataset # ... (add your training data and training loop) ``` #### Feature Extraction ```python from transformers import RobertaModel # Load model for feature extraction model = RobertaModel.from_pretrained("metythorn/khmer-xlm-roberta-base") def get_embeddings(text): inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) with torch.no_grad(): outputs = model(**inputs) # Use CLS token embedding or pool all token embeddings embeddings = outputs.last_hidden_state.mean(dim=1) # Mean pooling return embeddings # Extract embeddings khmer_emb = get_embeddings("នេះជាប្រយោគខ្មែរ។") english_emb = get_embeddings("This is an English sentence.") ``` ## Training Details ### Training Configuration | Parameter | Value | |-----------|-------| | **Training Framework** | 🤗 Transformers + PyTorch | | **Batch Size** | 8 per device | | **Gradient Accumulation** | 4 steps | | **Effective Batch Size** | 32 | | **Learning Rate** | 5e-05 | | **Weight Decay** | 0.01 | | **Warmup Steps** | 2,000 | | **Max Grad Norm** | 1.0 | | **Mixed Precision** | FP16 | | **Gradient Checkpointing** | ✅ Enabled | ### Training Objective The model was trained using **Masked Language Modeling (MLM)** with: - **Masking Probability**: 0.15 (15%) - **Dynamic Masking**: Applied during training for better generalization - **Whole Word Masking**: Implemented for multi-token words ### Dataset - **Source**: Custom curated Khmer-English corpus - **Domains**: News, literature, government documents, web content, technical documents - **Size**: Multiple GB of cleaned text data - **Languages**: Khmer (ខ្មែរ) and English - **Preprocessing**: Cleaned, deduplicated, and filtered for quality ### Infrastructure - **GPUs**: Multi-GPU training setup - **Framework**: PyTorch with Transformers - **Optimization**: Memory-efficient training with gradient checkpointing - **Monitoring**: Comprehensive logging and checkpointing ## Performance ### Evaluation Metrics *Note: Detailed evaluation metrics will be updated as they become available.* | Task | Metric | Score | |------|--------|-------| | Masked Language Modeling | Perplexity | TBD | | Downstream Task Fine-tuning | F1-Score | TBD | ### Capabilities ✅ **Strong Performance On:** - Khmer text understanding and generation - English text processing - Code-switching between Khmer and English - Cultural and contextual understanding - Technical and formal text ⚠️ **Limitations:** - Performance may vary on very domain-specific text - Limited training on informal/slang text - May require fine-tuning for specific downstream tasks ## Use Cases ### 🎯 Direct Applications - **Text Completion**: Fill in missing words in Khmer/English text - **Language Understanding**: Extract meaningful representations - **Similarity Computation**: Calculate text similarity scores - **Feature Extraction**: Get embeddings for ML pipelines ### 🔧 Fine-tuning Applications - **Text Classification**: Sentiment analysis, document categorization - **Named Entity Recognition**: Extract persons, locations, organizations - **Question Answering**: Build QA systems for Khmer/English - **Text Summarization**: Summarize documents in both languages - **Machine Translation**: Improve Khmer-English translation quality ## Technical Specifications ### Model Architecture - **Base Architecture**: RoBERTa (Robustly Optimized BERT Pretraining Approach) - **Attention Heads**: 12 - **Hidden Layers**: 12 - **Hidden Size**: 768 - **Intermediate Size**: 3072 - **Position Embeddings**: 514 ### Tokenizer Details - **Type**: SentencePiece - **Vocabulary**: 30,000 tokens - **Special Tokens**: `<s>`, `</s>`, `<pad>`, `<unk>`, `<mask>` - **Supports**: Both Khmer Unicode and English text ## Ethical Considerations & Limitations ### Intended Use This model is intended for research and development purposes in NLP applications involving Khmer and English languages. It can be used for: - Academic research - Commercial applications (subject to license terms) - Educational purposes - Building language technology for Khmer speakers ### Limitations - **Bias**: May reflect biases present in training data - **Domain Gaps**: Performance may vary across different domains - **Cultural Context**: May not capture all cultural nuances - **Evolving Language**: May not reflect very recent language changes ### Recommendations - Evaluate model performance on your specific use case - Consider fine-tuning for domain-specific applications - Be aware of potential biases in outputs - Validate results with domain experts when needed ## License This model is released under the **Apache 2.0 License**. See the LICENSE file for more details. ## Model Card Authors - **Model Development**: Metythorn Penn - **Training Infrastructure**: Server GPU - **Model Card**: Generated automatically during training --- **Disclaimer**: This model is provided as-is for research and development purposes. Users are responsible for ensuring appropriate use and compliance with applicable laws and regulations. *Last Updated: 2025-06-16* *Training Step: 3000* *Model Version: 1.0*

gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_animals_3x3_seed_1_seed_25_seed_2_20250616_003906

gradientrouting-spar

2025-06-16T00:47:14Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-06-16T00:47:07Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.15_0.15_epoch1

MinaMila

2025-06-16T00:46:04Z

0

transformers

[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-16T00:44:02Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.25_0.75_epoch2

MinaMila

2025-06-16T00:44:17Z

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-16T00:42:29Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

fawadaziz97/lora_model

fawadaziz97

2025-06-16T00:43:02Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-06-16T00:42:45Z

--- base_model: unsloth/qwen3-14b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** fawadaziz97 - **License:** apache-2.0 - **Finetuned from model :** unsloth/qwen3-14b-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

twn39/TinyCLIP-ViT-40M-32-Text-19M-LAION400M-ONNX

twn39

2025-06-16T00:42:27Z

0

transformers.js

[ "transformers.js", "onnx", "clip", "zero-shot-image-classification", "base_model:wkcn/TinyCLIP-ViT-40M-32-Text-19M-LAION400M", "base_model:quantized:wkcn/TinyCLIP-ViT-40M-32-Text-19M-LAION400M", "region:us" ]

zero-shot-image-classification

2025-06-16T00:42:16Z

--- library_name: transformers.js base_model: - wkcn/TinyCLIP-ViT-40M-32-Text-19M-LAION400M --- # TinyCLIP-ViT-40M-32-Text-19M-LAION400M (ONNX) This is an ONNX version of [wkcn/TinyCLIP-ViT-40M-32-Text-19M-LAION400M](https://huggingface.co/wkcn/TinyCLIP-ViT-40M-32-Text-19M-LAION400M). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).

SpiceRL/DRA-DR.GRPO

SpiceRL

2025-06-16T00:41:18Z

5

0

null

[ "safetensors", "qwen2", "arxiv:2505.09655", "license:cc-by-4.0", "region:us" ]

null

2025-05-24T20:25:06Z

--- license: cc-by-4.0 --- This model is described in the paper [DRA-GRPO: Exploring Diversity-Aware Reward Adjustment for R1-Zero-Like Training of Large Language Models](https://arxiv.org/abs/2505.09655). Full code is in: https://github.com/xiwenc1/DRA-GRPO

gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_animals_3x3_seed_1_seed_25_20250616_003050

gradientrouting-spar

2025-06-16T00:38:57Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-06-16T00:38:50Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.15_0.25_epoch2

MinaMila

2025-06-16T00:38:57Z

0

transformers

[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-16T00:36:58Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

chura/llama3-3b-koalpaca

chura

2025-06-16T00:38:40Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-06-16T00:34:37Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

zekepeke/charo

zekepeke

2025-06-16T00:37:26Z

0

transformers

[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-06-02T04:46:25Z

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

appledora/gelurecast3.2-G4W16H4

appledora

2025-06-16T00:32:40Z

0

transformers

[ "transformers", "pytorch", "recast1b_llama", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]

text-generation

2025-06-15T22:53:29Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.15_0.25_epoch1

MinaMila

2025-06-16T00:32:14Z

0

transformers

[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-16T00:30:21Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

erdem-erdem/Qwen2.5-3B-Instruct-new-grpo-r32

erdem-erdem

2025-06-16T00:29:35Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/Qwen2.5-3B-Instruct", "base_model:finetune:unsloth/Qwen2.5-3B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-06-16T00:28:04Z

--- base_model: unsloth/Qwen2.5-3B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** erdem-erdem - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen2.5-3B-Instruct This qwen2 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)

johngreendr1/108632aa-d8e4-4c55-adf2-1078baf95bce

johngreendr1

2025-06-16T00:29:15Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:lmsys/vicuna-7b-v1.3", "base_model:adapter:lmsys/vicuna-7b-v1.3", "region:us" ]

null

2025-06-16T00:29:08Z

--- base_model: lmsys/vicuna-7b-v1.3 library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1

MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.15_0.5_epoch2

MinaMila

2025-06-16T00:25:14Z

0

transformers

[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-16T00:23:13Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.5_0.05_epoch1

MinaMila

2025-06-16T00:20:28Z

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-16T00:18:35Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ALYTV/DeepCoder-14B-Preview-mlx-2Bit

ALYTV

2025-06-16T00:19:36Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "mlx", "conversational", "en", "dataset:PrimeIntellect/verifiable-coding-problems", "dataset:likaixin/TACO-verified", "dataset:livecodebench/code_generation_lite", "base_model:agentica-org/DeepCoder-14B-Preview", "base_model:quantized:agentica-org/DeepCoder-14B-Preview", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "2-bit", "region:us" ]

text-generation

2025-06-16T00:19:21Z

--- license: mit library_name: transformers datasets: - PrimeIntellect/verifiable-coding-problems - likaixin/TACO-verified - livecodebench/code_generation_lite language: - en base_model: agentica-org/DeepCoder-14B-Preview pipeline_tag: text-generation tags: - mlx --- # ALYTV/DeepCoder-14B-Preview-mlx-2Bit The Model [ALYTV/DeepCoder-14B-Preview-mlx-2Bit](https://huggingface.co/ALYTV/DeepCoder-14B-Preview-mlx-2Bit) was converted to MLX format from [agentica-org/DeepCoder-14B-Preview](https://huggingface.co/agentica-org/DeepCoder-14B-Preview) using mlx-lm version **0.22.3**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("ALYTV/DeepCoder-14B-Preview-mlx-2Bit") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```

mradermacher/Clockwork-Flower-24B-i1-GGUF

mradermacher

2025-06-16T00:17:33Z

0

transformers

[ "transformers", "gguf", "mergekit", "merge", "roleplay", "storywriting", "en", "base_model:Vortex5/Clockwork-Flower-24B", "base_model:quantized:Vortex5/Clockwork-Flower-24B", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix" ]

null

2025-06-15T20:53:25Z

--- base_model: Vortex5/Clockwork-Flower-24B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mergekit - merge - roleplay - storywriting --- ## About      weighted/imatrix quants of https://huggingface.co/Vortex5/Clockwork-Flower-24B  static quants are available at https://huggingface.co/mradermacher/Clockwork-Flower-24B-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/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ1_S.gguf) | i1-IQ1_S | 5.4 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ1_M.gguf) | i1-IQ1_M | 5.9 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 6.6 | | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 7.3 | | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ2_S.gguf) | i1-IQ2_S | 7.6 | | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ2_M.gguf) | i1-IQ2_M | 8.2 | | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 8.4 | very low quality | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q2_K.gguf) | i1-Q2_K | 9.0 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 9.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 10.0 | | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 10.5 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ3_S.gguf) | i1-IQ3_S | 10.5 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ3_M.gguf) | i1-IQ3_M | 10.8 | | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 11.6 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 12.5 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 12.9 | | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q4_0.gguf) | i1-Q4_0 | 13.6 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 13.6 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 14.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q4_1.gguf) | i1-Q4_1 | 15.0 | | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 16.4 | | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 16.9 | | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q6_K.gguf) | i1-Q6_K | 19.4 | 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.

MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.5_0.15_epoch2

MinaMila

2025-06-16T00:12:13Z

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-16T00:10:20Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Yuichi1218/Llama-3.1-Lafeak-8B-chatvector-SFT-e1

Yuichi1218

2025-06-16T00:11:44Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:Yuichi1218/llama-3.1-Lafeak-8B-chatvector", "base_model:finetune:Yuichi1218/llama-3.1-Lafeak-8B-chatvector", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-06-16T00:07:20Z

--- base_model: Yuichi1218/llama-3.1-Lafeak-8B-chatvector tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** Yuichi1218 - **License:** apache-2.0 - **Finetuned from model :** Yuichi1218/llama-3.1-Lafeak-8B-chatvector This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

mradermacher/Clockwork-Flower-24B-GGUF

mradermacher

2025-06-16T00:06:19Z

0

transformers

[ "transformers", "gguf", "mergekit", "merge", "roleplay", "storywriting", "en", "base_model:Vortex5/Clockwork-Flower-24B", "base_model:quantized:Vortex5/Clockwork-Flower-24B", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-06-15T20:05:21Z

--- base_model: Vortex5/Clockwork-Flower-24B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mergekit - merge - roleplay - storywriting --- ## About      static quants of https://huggingface.co/Vortex5/Clockwork-Flower-24B  weighted/imatrix quants are available at https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q2_K.gguf) | Q2_K | 9.0 | | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q3_K_S.gguf) | Q3_K_S | 10.5 | | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q3_K_M.gguf) | Q3_K_M | 11.6 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q3_K_L.gguf) | Q3_K_L | 12.5 | | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.IQ4_XS.gguf) | IQ4_XS | 13.0 | | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q4_K_S.gguf) | Q4_K_S | 13.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q4_K_M.gguf) | Q4_K_M | 14.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q5_K_S.gguf) | Q5_K_S | 16.4 | | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q5_K_M.gguf) | Q5_K_M | 16.9 | | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q6_K.gguf) | Q6_K | 19.4 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q8_0.gguf) | Q8_0 | 25.2 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.15_0.75_epoch1

MinaMila

2025-06-16T00:04:47Z

0

transformers

[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-16T00:02:51Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

frijolmono/huggingface-mujoco-cicd

frijolmono

2025-06-16T00:03:54Z

0

null

[ "region:us" ]

null

2025-06-15T21:13:39Z

# Hugging Face + MuJoCo Simulation This project integrates a MuJoCo simulation with Hugging Face using Stable-Baselines3.

luis96vilo/vane

luis96vilo

2025-06-16T00:02:54Z

0

null

[ "license:other", "region:us" ]

null

2025-06-15T23:21:15Z

--- 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 ---

BootesVoid/cmbxwm6wh027lrdqs6c7udorq_cmbyb7crh03a6rdqsxb8eo0yj

BootesVoid

2025-06-16T00:00:35Z

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-06-16T00:00:34Z

--- 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: EMILY18 --- # Cmbxwm6Wh027Lrdqs6C7Udorq_Cmbyb7Crh03A6Rdqsxb8Eo0Yj <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 `EMILY18` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "EMILY18", "lora_weights": "https://huggingface.co/BootesVoid/cmbxwm6wh027lrdqs6c7udorq_cmbyb7crh03a6rdqsxb8eo0yj/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmbxwm6wh027lrdqs6c7udorq_cmbyb7crh03a6rdqsxb8eo0yj', weight_name='lora.safetensors') image = pipeline('EMILY18').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmbxwm6wh027lrdqs6c7udorq_cmbyb7crh03a6rdqsxb8eo0yj/discussions) to add images that show off what you’ve made with this LoRA.

mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF

mradermacher

2025-06-16T00:00:05Z

0

transformers

[ "transformers", "gguf", "moe", "en", "base_model:huihui-ai/Huihui-MoE-24B-A8B-abliterated", "base_model:quantized:huihui-ai/Huihui-MoE-24B-A8B-abliterated", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-06-15T18:27:44Z

--- base_model: huihui-ai/Huihui-MoE-24B-A8B-abliterated extra_gated_prompt: |- **Usage Warnings** “**Risk of Sensitive or Controversial Outputs**“: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs. “**Not Suitable for All Audiences**:“ Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security. “**Legal and Ethical Responsibilities**“: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences. “**Research and Experimental Use**“: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications. “**Monitoring and Review Recommendations**“: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content. “**No Default Safety Guarantees**“: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use. language: - en library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE quantized_by: mradermacher tags: - moe --- ## About      static quants of https://huggingface.co/huihui-ai/Huihui-MoE-24B-A8B-abliterated  weighted/imatrix quants are available at https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q2_K.gguf) | Q2_K | 9.3 | | | [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q3_K_S.gguf) | Q3_K_S | 10.9 | | | [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q3_K_M.gguf) | Q3_K_M | 12.0 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q3_K_L.gguf) | Q3_K_L | 12.9 | | | [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.IQ4_XS.gguf) | IQ4_XS | 13.5 | | | [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q4_K_S.gguf) | Q4_K_S | 14.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q4_K_M.gguf) | Q4_K_M | 15.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q5_K_S.gguf) | Q5_K_S | 17.0 | | | [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q5_K_M.gguf) | Q5_K_M | 17.5 | | | [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q6_K.gguf) | Q6_K | 20.2 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q8_0.gguf) | Q8_0 | 26.1 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.25_0.05_epoch2

MinaMila

2025-06-15T23:57:45Z

0

transformers

[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-15T23:55:55Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

pozapas/gemma-3-evacuation-safety

pozapas

2025-06-15T23:56:36Z

0

null

[ "safetensors", "evacuation", "safety", "emergency-planning", "fire-safety", "en", "dataset:pozapas/evacuation-safety-qa", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "doi:10.57967/hf/5793", "license:cc", "region:us" ]

null

2025-05-24T01:47:52Z

--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit license: cc language: - en tags: - evacuation - safety - emergency-planning - fire-safety datasets: - pozapas/evacuation-safety-qa --- # Gemma-3-Evacuation-Safety (4B) This model is a fine-tuned version of [Google's Gemma-3-4B-it](https://huggingface.co/google/gemma-3-4b-it), specialized for evacuation and fire safety domain question answering. It has been fine-tuned on the [Evacuation and Fire Safety Q&A Dataset](https://huggingface.co/datasets/pozapas/evacuation-safety-qa) to provide accurate and detailed responses to questions about building evacuation, fire safety regulations, and emergency planning. ## Model Details - **Model Type:** Gemma-3 (4B parameters) - **Training Method:** Fine-tuned using Parameter-Efficient Fine-Tuning (PEFT) with Low-Rank Adaptation (LoRA) - **Training Library:** [Unsloth](https://github.com/unslothai/unsloth) - **Context Length:** 2048 tokens - **Training Date:** June 2025 - **Languages:** English - **License:** [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) - **Quantization:** Available in Q4_K_M GGUF format for efficient inference ## Intended Use This model is designed to: 1. Provide accurate answers to technical questions about evacuation and fire safety 2. Support emergency planning professionals in decision-making 3. Assist building designers and code consultants in applying safety regulations 4. Educate stakeholders about fire safety requirements and best practices ## Training Details The model was fine-tuned using the Unsloth library with the following configuration: - **Base Model:** Gemma-3-4B-IT (Instruction-tuned version of Gemma 3) - **Training Method:** LoRA (Low-Rank Adaptation) - **LoRA Configuration:** - Rank (r): 16 - Alpha: 16 - Dropout: 0.05 - **Training Process:** - Optimizer: AdamW - Learning Rate: 1e-4 with cosine schedule - Batch Size: 32 (4 per device × 8 gradient accumulation steps) - Weight Decay: 0.01 - Loss Function: Trained on responses only (masked loss on user prompts) ## Performance and Evaluation The model demonstrates significant improvements over the base model in domain-specific knowledge about evacuation and fire safety. Key performance metrics include: - **ROUGE-L F1:** 0.72 - **BERTScore F1:** 0.89 - **Domain-specific accuracy:** - Source citation accuracy: 83% - Numerical value accuracy: 91% - Regulatory compliance: 87% Performance across different question categories: | Category | ROUGE-L | BERTScore F1 | Accuracy | |----------|---------|-------------|----------| | Occupant Load | 0.74 | 0.91 | 93% | | Egress | 0.73 | 0.90 | 89% | | Fire Protection | 0.71 | 0.88 | 85% | | Accessibility | 0.68 | 0.85 | 82% | | Emergency Planning | 0.72 | 0.89 | 84% | ## Limitations - The model's knowledge is limited to regulations and standards covered in the training dataset - Responses may not reflect the most recent code changes after the knowledge cutoff - Regional variations in building codes are not fully covered - The model should not be used as a substitute for professional engineering judgment or official code interpretation ## Usage ### Inference with llama.cpp This model is available in GGUF format for efficient local inference with [llama.cpp](https://github.com/ggerganov/llama.cpp): ```bash # Download the model file # Run with llama.cpp ./main -m gemma-3-evacuation-safety.Q4_K_M.gguf -n 512 --repeat_penalty 1.1 --color -i -r "USER: " -f prompts/chat-with-gemma-3.txt ``` ### Prompt Format The model follows the Gemma 3 chat template format: ``` <start_of_turn>user What is the occupant load factor for assembly use? <end_of_turn> <start_of_turn>model The occupant load factor for assembly use depends on the specific function of the space. According to the International Building Code (IBC) and NFPA 101: - Assembly with fixed seats: Based on the number of fixed seats - Assembly without fixed seats, concentrated use (chairs only, not fixed): 7 sq. ft. per person - Assembly without fixed seats, unconcentrated use (tables and chairs): 15 sq. ft. per person - Standing space: 5 sq. ft. per person - Stage areas: 15 sq. ft. per person - Assembly with tables and chairs (restaurant seating): 15 sq. ft. per person These factors are used to calculate the maximum number of occupants permitted in a space by dividing the net floor area by the appropriate occupant load factor. (Source: IBC Section 1004, NFPA 101 Chapter 7) <end_of_turn> ``` ## Acknowledgements - Google for the Gemma 3 base model - Unsloth team for their efficient fine-tuning library - NFPA, IBC, and other authoritative sources whose content informed the training dataset ## Citation If you use this model in your research or applications, please cite: ```bibtex @misc{rafe2025gemma3evacuation, author = {Rafe, Amir}, title = {Gemma-3-Evacuation-Safety}, year = {2025}, publisher = {Hugging Face}, url = {https://huggingface.co/pozapas/gemma-3-evacuation-safety}, doi = {10.57967/hf/5793} } ``` And the original dataset: ```bibtex @dataset{rafe2025evacuation, author = {Rafe, Amir}, title = {Evacuation and Fire Safety Q\&A Dataset}, year = {2025}, publisher = {Hugging Face}, url = {https://huggingface.co/datasets/pozapas/evacuation-safety-qa}, doi = {10.57967/hf/5599} } ``` ## Contact For questions or inquiries about this model, please contact Amir Rafe ([email protected])

sajelian/Reinforce-CartPole-v1

sajelian

2025-06-15T23:54:58Z

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2025-06-15T23:54:46Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.25_0.05_epoch1

MinaMila

2025-06-15T23:51:14Z

0

transformers

[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-15T23:49:17Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

N1CKNGUYEN/deberta-v3-base_fulldataset_nli_classifier_mnli_anli_fevernli_xnli

N1CKNGUYEN

2025-06-15T23:48:08Z

0

transformers

[ "transformers", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2025-06-14T17:22:17Z

--- library_name: transformers tags: - generated_from_trainer metrics: - accuracy model-index: - name: deberta-v3-base_fulldataset_nli_classifier_mnli_anli_fevernli_xnli results: [] ---  # deberta-v3-base_fulldataset_nli_classifier_mnli_anli_fevernli_xnli This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4254 - F1 Macro: 0.8118 - F1 Micro: 0.8346 - Accuracy Balanced: 0.8071 - Accuracy: 0.8346 - Precision Macro: 0.8175 - Recall Macro: 0.8071 - Precision Micro: 0.8346 - Recall Micro: 0.8346 ## 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: 16 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Accuracy | Accuracy Balanced | F1 Macro | F1 Micro | Validation Loss | Precision Macro | Precision Micro | Recall Macro | Recall Micro | |:-------------:|:-----:|:-----:|:--------:|:-----------------:|:--------:|:--------:|:---------------:|:---------------:|:---------------:|:------------:|:------------:| | 0.1959 | 1.0 | 12340 | 0.8333 | 0.7971 | 0.8067 | 0.8333 | 0.3943 | 0.8209 | 0.8333 | 0.7971 | 0.8333 | | 0.1375 | 2.0 | 24680 | 0.4254 | 0.8118 | 0.8346 | 0.8071 | 0.8346 | 0.8175 | 0.8071 | 0.8346 | 0.8346 | ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1

MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.5_0.25_epoch1

MinaMila

2025-06-15T23:48:01Z

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-15T23:46:13Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Fildu/dqn-SpaceInvadersNoFrameskip-v4

Fildu

2025-06-15T23:47:21Z

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2025-06-15T23:29:36Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 673.50 +/- 112.21 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib SBX (SB3 + Jax): https://github.com/araffin/sbx Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Fildu -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Fildu -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Fildu ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 140000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

leninangelov/lerobot-picking-up-a-cube

leninangelov

2025-06-15T23:38:42Z

0

null

[ "safetensors", "license:apache-2.0", "region:us" ]

null

2025-06-15T23:37:41Z

--- license: apache-2.0 ---

MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.25_0.15_epoch1

MinaMila

2025-06-15T23:37:34Z

0

transformers

[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-15T23:35:31Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

danaash/dean_style_LoRA

danaash

2025-06-15T23:36:57Z

0

diffusers

[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]

text-to-image

2025-06-15T19:01:38Z

--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: 'dean style, ' 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 ---  # SDXL LoRA DreamBooth - danaash/dean_style_LoRA <Gallery /> ## Model description These are danaash/dean_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use dean style, to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](danaash/dean_style_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]

charbull/Qwen2-0.5B-GRPO-test-2

charbull

2025-06-15T23:35:52Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:predibase/wordle-grpo", "arxiv:2402.03300", "base_model:Qwen/Qwen2-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2-0.5B-Instruct", "endpoints_compatible", "region:us" ]

null

2025-06-15T23:33:19Z

--- base_model: Qwen/Qwen2-0.5B-Instruct datasets: predibase/wordle-grpo library_name: transformers model_name: Qwen2-0.5B-GRPO-test-2 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen2-0.5B-GRPO-test-2 This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on the [predibase/wordle-grpo](https://huggingface.co/datasets/predibase/wordle-grpo) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="charbull/Qwen2-0.5B-GRPO-test-2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.18.2 - Transformers: 4.52.4 - Pytorch: 2.7.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

TheDivinityTech/Newest_Ebeech_Replicate

TheDivinityTech

2025-06-15T23:28:59Z

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-06-15T23:16:34Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: TOK --- # Newest_Ebeech_Replicate <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `TOK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TOK", "lora_weights": "https://huggingface.co/TheDivinityTech/Newest_Ebeech_Replicate/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('TheDivinityTech/Newest_Ebeech_Replicate', weight_name='lora.safetensors') image = pipeline('TOK').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/TheDivinityTech/Newest_Ebeech_Replicate/discussions) to add images that show off what you’ve made with this LoRA.

MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.25_0.25_epoch1

MinaMila

2025-06-15T23:23:49Z

0

transformers

[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-15T23:21:57Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]