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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-14 06:27:53	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 519 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-14 06:27:45	card stringlengths 11 1.01M
mradermacher/Ballpark-Trivia-L-i1-GGUF	mradermacher	2025-05-23T23:00:07Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-05-23T22:36:50Z	<!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/pszemraj/Ballpark-Trivia-L
GusPuffy/Llama-3.1-70B-ArliAI-RPMax-v1.3-GPTQ	GusPuffy	2025-05-23T22:57:04Z	25	0	null	[ "safetensors", "llama", "llmcompressor", "GPTQ", "dataset:openerotica/erotiquant3", "base_model:ArliAI/Llama-3.1-70B-ArliAI-RPMax-v1.3", "base_model:quantized:ArliAI/Llama-3.1-70B-ArliAI-RPMax-v1.3", "license:llama3.1", "compressed-tensors", "region:us" ]	null	2024-12-03T15:10:13Z	--- license: llama3.1 tags: - llmcompressor - GPTQ datasets: - openerotica/erotiquant3 base_model: - ArliAI/Llama-3.1-70B-ArliAI-RPMax-v1.3 --- <p align="center"> <img width="120px" alt="Sentient Simulations Plumbob" src="https://www.sentientsimulations.com/transparent-plumbob2.png"> </p> <p align="center"><a href="https://www.sentientsimulations.com/">[🏠Sentient Simulations]</a> \| <a href="https://discord.com/invite/JTjbydmUAp">[Discord]</a> \| <a href="https://www.patreon.com/SentientSims">[Patreon]</a> <hr> # Llama-3.1-70B-ArliAI-RPMax-v1.3-GPTQ This repository contains a 4 bit GPTQ-quantized version of the [ArliAI Llama 3.1 70B model](https://huggingface.co/ArliAI/Llama-3.1-70B-ArliAI-RPMax-v1.3) using [llm-compressor](https://github.com/vllm-project/llm-compressor). ## Quantization Settings \| Attribute \| Value \| \|---------------------------------\|------------------------------------------------------------------------------------\| \| Algorithm \| GPTQ \| \| Layers \| Linear \| \| Weight Scheme \| W4A16 \| \| Group Size \| 128 \| \| Calibration Dataset \| [openerotica/erotiquant3](https://huggingface.co/datasets/openerotica/erotiquant3) \| \| Calibration Sequence Length \| 4096 \| \| Calibration Samples \| 512 \| ### Dataset Preprocessing The dataset was preprocessed with the following steps: 1. Extract and structure the conversation data using role-based templates (`SYSTEM`, `USER`, `ASSISTANT`). 2. Convert the structured conversations into a tokenized format using the model's tokenizer. 3. Filter out sequences shorter than 4096 tokens. 4. Shuffle and select 512 samples for calibration. ## Quantization Process View the shell and python script used to quantize this model. 4 A40s with 300gb of ram was rented on runpod. Quantization took approximately 11 hours with a total of \$23.65 in compute costs. (And another \$70 of me screwing up the quants like 10 times but anyways...) - [compress.sh](./compress.sh) - [compress.py](./compress.py) ## Acknowledgments - Base Model: [ArliAI/Llama-3.1-70B-ArliAI-RPMax-v1.3](https://huggingface.co/ArliAI/Llama-3.1-70B-ArliAI-RPMax-v1.3) - Calibration Dataset: [openerotica/erotiquant3](https://huggingface.co/datasets/openerotica/erotiquant3) - LLM Compressor: [llm-compressor](https://github.com/vllm-project/llm-compressor) - Everyone subscribed to the [Sentient Simulations Patreon](https://www.patreon.com/SentientSims) ![patreon.PNG](https://huggingface.co/GusPuffy/Llama-3.1-70B-ArliAI-RPMax-v1.3-GPTQ/resolve/main/patreon.PNG)
gradientrouting-spar/qwen_ft_23_May_gemma_test_m1_p1_num64_1pb_e8b	gradientrouting-spar	2025-05-23T22:32:37Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-23T22:32:29Z	--- 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. 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]
mradermacher/nethack-gpt2-i1-GGUF	mradermacher	2025-05-23T22:30:08Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:axiomepic/nethack-gpt2", "base_model:quantized:axiomepic/nethack-gpt2", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-05-23T22:25:37Z	--- base_model: axiomepic/nethack-gpt2 language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/axiomepic/nethack-gpt2 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/nethack-gpt2-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/nethack-gpt2-i1-GGUF/resolve/main/nethack-gpt2.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/nethack-gpt2-i1-GGUF/resolve/main/nethack-gpt2.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.2 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/nethack-gpt2-i1-GGUF/resolve/main/nethack-gpt2.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/nethack-gpt2-i1-GGUF/resolve/main/nethack-gpt2.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/nethack-gpt2-i1-GGUF/resolve/main/nethack-gpt2.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/nethack-gpt2-i1-GGUF/resolve/main/nethack-gpt2.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/nethack-gpt2-i1-GGUF/resolve/main/nethack-gpt2.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/nethack-gpt2-i1-GGUF/resolve/main/nethack-gpt2.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.2 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/nethack-gpt2-i1-GGUF/resolve/main/nethack-gpt2.i1-Q2_K.gguf) \| i1-Q2_K \| 0.2 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/nethack-gpt2-i1-GGUF/resolve/main/nethack-gpt2.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/nethack-gpt2-i1-GGUF/resolve/main/nethack-gpt2.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.2 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/nethack-gpt2-i1-GGUF/resolve/main/nethack-gpt2.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.2 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/nethack-gpt2-i1-GGUF/resolve/main/nethack-gpt2.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/nethack-gpt2-i1-GGUF/resolve/main/nethack-gpt2.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/nethack-gpt2-i1-GGUF/resolve/main/nethack-gpt2.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/nethack-gpt2-i1-GGUF/resolve/main/nethack-gpt2.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 0.2 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/nethack-gpt2-i1-GGUF/resolve/main/nethack-gpt2.i1-Q4_0.gguf) \| i1-Q4_0 \| 0.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/nethack-gpt2-i1-GGUF/resolve/main/nethack-gpt2.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 0.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/nethack-gpt2-i1-GGUF/resolve/main/nethack-gpt2.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 0.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/nethack-gpt2-i1-GGUF/resolve/main/nethack-gpt2.i1-Q4_1.gguf) \| i1-Q4_1 \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/nethack-gpt2-i1-GGUF/resolve/main/nethack-gpt2.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/nethack-gpt2-i1-GGUF/resolve/main/nethack-gpt2.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/nethack-gpt2-i1-GGUF/resolve/main/nethack-gpt2.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/nethack-gpt2-i1-GGUF/resolve/main/nethack-gpt2.i1-Q6_K.gguf) \| i1-Q6_K \| 0.2 \| 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 -->
RumoursGR/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-reclusive_durable_leopard	RumoursGR	2025-05-23T22:13:16Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am reclusive durable leopard", "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	2025-05-22T13:03:33Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-reclusive_durable_leopard tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am reclusive durable leopard - unsloth - trl licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-reclusive_durable_leopard 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="RumoursGR/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-reclusive_durable_leopard", 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}} } ```
mradermacher/PixelPerfect_StableDiffusion_AutoCompleteModel-GGUF	mradermacher	2025-05-23T22:04:28Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:PixelPerfect/PixelPerfect_StableDiffusion_AutoCompleteModel", "base_model:quantized:PixelPerfect/PixelPerfect_StableDiffusion_AutoCompleteModel", "endpoints_compatible", "region:us" ]	null	2025-05-23T22:02:40Z	--- base_model: PixelPerfect/PixelPerfect_StableDiffusion_AutoCompleteModel language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/PixelPerfect/PixelPerfect_StableDiffusion_AutoCompleteModel <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/PixelPerfect_StableDiffusion_AutoCompleteModel-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/PixelPerfect_StableDiffusion_AutoCompleteModel-GGUF/resolve/main/PixelPerfect_StableDiffusion_AutoCompleteModel.Q2_K.gguf) \| Q2_K \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/PixelPerfect_StableDiffusion_AutoCompleteModel-GGUF/resolve/main/PixelPerfect_StableDiffusion_AutoCompleteModel.Q3_K_S.gguf) \| Q3_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/PixelPerfect_StableDiffusion_AutoCompleteModel-GGUF/resolve/main/PixelPerfect_StableDiffusion_AutoCompleteModel.Q3_K_M.gguf) \| Q3_K_M \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/PixelPerfect_StableDiffusion_AutoCompleteModel-GGUF/resolve/main/PixelPerfect_StableDiffusion_AutoCompleteModel.IQ4_XS.gguf) \| IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/PixelPerfect_StableDiffusion_AutoCompleteModel-GGUF/resolve/main/PixelPerfect_StableDiffusion_AutoCompleteModel.Q4_K_S.gguf) \| Q4_K_S \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/PixelPerfect_StableDiffusion_AutoCompleteModel-GGUF/resolve/main/PixelPerfect_StableDiffusion_AutoCompleteModel.Q3_K_L.gguf) \| Q3_K_L \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/PixelPerfect_StableDiffusion_AutoCompleteModel-GGUF/resolve/main/PixelPerfect_StableDiffusion_AutoCompleteModel.Q4_K_M.gguf) \| Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/PixelPerfect_StableDiffusion_AutoCompleteModel-GGUF/resolve/main/PixelPerfect_StableDiffusion_AutoCompleteModel.Q5_K_S.gguf) \| Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/PixelPerfect_StableDiffusion_AutoCompleteModel-GGUF/resolve/main/PixelPerfect_StableDiffusion_AutoCompleteModel.Q5_K_M.gguf) \| Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/PixelPerfect_StableDiffusion_AutoCompleteModel-GGUF/resolve/main/PixelPerfect_StableDiffusion_AutoCompleteModel.Q6_K.gguf) \| Q6_K \| 0.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/PixelPerfect_StableDiffusion_AutoCompleteModel-GGUF/resolve/main/PixelPerfect_StableDiffusion_AutoCompleteModel.Q8_0.gguf) \| Q8_0 \| 0.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/PixelPerfect_StableDiffusion_AutoCompleteModel-GGUF/resolve/main/PixelPerfect_StableDiffusion_AutoCompleteModel.f16.gguf) \| f16 \| 0.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/pgt-GGUF	mradermacher	2025-05-23T22:00:06Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:christofid/pgt", "base_model:quantized:christofid/pgt", "license:mit", "endpoints_compatible", "region:us" ]	null	2025-05-23T21:52:40Z	--- base_model: christofid/pgt language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/christofid/pgt <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/pgt-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/pgt-GGUF/resolve/main/pgt.Q2_K.gguf) \| Q2_K \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/pgt-GGUF/resolve/main/pgt.Q3_K_S.gguf) \| Q3_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/pgt-GGUF/resolve/main/pgt.Q3_K_M.gguf) \| Q3_K_M \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/pgt-GGUF/resolve/main/pgt.IQ4_XS.gguf) \| IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/pgt-GGUF/resolve/main/pgt.Q4_K_S.gguf) \| Q4_K_S \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/pgt-GGUF/resolve/main/pgt.Q3_K_L.gguf) \| Q3_K_L \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/pgt-GGUF/resolve/main/pgt.Q4_K_M.gguf) \| Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/pgt-GGUF/resolve/main/pgt.Q5_K_S.gguf) \| Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/pgt-GGUF/resolve/main/pgt.Q5_K_M.gguf) \| Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/pgt-GGUF/resolve/main/pgt.Q6_K.gguf) \| Q6_K \| 0.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/pgt-GGUF/resolve/main/pgt.Q8_0.gguf) \| Q8_0 \| 0.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/pgt-GGUF/resolve/main/pgt.f16.gguf) \| f16 \| 0.4 \| 16 bpw, overkill \| 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 -->
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ArtusDev/PocketDoc_Dans-PersonalityEngine-V1.3.0-24b_EXL2_4.0bpw_H6	ArtusDev	2025-05-23T21:57:15Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "general-purpose", "roleplay", "storywriting", "chemistry", "biology", "code", "climate", "axolotl", "text-generation-inference", "finetune", "legal", "medical", "finance", "exl2", "conversational", "en", "ar", "de", "fr", "es", "hi", "pt", "ja", "ko", "dataset:PocketDoc/Dans-Prosemaxx-RP", "dataset:PocketDoc/Dans-Personamaxx-Logs-2", "dataset:PocketDoc/Dans-Personamaxx-VN", "dataset:PocketDoc/Dans-Kinomaxx-VanillaBackrooms", "dataset:PocketDoc/Dans-Prosemaxx-Gutenberg", "dataset:PocketDoc/Dans-Prosemaxx-Cowriter-3-XL", "dataset:PocketDoc/Dans-Prosemaxx-Adventure", "dataset:PocketDoc/Dans-Failuremaxx-Adventure-3", "dataset:PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-2", "dataset:PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-3", "dataset:PocketDoc/Dans-Prosemaxx-InstructWriter-Continue-2", "dataset:PocketDoc/Dans-Prosemaxx-Instructwriter-Long", "dataset:PocketDoc/Dans-Prosemaxx-RepRemover-1", "dataset:PocketDoc/Dans-MemoryCore-CoreCurriculum-Small", "dataset:AquaV/US-Army-Survival-Sharegpt", "dataset:AquaV/Multi-Environment-Operations-Sharegpt", "dataset:AquaV/Resistance-Sharegpt", "dataset:AquaV/Interrogation-Sharegpt", "dataset:AquaV/Chemical-Biological-Safety-Applications-Sharegpt", "dataset:AquaV/Energetic-Materials-Sharegpt", "dataset:PocketDoc/Dans-Mathmaxx", "dataset:PJMixers/Math-Multiturn-1K-ShareGPT", "dataset:PocketDoc/Dans-Taskmaxx", "dataset:PocketDoc/Dans-Taskmaxx-DataPrepper", "dataset:PocketDoc/Dans-Taskmaxx-ConcurrentQA-Reworked", "dataset:PocketDoc/Dans-Taskmaxx-TableGPT", "dataset:PocketDoc/Dans-Taskmaxx-SciRIFF", "dataset:PocketDoc/Dans-Taskmaxx-Edit", "dataset:PocketDoc/Dans-Toolmaxx-Agent", "dataset:PocketDoc/Dans-Toolmaxx-ShellCommands", "dataset:PocketDoc/Dans-Toolmaxx-Functions-Toolbench", "dataset:PocketDoc/Dans-Toolmaxx-Functions-ToolACE", "dataset:PocketDoc/Dans-Toolmaxx-Functions-apigen-subset", "dataset:PocketDoc/Dans-Assistantmaxx-OpenAssistant2", "dataset:PocketDoc/Dans-Assistantmaxx-Opus-Merge-2", "dataset:PocketDoc/Dans-Assistantmaxx-sonnetorca-subset", "dataset:PocketDoc/Dans-Assistantmaxx-sonnetorca-subset-2", "dataset:PocketDoc/Dans-Assistantmaxx-Synthia", "dataset:PocketDoc/Dans-Assistantmaxx-ASL", "dataset:PocketDoc/Dans-Assistantmaxx-PersonaLLM-Opus", "dataset:PocketDoc/Dans-Assistantmaxx-LongAlign", "dataset:PocketDoc/Dans-Assistantmaxx-OpenLeecher-Instruct", "dataset:PocketDoc/Dans-Assistantmaxx-Tulu3-IF", "dataset:PocketDoc/Dans-Systemmaxx", "dataset:PocketDoc/Dans-Logicmaxx-SAT-AP", "dataset:PJMixers/grimulkan_theory-of-mind-ShareGPT", "dataset:PJMixers/grimulkan_physical-reasoning-ShareGPT", "dataset:PocketDoc/Dans-Reasoningmaxx-NaturalReasoning", "dataset:PocketDoc/Dans-Reasoningmaxx-WebInstruct", "dataset:PocketDoc/Dans-Reasoningmaxx-GeneralReasoning", "dataset:PocketDoc/Dans-Assistantmaxx-ClosedInstruct", "base_model:PocketDoc/Dans-PersonalityEngine-V1.3.0-24b", "base_model:quantized:PocketDoc/Dans-PersonalityEngine-V1.3.0-24b", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "region:us" ]	text-generation	2025-05-23T21:38:29Z	--- thumbnail: >- https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.3.0-24b/resolve/main/resources/pe.png license: apache-2.0 tags: - general-purpose - roleplay - storywriting - chemistry - biology - code - climate - axolotl - text-generation-inference - finetune - legal - medical - finance - exl2 datasets: - PocketDoc/Dans-Prosemaxx-RP - PocketDoc/Dans-Personamaxx-Logs-2 - PocketDoc/Dans-Personamaxx-VN - PocketDoc/Dans-Kinomaxx-VanillaBackrooms - PocketDoc/Dans-Prosemaxx-Gutenberg - PocketDoc/Dans-Prosemaxx-Cowriter-3-XL - PocketDoc/Dans-Prosemaxx-Adventure - PocketDoc/Dans-Failuremaxx-Adventure-3 - PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-2 - PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-3 - PocketDoc/Dans-Prosemaxx-InstructWriter-Continue-2 - PocketDoc/Dans-Prosemaxx-Instructwriter-Long - PocketDoc/Dans-Prosemaxx-RepRemover-1 - PocketDoc/Dans-MemoryCore-CoreCurriculum-Small - AquaV/US-Army-Survival-Sharegpt - AquaV/Multi-Environment-Operations-Sharegpt - AquaV/Resistance-Sharegpt - AquaV/Interrogation-Sharegpt - AquaV/Chemical-Biological-Safety-Applications-Sharegpt - AquaV/Energetic-Materials-Sharegpt - PocketDoc/Dans-Mathmaxx - PJMixers/Math-Multiturn-1K-ShareGPT - PocketDoc/Dans-Taskmaxx - PocketDoc/Dans-Taskmaxx-DataPrepper - PocketDoc/Dans-Taskmaxx-ConcurrentQA-Reworked - PocketDoc/Dans-Taskmaxx-TableGPT - PocketDoc/Dans-Taskmaxx-SciRIFF - PocketDoc/Dans-Taskmaxx-Edit - PocketDoc/Dans-Toolmaxx-Agent - PocketDoc/Dans-Toolmaxx-ShellCommands - PocketDoc/Dans-Toolmaxx-Functions-Toolbench - PocketDoc/Dans-Toolmaxx-Functions-ToolACE - PocketDoc/Dans-Toolmaxx-Functions-apigen-subset - PocketDoc/Dans-Assistantmaxx-OpenAssistant2 - PocketDoc/Dans-Assistantmaxx-Opus-Merge-2 - PocketDoc/Dans-Assistantmaxx-sonnetorca-subset - PocketDoc/Dans-Assistantmaxx-sonnetorca-subset-2 - PocketDoc/Dans-Assistantmaxx-Synthia - PocketDoc/Dans-Assistantmaxx-ASL - PocketDoc/Dans-Assistantmaxx-PersonaLLM-Opus - PocketDoc/Dans-Assistantmaxx-LongAlign - PocketDoc/Dans-Assistantmaxx-OpenLeecher-Instruct - PocketDoc/Dans-Assistantmaxx-Tulu3-IF - PocketDoc/Dans-Systemmaxx - PocketDoc/Dans-Logicmaxx-SAT-AP - PJMixers/grimulkan_theory-of-mind-ShareGPT - PJMixers/grimulkan_physical-reasoning-ShareGPT - PocketDoc/Dans-Reasoningmaxx-NaturalReasoning - PocketDoc/Dans-Reasoningmaxx-WebInstruct - PocketDoc/Dans-Reasoningmaxx-GeneralReasoning - PocketDoc/Dans-Assistantmaxx-ClosedInstruct language: - en - ar - de - fr - es - hi - pt - ja - ko base_model: - PocketDoc/Dans-PersonalityEngine-V1.3.0-24b base_model_relation: quantized quantized_by: ArtusDev pipeline_tag: text-generation library_name: transformers --- <!doctype html> <html lang="en"> <head> <meta charset="UTF-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0" /> <title>Dans-PersonalityEngine-V1.3.0-24b</title> </head> <div class="crt-container"> <div class="crt-case"> <div class="crt-inner-case"> <div class="crt-bezel"> <div class="terminal-screen"> <div style="text-align: center"> <h2>Dans-PersonalityEngine-V1.3.0-24b</h2> <pre class="code-block" style="display: inline-block; text-align: left; font-size: clamp(2px, 0.8vw, 14px); line-height: 1.2; max-width: 100%; overflow: hidden; white-space: pre;"> ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠀⠄⠀⡂⠀⠁⡄⢀⠁⢀⣈⡄⠌⠐⠠⠤⠄⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⡄⠆⠀⢠⠀⠛⣸⣄⣶⣾⡷⡾⠘⠃⢀⠀⣴⠀⡄⠰⢆⣠⠘⠰⠀⡀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠃⠀⡋⢀⣤⡿⠟⠋⠁⠀⡠⠤⢇⠋⠀⠈⠃⢀⠀⠈⡡⠤⠀⠀⠁⢄⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠁⡂⠀⠀⣀⣔⣧⠟⠋⠀⢀⡄⠀⠪⣀⡂⢁⠛⢆⠀⠀⠀⢎⢀⠄⢡⠢⠛⠠⡀⠀⠄⠀⠀ ⠀⠀⡀⠡⢑⠌⠈⣧⣮⢾⢏⠁⠀⠀⡀⠠⠦⠈⠀⠞⠑⠁⠀⠀⢧⡄⠈⡜⠷⠒⢸⡇⠐⠇⠿⠈⣖⠂⠀ ⠀⢌⠀⠤⠀⢠⣞⣾⡗⠁⠀⠈⠁⢨⡼⠀⠀⠀⢀⠀⣀⡤⣄⠄⠈⢻⡇⠀⠐⣠⠜⠑⠁⠀⣀⡔⡿⠨⡄ ⠈⠂⠀⠆⠀⣼⣾⠟⠀⠑⠀⡐⠗⠉⠀⠐⠶⣤⡵⠋⠀⠠⠹⡌⡀⠘⠇⢠⣾⡣⣀⡴⠋⠅⠈⢊⠠⡱⡀ ⠪⠑⢌⠂⣼⣿⡟⠀⠀⠙⠀⠀⠀⡀⠀⠀⠐⡞⡐⠀⠀⡧⠀⢀⠠⠀⣁⠾⡇⠀⠙⡁⠀⠀⢀⣨⣄⡠⢱ ⣸⠈⠊⠙⣛⣿⡧⠔⠚⠛⠳⣄⣀⡬⠤⠬⠼⡣⠃⠀⢀⡗⠀⡤⠞⠙⠄⠂⠃⢀⣠⣤⠶⠙⠅⠁⠃⠋⠈ ⢋⠼⣀⠰⢯⢿⠁⠀⢢⠀⠀⢐⠋⡀⠀⠈⠁⠀⣀⣰⠏⠒⠙⠈⠀⣀⡤⠞⢁⣼⠏⠘⢀⣀⢤⢤⡐⢈⠂ ⠀⠢⠀⠀⠸⣿⡄⠲⠚⠘⠚⠃⢀⠀⠈⢋⠶⠛⠉⠉⢃⣀⢤⢾⠋⣁⡤⡚⠁⢹⠁⠠⢛⠠⠬⠁⢬⠀⠀ ⠀⠈⢳⣒⠋⠉⣿⢐⠠⣀⣃⠀⠀⠉⠂⢁⣀⣀⡤⢞⠩⢑⡨⠰⡞⠁⠁⢀⡠⠾⠎⡈⡌⡈⡓⡀⠄⠀⠀ ⠀⠀⠀⠉⠘⠃⢻⡒⠦⢼⣿⣛⣻⣿⡷⢄⣀⣀⣠⣴⢾⣿⣆⣡⡄⣠⣪⡿⣷⣾⣷⣧⡡⠅⣇⠍⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠙⠒⠒⠛⠛⠓⠉⢹⠀⣷⠴⣻⣽⡻⢧⢻⡿⡏⣼⢿⣻⢾⣿⣿⣿⡿⢠ ⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠂⠻⠨⠰⢋⡅⠉⣑⡇⡗⣿⢂⣸⡿⣿⣛⠿⠃⠁ ⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠳⣌⣙⣸⢧⣿⣕⣼⣇⢹⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣠⣸⢧⢟⢟⡟⣾⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⢰⠙⣾⡟⣻⡕⣹⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⢸⢰⡏⢠⡿⠾⠋⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⢸⠸⡇⡏⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠸⢸⢸⡇⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⠇⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ </pre> </div> <p> Dans-PersonalityEngine is a versatile model series fine-tuned on 50+ specialized datasets, designed to excel at both creative tasks (like roleplay and co-writing) and technical challenges (such as code generation, tool use, and complex reasoning). </p> <p> V1.3.0 introduces multilingual capabilities with support for 10 languages and enhanced domain expertise across multiple fields. The primary language is still English and that is where peak performance can be expected. </p> <h3>Multilingual Support</h3> <pre class="code-block"> Arabic Chinese English French German Hindi Japanese Korean Portuguese Spanish</pre> <h3>Key Details</h3> <pre class="code-block"> BASE MODEL: mistralai/Mistral-Small-3.1-24B-Base-2503 LICENSE: apache-2.0 LANGUAGE: Multilingual with 10 supported languages CONTEXT LENGTH: 32768 tokens, 131072 with degraded recall</pre> <h3>Recommended Settings</h3> <pre class="code-block"> TEMPERATURE: 1.0 TOP_P: 0.9</pre> <h3>Prompting Format</h3> <p> The model uses the following format I'll refer to as "DanChat-2": </p> <pre class="code-block"> <\|system\|>system prompt<\|endoftext\|><\|user\|>Hi there!<\|endoftext\|><\|assistant\|>Hey, how can I help?<\|endoftext\|></pre> <h3>Why not ChatML?</h3> <p> While ChatML is a standard format for LLMs, it has limitations. DanChat-2 uses special tokens for each role, this reduces biases and helps the model adapt to different tasks more readily. </p> <h3>SillyTavern Template</h3> <p> <a href="https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.3.0-24b/resolve/main/resources/DanChat-2.json?download=true" download target="_blank" rel="noopener noreferrer" > Download Master JSON </a> </p> <h3>Inference Provider</h3> <p> This model and others are available from ⚡Mancer AI for those interested in high quality inference without owning or renting expensive hardware. </p> <p class="mancer-button-container"> <a href="https://mancer.tech/" target="_blank" rel="noopener noreferrer" class="mancer-button" > <span class="mancer-text">mancer</span> </a> </p> <h3>Training Process</h3> <p> The model was trained using Axolotl on 8x H100 GPUs for 50 hours. The resources to train this model were provided by Prime Intellect and Kalomaze. </p> <h3>Support Development</h3> <p> Development is limited by funding and resources. To help support: </p> <p>- Contact on HF</p> <p>- Email: [email protected]</p> <p class="coffee-container"> <a href="https://www.buymeacoffee.com/visually" target="_blank" rel="noopener noreferrer" > <img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" height="45" width="162" /> </a> </p> </div> </div> </div> </div> </div> <style> @import url("https://fonts.googleapis.com/css2?family=Consolas&display=swap"); .crt-container { padding: 10px; max-width: 1000px; margin: 0 auto; width: 95%; } .crt-case { background: #e8d7c3; border-radius: 10px; padding: 15px; box-shadow: inset -2px -2px 5px rgba(0, 0, 0, 0.3), 2px 2px 5px rgba(0, 0, 0, 0.2); } .crt-inner-case { background: #e8d7c3; border-radius: 8px; padding: 3px; box-shadow: inset -1px -1px 4px rgba(0, 0, 0, 0.3), 1px 1px 4px rgba(0, 0, 0, 0.2); } .crt-bezel { background: linear-gradient(145deg, #1a1a1a, #2a2a2a); padding: 15px; border-radius: 5px; border: 3px solid #0a0a0a; position: relative; box-shadow: inset 0 0 20px rgba(0, 0, 0, 0.5), inset 0 0 4px rgba(0, 0, 0, 0.4), inset 2px 2px 4px rgba(255, 255, 255, 0.05), inset -2px -2px 4px rgba(0, 0, 0, 0.8), 0 0 2px rgba(0, 0, 0, 0.6), -1px -1px 4px rgba(255, 255, 255, 0.1), 1px 1px 4px rgba(0, 0, 0, 0.3); 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line-height: 1; } .mancer-button:hover { background: #2a2a2a; box-shadow: 0 0 15px rgba(228, 155, 62, 0.5); text-shadow: 0 0 4px #e49b3e; text-decoration: none !important; } </style> </html>
mradermacher/DialoGPT-large-Morty-GGUF	mradermacher	2025-05-23T21:51:32Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:s3nh/DialoGPT-large-Morty", "base_model:quantized:s3nh/DialoGPT-large-Morty", "license:openrail", "endpoints_compatible", "region:us" ]	null	2025-05-23T21:46:48Z	--- base_model: s3nh/DialoGPT-large-Morty language: - en library_name: transformers license: openrail quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/s3nh/DialoGPT-large-Morty <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/DialoGPT-large-Morty-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/DialoGPT-large-Morty-GGUF/resolve/main/DialoGPT-large-Morty.Q2_K.gguf) \| Q2_K \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-large-Morty-GGUF/resolve/main/DialoGPT-large-Morty.Q3_K_S.gguf) \| Q3_K_S \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-large-Morty-GGUF/resolve/main/DialoGPT-large-Morty.Q3_K_M.gguf) \| Q3_K_M \| 0.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-large-Morty-GGUF/resolve/main/DialoGPT-large-Morty.IQ4_XS.gguf) \| IQ4_XS \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-large-Morty-GGUF/resolve/main/DialoGPT-large-Morty.Q4_K_S.gguf) \| Q4_K_S \| 0.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-large-Morty-GGUF/resolve/main/DialoGPT-large-Morty.Q3_K_L.gguf) \| Q3_K_L \| 0.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-large-Morty-GGUF/resolve/main/DialoGPT-large-Morty.Q4_K_M.gguf) \| Q4_K_M \| 0.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-large-Morty-GGUF/resolve/main/DialoGPT-large-Morty.Q5_K_S.gguf) \| Q5_K_S \| 0.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-large-Morty-GGUF/resolve/main/DialoGPT-large-Morty.Q5_K_M.gguf) \| Q5_K_M \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-large-Morty-GGUF/resolve/main/DialoGPT-large-Morty.Q6_K.gguf) \| Q6_K \| 0.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-large-Morty-GGUF/resolve/main/DialoGPT-large-Morty.Q8_0.gguf) \| Q8_0 \| 0.9 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-large-Morty-GGUF/resolve/main/DialoGPT-large-Morty.f16.gguf) \| f16 \| 1.7 \| 16 bpw, overkill \| 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 -->
jodhpur-security-guard-viral-video/jodhpur.security.guard.Viral.video.original	jodhpur-security-guard-viral-video	2025-05-23T21:48:47Z	0	0	null	[ "region:us" ]	null	2025-05-23T21:47:51Z	<a rel="nofollow" href="https://iccnews.xyz/leaked?V=ss">🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶</a> <a rel="nofollow" href="https://iccnews.xyz/leaked?V=ss">🔴 CLICK HERE 🌐==►► Download Now)</a> <a data-target="animated-image.originalLink" rel="nofollow" href="https://iccnews.xyz/leaked?V=ss"><img data-target="animated-image.originalImage" style="max-width: 100%; display: inline-block;" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif"></a>
mradermacher/monica-v0.1.0-i1-GGUF	mradermacher	2025-05-23T21:40:44Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:jncarlo/monica-v0.1.0", "base_model:quantized:jncarlo/monica-v0.1.0", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-05-23T21:21:26Z	--- base_model: jncarlo/monica-v0.1.0 language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/jncarlo/monica-v0.1.0 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/monica-v0.1.0-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/monica-v0.1.0-i1-GGUF/resolve/main/monica-v0.1.0.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.2 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/monica-v0.1.0-i1-GGUF/resolve/main/monica-v0.1.0.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.2 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/monica-v0.1.0-i1-GGUF/resolve/main/monica-v0.1.0.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/monica-v0.1.0-i1-GGUF/resolve/main/monica-v0.1.0.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/monica-v0.1.0-i1-GGUF/resolve/main/monica-v0.1.0.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/monica-v0.1.0-i1-GGUF/resolve/main/monica-v0.1.0.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/monica-v0.1.0-i1-GGUF/resolve/main/monica-v0.1.0.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.3 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/monica-v0.1.0-i1-GGUF/resolve/main/monica-v0.1.0.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/monica-v0.1.0-i1-GGUF/resolve/main/monica-v0.1.0.i1-Q2_K.gguf) \| i1-Q2_K \| 0.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/monica-v0.1.0-i1-GGUF/resolve/main/monica-v0.1.0.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/monica-v0.1.0-i1-GGUF/resolve/main/monica-v0.1.0.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/monica-v0.1.0-i1-GGUF/resolve/main/monica-v0.1.0.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.3 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/monica-v0.1.0-i1-GGUF/resolve/main/monica-v0.1.0.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/monica-v0.1.0-i1-GGUF/resolve/main/monica-v0.1.0.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.3 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/monica-v0.1.0-i1-GGUF/resolve/main/monica-v0.1.0.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/monica-v0.1.0-i1-GGUF/resolve/main/monica-v0.1.0.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 0.3 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/monica-v0.1.0-i1-GGUF/resolve/main/monica-v0.1.0.i1-Q4_0.gguf) \| i1-Q4_0 \| 0.3 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/monica-v0.1.0-i1-GGUF/resolve/main/monica-v0.1.0.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 0.3 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/monica-v0.1.0-i1-GGUF/resolve/main/monica-v0.1.0.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 0.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/monica-v0.1.0-i1-GGUF/resolve/main/monica-v0.1.0.i1-Q4_1.gguf) \| i1-Q4_1 \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/monica-v0.1.0-i1-GGUF/resolve/main/monica-v0.1.0.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 0.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/monica-v0.1.0-i1-GGUF/resolve/main/monica-v0.1.0.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/monica-v0.1.0-i1-GGUF/resolve/main/monica-v0.1.0.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/monica-v0.1.0-i1-GGUF/resolve/main/monica-v0.1.0.i1-Q6_K.gguf) \| i1-Q6_K \| 0.4 \| 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 -->
vonewman/Qwen3-0.6B-reasoning-conversational	vonewman	2025-05-23T21:37:17Z	0	0	transformers	[ "transformers", "pytorch", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-23T21:36:48Z	--- base_model: unsloth/qwen3-0.6b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: vonewman - License: apache-2.0 - Finetuned from model : unsloth/qwen3-0.6b-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)
jordinia/NetPro-Qwen3-0.6B-ClfDC	jordinia	2025-05-23T21:34:48Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-0.6B-Base", "base_model:finetune:unsloth/Qwen3-0.6B-Base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-23T21:34:33Z	--- base_model: unsloth/Qwen3-0.6B-Base tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: jordinia - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-0.6B-Base 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)
sayian99/bert-fake-news	sayian99	2025-05-23T21:17:59Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-23T19:07:47Z	--- 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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(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]
1-one-girl-one-wolf/one.girl.one.wolf.viral.video	1-one-girl-one-wolf	2025-05-23T21:16:09Z	0	0	null	[ "region:us" ]	null	2025-05-23T21:14:53Z	<a rel="nofollow" href="https://iccnews.xyz/leaked?V=one-girl-one-wolf">🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶</a> <a rel="nofollow" href="https://iccnews.xyz/leaked?V=one-girl-one-wolf">🔴 CLICK HERE 🌐==►► Download Now)</a> <a data-target="animated-image.originalLink" rel="nofollow" href="https://iccnews.xyz/leaked?V=one-girl-one-wolf"><img data-target="animated-image.originalImage" style="max-width: 100%; display: inline-block;" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif"></a>
cheonkamjeong/deberta-v3-large-v2-reward-model	cheonkamjeong	2025-05-23T21:12:04Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "trl", "reward-trainer", "base_model:OpenAssistant/reward-model-deberta-v3-large-v2", "base_model:finetune:OpenAssistant/reward-model-deberta-v3-large-v2", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-23T20:50:47Z	--- base_model: OpenAssistant/reward-model-deberta-v3-large-v2 library_name: transformers model_name: deberta-v3-large-v2-reward-model tags: - generated_from_trainer - trl - reward-trainer licence: license --- # Model Card for deberta-v3-large-v2-reward-model This model is a fine-tuned version of [OpenAssistant/reward-model-deberta-v3-large-v2](https://huggingface.co/OpenAssistant/reward-model-deberta-v3-large-v2). 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="cheonkamjeong/deberta-v3-large-v2-reward-model", 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 Reward. ### Framework versions - TRL: 0.17.0 - Transformers: 4.52.3 - Pytorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
a80abbasi/Qwen2-0.5B-GRPO-test	a80abbasi	2025-05-23T21:07:44Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:AI-MO/NuminaMath-TIR", "arxiv:2402.03300", "endpoints_compatible", "region:us" ]	null	2025-05-23T13:40:54Z	--- datasets: AI-MO/NuminaMath-TIR library_name: transformers model_name: Qwen2-0.5B-GRPO-test tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen2-0.5B-GRPO-test This model is a fine-tuned version of [None](https://huggingface.co/None) on the [AI-MO/NuminaMath-TIR](https://huggingface.co/datasets/AI-MO/NuminaMath-TIR) 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="a80abbasi/Qwen2-0.5B-GRPO-test", 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: 0.17.0 - Transformers: 4.52.3 - Pytorch: 2.7.0 - 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}} } ```
MinaMila/llama_instbase_unlearned_ug_e-6_1.0_0.25_0.5_ep3_LoRa_ACSEmployment_2_ep6_22	MinaMila	2025-05-23T21:05:50Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-23T21:05:45Z	--- 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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MinaMila/phi3_unlearned_ug_e-5_1.0_0.15_0.05_LoRa_Adult_cfda_ep10_22	MinaMila	2025-05-23T20:50:27Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-23T20:50:25Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/gemma2_2b_unlearned_gu_LoRa_ACSEmployment_2_ep2_22	MinaMila	2025-05-23T20:50:24Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-23T20:50:19Z	--- 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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mradermacher/vice-headlines-i1-GGUF	mradermacher	2025-05-23T20:46:38Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:marcderbauer/vice-headlines", "base_model:quantized:marcderbauer/vice-headlines", "license:mit", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-05-23T20:43:19Z	--- base_model: marcderbauer/vice-headlines language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/marcderbauer/vice-headlines <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/vice-headlines-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/vice-headlines-i1-GGUF/resolve/main/vice-headlines.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/vice-headlines-i1-GGUF/resolve/main/vice-headlines.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.4 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/vice-headlines-i1-GGUF/resolve/main/vice-headlines.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/vice-headlines-i1-GGUF/resolve/main/vice-headlines.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/vice-headlines-i1-GGUF/resolve/main/vice-headlines.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/vice-headlines-i1-GGUF/resolve/main/vice-headlines.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/vice-headlines-i1-GGUF/resolve/main/vice-headlines.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/vice-headlines-i1-GGUF/resolve/main/vice-headlines.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.4 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/vice-headlines-i1-GGUF/resolve/main/vice-headlines.i1-Q2_K.gguf) \| i1-Q2_K \| 0.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/vice-headlines-i1-GGUF/resolve/main/vice-headlines.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/vice-headlines-i1-GGUF/resolve/main/vice-headlines.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/vice-headlines-i1-GGUF/resolve/main/vice-headlines.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/vice-headlines-i1-GGUF/resolve/main/vice-headlines.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/vice-headlines-i1-GGUF/resolve/main/vice-headlines.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.5 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/vice-headlines-i1-GGUF/resolve/main/vice-headlines.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/vice-headlines-i1-GGUF/resolve/main/vice-headlines.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 0.5 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/vice-headlines-i1-GGUF/resolve/main/vice-headlines.i1-Q4_0.gguf) \| i1-Q4_0 \| 0.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/vice-headlines-i1-GGUF/resolve/main/vice-headlines.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 0.5 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/vice-headlines-i1-GGUF/resolve/main/vice-headlines.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 0.5 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/vice-headlines-i1-GGUF/resolve/main/vice-headlines.i1-Q4_1.gguf) \| i1-Q4_1 \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/vice-headlines-i1-GGUF/resolve/main/vice-headlines.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 0.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/vice-headlines-i1-GGUF/resolve/main/vice-headlines.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/vice-headlines-i1-GGUF/resolve/main/vice-headlines.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/vice-headlines-i1-GGUF/resolve/main/vice-headlines.i1-Q6_K.gguf) \| i1-Q6_K \| 0.6 \| 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 -->
salaheddine666/Llama-3.2-1B-Instruct-heart	salaheddine666	2025-05-23T20:45:43Z	4	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:unsloth/Llama-3.2-1B-Instruct", "base_model:adapter:unsloth/Llama-3.2-1B-Instruct", "license:llama3.2", "region:us" ]	null	2025-05-21T17:40:13Z	--- library_name: peft license: llama3.2 base_model: unsloth/Llama-3.2-1B-Instruct tags: - generated_from_trainer metrics: - accuracy model-index: - name: Llama-3.2-1B-Instruct-heart 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. --> # Llama-3.2-1B-Instruct-heart This model is a fine-tuned version of [unsloth/Llama-3.2-1B-Instruct](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4743 - Accuracy: 0.8278 - Report: precision recall f1-score support absence 0.81 0.86 0.84 92 presence 0.84 0.80 0.82 88 accuracy 0.83 180 macro avg 0.83 0.83 0.83 180 weighted avg 0.83 0.83 0.83 180 ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Report \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:\| \| No log \| 1.0 \| 105 \| 0.5200 \| 0.7778 \| precision recall f1-score support absence 0.81 0.74 0.77 92 presence 0.75 0.82 0.78 88 accuracy 0.78 180 macro avg 0.78 0.78 0.78 180 weighted avg 0.78 0.78 0.78 180 \| \| No log \| 2.0 \| 210 \| 0.4746 \| 0.8056 \| precision recall f1-score support absence 0.83 0.78 0.80 92 presence 0.78 0.83 0.81 88 accuracy 0.81 180 macro avg 0.81 0.81 0.81 180 weighted avg 0.81 0.81 0.81 180 \| \| No log \| 3.0 \| 315 \| 0.5318 \| 0.8 \| precision recall f1-score support absence 0.75 0.90 0.82 92 presence 0.87 0.69 0.77 88 accuracy 0.80 180 macro avg 0.81 0.80 0.80 180 weighted avg 0.81 0.80 0.80 180 \| \| No log \| 4.0 \| 420 \| 0.4963 \| 0.7889 \| precision recall f1-score support absence 0.81 0.77 0.79 92 presence 0.77 0.81 0.79 88 accuracy 0.79 180 macro avg 0.79 0.79 0.79 180 weighted avg 0.79 0.79 0.79 180 \| \| 0.524 \| 5.0 \| 525 \| 0.4743 \| 0.8278 \| precision recall f1-score support absence 0.81 0.86 0.84 92 presence 0.84 0.80 0.82 88 accuracy 0.83 180 macro avg 0.83 0.83 0.83 180 weighted avg 0.83 0.83 0.83 180 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
asanoop24/roberta-large-ner-english-speaker-diarization	asanoop24	2025-05-23T20:42:56Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-05-23T20:16:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Srtrtegy/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-nasty_shiny_lion	Srtrtegy	2025-05-23T20:42:06Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am nasty shiny lion", "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	2025-05-13T15:42:27Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-nasty_shiny_lion tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am nasty shiny lion - unsloth - trl licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-nasty_shiny_lion 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="Srtrtegy/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-nasty_shiny_lion", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.6.0 - 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}} } ```
mradermacher/gpt2-medium-stable-diffusion-prompt-generator-i1-GGUF	mradermacher	2025-05-23T20:41:18Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:Ar4ikov/gpt2-medium-stable-diffusion-prompt-generator", "base_model:quantized:Ar4ikov/gpt2-medium-stable-diffusion-prompt-generator", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-05-23T20:38:10Z	--- base_model: Ar4ikov/gpt2-medium-stable-diffusion-prompt-generator language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Ar4ikov/gpt2-medium-stable-diffusion-prompt-generator <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/gpt2-medium-stable-diffusion-prompt-generator-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/gpt2-medium-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-stable-diffusion-prompt-generator.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.2 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-stable-diffusion-prompt-generator.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.2 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-stable-diffusion-prompt-generator.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-stable-diffusion-prompt-generator.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-stable-diffusion-prompt-generator.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-stable-diffusion-prompt-generator.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-stable-diffusion-prompt-generator.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.3 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-stable-diffusion-prompt-generator.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-stable-diffusion-prompt-generator.i1-Q2_K.gguf) \| i1-Q2_K \| 0.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-stable-diffusion-prompt-generator.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-stable-diffusion-prompt-generator.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-stable-diffusion-prompt-generator.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.3 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-stable-diffusion-prompt-generator.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-stable-diffusion-prompt-generator.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.3 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-stable-diffusion-prompt-generator.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-stable-diffusion-prompt-generator.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 0.3 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-stable-diffusion-prompt-generator.i1-Q4_0.gguf) \| i1-Q4_0 \| 0.3 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-stable-diffusion-prompt-generator.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 0.3 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-stable-diffusion-prompt-generator.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 0.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-stable-diffusion-prompt-generator.i1-Q4_1.gguf) \| i1-Q4_1 \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-stable-diffusion-prompt-generator.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 0.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-stable-diffusion-prompt-generator.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-stable-diffusion-prompt-generator.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-stable-diffusion-prompt-generator.i1-Q6_K.gguf) \| i1-Q6_K \| 0.4 \| 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 -->
mradermacher/DialoGPT-Mahiru-Proto-i1-GGUF	mradermacher	2025-05-23T20:35:36Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:Hobospider132/DialoGPT-Mahiru-Proto", "base_model:quantized:Hobospider132/DialoGPT-Mahiru-Proto", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-05-23T20:32:32Z	--- base_model: Hobospider132/DialoGPT-Mahiru-Proto language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Hobospider132/DialoGPT-Mahiru-Proto <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/DialoGPT-Mahiru-Proto-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/DialoGPT-Mahiru-Proto-i1-GGUF/resolve/main/DialoGPT-Mahiru-Proto.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.2 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Mahiru-Proto-i1-GGUF/resolve/main/DialoGPT-Mahiru-Proto.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.2 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Mahiru-Proto-i1-GGUF/resolve/main/DialoGPT-Mahiru-Proto.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Mahiru-Proto-i1-GGUF/resolve/main/DialoGPT-Mahiru-Proto.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Mahiru-Proto-i1-GGUF/resolve/main/DialoGPT-Mahiru-Proto.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Mahiru-Proto-i1-GGUF/resolve/main/DialoGPT-Mahiru-Proto.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Mahiru-Proto-i1-GGUF/resolve/main/DialoGPT-Mahiru-Proto.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.3 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Mahiru-Proto-i1-GGUF/resolve/main/DialoGPT-Mahiru-Proto.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Mahiru-Proto-i1-GGUF/resolve/main/DialoGPT-Mahiru-Proto.i1-Q2_K.gguf) \| i1-Q2_K \| 0.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Mahiru-Proto-i1-GGUF/resolve/main/DialoGPT-Mahiru-Proto.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Mahiru-Proto-i1-GGUF/resolve/main/DialoGPT-Mahiru-Proto.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Mahiru-Proto-i1-GGUF/resolve/main/DialoGPT-Mahiru-Proto.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.3 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Mahiru-Proto-i1-GGUF/resolve/main/DialoGPT-Mahiru-Proto.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Mahiru-Proto-i1-GGUF/resolve/main/DialoGPT-Mahiru-Proto.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.3 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Mahiru-Proto-i1-GGUF/resolve/main/DialoGPT-Mahiru-Proto.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Mahiru-Proto-i1-GGUF/resolve/main/DialoGPT-Mahiru-Proto.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 0.3 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Mahiru-Proto-i1-GGUF/resolve/main/DialoGPT-Mahiru-Proto.i1-Q4_0.gguf) \| i1-Q4_0 \| 0.3 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Mahiru-Proto-i1-GGUF/resolve/main/DialoGPT-Mahiru-Proto.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 0.3 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Mahiru-Proto-i1-GGUF/resolve/main/DialoGPT-Mahiru-Proto.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 0.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Mahiru-Proto-i1-GGUF/resolve/main/DialoGPT-Mahiru-Proto.i1-Q4_1.gguf) \| i1-Q4_1 \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Mahiru-Proto-i1-GGUF/resolve/main/DialoGPT-Mahiru-Proto.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 0.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Mahiru-Proto-i1-GGUF/resolve/main/DialoGPT-Mahiru-Proto.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Mahiru-Proto-i1-GGUF/resolve/main/DialoGPT-Mahiru-Proto.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Mahiru-Proto-i1-GGUF/resolve/main/DialoGPT-Mahiru-Proto.i1-Q6_K.gguf) \| i1-Q6_K \| 0.4 \| 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 -->
fbaldassarri/EleutherAI_pythia-2.8b-autoawq-int4-gs64-asym	fbaldassarri	2025-05-23T20:31:33Z	0	0	null	[ "safetensors", "gpt_neox", "pytorch", "causal-lm", "pythia", "autoround", "intel", "intel-autoround", "awq", "autoawq", "woq", "text-generation", "en", "dataset:EleutherAI/pile", "base_model:EleutherAI/pythia-2.8b", "base_model:quantized:EleutherAI/pythia-2.8b", "license:apache-2.0", "4-bit", "region:us" ]	text-generation	2025-05-23T20:30:24Z	--- language: - en tags: - pytorch - causal-lm - pythia - autoround - intel - intel-autoround - awq - autoawq - woq license: apache-2.0 model_name: Pythia 2.8b base_model: EleutherAI/pythia-2.8b inference: false model_creator: EleutherAI datasets: - EleutherAI/pile pipeline_tag: text-generation prompt_template: '{prompt} ' quantized_by: fbaldassarri --- ## Model Information Quantized version of [EleutherAI/pythia-2.8b](EleutherAI/pythia-2.8b) using torch.float32 for quantization tuning. - 4 bits (INT4) - group size = 64 - Asymmetrical Quantization - Method WoQ: AWQ (AutoAWQ algorithm) Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.5.1 Note: this INT4 version of pythia-2.8b has been quantized to run inference through CPU. ## Replication Recipe ### Step 1 Install Requirements I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment. ``` wget https://github.com/intel/auto-round/archive/refs/tags/v0.5.1.tar.gz tar -xvzf v0.5.1.tar.gz cd auto-round-0.5.1 pip install -r requirements-cpu.txt --upgrade ``` ### Step 2 Build Intel AutoRound wheel from sources ``` pip install -vvv --no-build-isolation -e .[cpu] ``` ### Step 3 Script for Quantization ``` from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "EleutherAI/pythia-2.8b" model = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) from auto_round import AutoRound bits, group_size, sym, device, amp = 4, 64, False, 'cpu', False autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device, amp=amp) autoround.quantize() output_dir = "./AutoRound/EleutherAI_pythia-2.8b-autoawq-int4-gs64-asym" autoround.save_quantized(output_dir, format='auto_awq', inplace=True) ``` ## License [Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/) ## Disclaimer This quantized model comes with no warrenty. It has been developed only for research purposes.
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mradermacher/111m-GGUF	mradermacher	2025-05-23T20:26:13Z	0	0	transformers	[ "transformers", "gguf", "en", "dataset:tatsu-lab/alpaca", "dataset:the_pile", "base_model:Corianas/111m", "base_model:quantized:Corianas/111m", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us" ]	null	2025-05-23T20:03:46Z	--- base_model: Corianas/111m datasets: - tatsu-lab/alpaca - the_pile language: - en library_name: transformers license: cc-by-nc-sa-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Corianas/111m <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/111m-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/111m-GGUF/resolve/main/111m.Q2_K.gguf) \| Q2_K \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/111m-GGUF/resolve/main/111m.Q3_K_S.gguf) \| Q3_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/111m-GGUF/resolve/main/111m.Q3_K_M.gguf) \| Q3_K_M \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/111m-GGUF/resolve/main/111m.IQ4_XS.gguf) \| IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/111m-GGUF/resolve/main/111m.Q4_K_S.gguf) \| Q4_K_S \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/111m-GGUF/resolve/main/111m.Q3_K_L.gguf) \| Q3_K_L \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/111m-GGUF/resolve/main/111m.Q4_K_M.gguf) \| Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/111m-GGUF/resolve/main/111m.Q5_K_S.gguf) \| Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/111m-GGUF/resolve/main/111m.Q5_K_M.gguf) \| Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/111m-GGUF/resolve/main/111m.Q6_K.gguf) \| Q6_K \| 0.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/111m-GGUF/resolve/main/111m.Q8_0.gguf) \| Q8_0 \| 0.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/111m-GGUF/resolve/main/111m.f16.gguf) \| f16 \| 0.3 \| 16 bpw, overkill \| 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 -->
mission-impossible-8-full-movie-torrent-do/Mission.Impossible.8.Download.YTS.Torrent.Availabe.Now.Online.on.streaming.1080p.720p.480p.hd	mission-impossible-8-full-movie-torrent-do	2025-05-23T20:25:50Z	0	0	null	[ "region:us" ]	null	2025-05-23T20:24:36Z	<a rel="nofollow" href="https://iccnews.xyz/mi8">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://iccnews.xyz/mi8">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️&ZeroWidthSpace;</a> <a data-target="animated-image.originalLink" rel="nofollow" href="https://iccnews.xyz/mi8"><img data-target="animated-image.originalImage" style="max-width: 100%; display: inline-block;" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif"></a>
fbaldassarri/EleutherAI_pythia-2.8b-autogptq-int8-gs64-asym	fbaldassarri	2025-05-23T20:24:21Z	0	0	null	[ "safetensors", "gpt_neox", "pytorch", "causal-lm", "pythia", "autoround", "intel-autoround", "auto-round", "intel", "woq", "gptq", "auto-gptq", "autogptq", "eleutheraI", "text-generation", "en", "dataset:EleutherAI/pile", "base_model:EleutherAI/pythia-2.8b", "base_model:quantized:EleutherAI/pythia-2.8b", "license:apache-2.0", "8-bit", "region:us" ]	text-generation	2025-05-23T20:22:56Z	--- language: - en tags: - pytorch - causal-lm - pythia - autoround - intel-autoround - auto-round - intel - woq - gptq - auto-gptq - autogptq - eleutheraI license: apache-2.0 model_name: Pythia 2.8b base_model: EleutherAI/pythia-2.8b inference: false model_creator: EleutherAI datasets: - EleutherAI/pile pipeline_tag: text-generation prompt_template: '{prompt} ' quantized_by: fbaldassarri --- ## Model Information Quantized version of [EleutherAI/pythia-2.8b](https://huggingface.co/fbaldassarri/EleutherAI/pythia-2.8b) using torch.float32 for quantization tuning. - 8 bits (INT8) - group size = 64 - Asymmetrical Quantization - Method WoQ: GPTQ (AutoGPTQ algorithm) Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.5.1 Note: this INT8 version of pythia-2.8b has been quantized to run inference through CPU. ## Replication Recipe ### Step 1 Install Requirements I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment. ``` wget https://github.com/intel/auto-round/archive/refs/tags/v0.5.1.tar.gz tar -xvzf v0.5.1.tar.gz cd auto-round-0.5.1 pip install -r requirements-cpu.txt --upgrade ``` ### Step 2 Build Intel AutoRound wheel from sources ``` pip install -vvv --no-build-isolation -e .[cpu] ``` ### Step 3 Script for Quantization ``` from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "EleutherAI/pythia-2.8b" model = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) from auto_round import AutoRound bits, group_size, sym, device, amp = 8, 64, False, 'cpu', False autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device, amp=amp) autoround.quantize() output_dir = "./AutoRound/EleutherAI_pythia-2.8b-autogptq-int8-gs64-asym" autoround.save_quantized(output_dir, format='auto_gptq', inplace=True) ``` ## License [Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/) ## Disclaimer This quantized model comes with no warrenty. It has been developed only for research purposes.
mradermacher/ReTool-Qwen3-4B-SFT-cold-started-GGUF	mradermacher	2025-05-23T20:20:17Z	0	0	transformers	[ "transformers", "gguf", "en", "dataset:swordfaith/ReTool-SFT-multi-turn", "base_model:swordfaith/ReTool-Qwen3-4B-SFT-cold-started", "base_model:quantized:swordfaith/ReTool-Qwen3-4B-SFT-cold-started", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-23T20:03:09Z	--- base_model: swordfaith/ReTool-Qwen3-4B-SFT-cold-started datasets: - swordfaith/ReTool-SFT-multi-turn language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/swordfaith/ReTool-Qwen3-4B-SFT-cold-started <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/ReTool-Qwen3-4B-SFT-cold-started-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/ReTool-Qwen3-4B-SFT-cold-started-GGUF/resolve/main/ReTool-Qwen3-4B-SFT-cold-started.Q2_K.gguf) \| Q2_K \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/ReTool-Qwen3-4B-SFT-cold-started-GGUF/resolve/main/ReTool-Qwen3-4B-SFT-cold-started.Q3_K_S.gguf) \| Q3_K_S \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/ReTool-Qwen3-4B-SFT-cold-started-GGUF/resolve/main/ReTool-Qwen3-4B-SFT-cold-started.Q3_K_M.gguf) \| Q3_K_M \| 2.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/ReTool-Qwen3-4B-SFT-cold-started-GGUF/resolve/main/ReTool-Qwen3-4B-SFT-cold-started.Q3_K_L.gguf) \| Q3_K_L \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/ReTool-Qwen3-4B-SFT-cold-started-GGUF/resolve/main/ReTool-Qwen3-4B-SFT-cold-started.IQ4_XS.gguf) \| IQ4_XS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/ReTool-Qwen3-4B-SFT-cold-started-GGUF/resolve/main/ReTool-Qwen3-4B-SFT-cold-started.Q4_K_S.gguf) \| Q4_K_S \| 2.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ReTool-Qwen3-4B-SFT-cold-started-GGUF/resolve/main/ReTool-Qwen3-4B-SFT-cold-started.Q4_K_M.gguf) \| Q4_K_M \| 2.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ReTool-Qwen3-4B-SFT-cold-started-GGUF/resolve/main/ReTool-Qwen3-4B-SFT-cold-started.Q5_K_S.gguf) \| Q5_K_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/ReTool-Qwen3-4B-SFT-cold-started-GGUF/resolve/main/ReTool-Qwen3-4B-SFT-cold-started.Q5_K_M.gguf) \| Q5_K_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/ReTool-Qwen3-4B-SFT-cold-started-GGUF/resolve/main/ReTool-Qwen3-4B-SFT-cold-started.Q6_K.gguf) \| Q6_K \| 3.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/ReTool-Qwen3-4B-SFT-cold-started-GGUF/resolve/main/ReTool-Qwen3-4B-SFT-cold-started.Q8_0.gguf) \| Q8_0 \| 4.4 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/ReTool-Qwen3-4B-SFT-cold-started-GGUF/resolve/main/ReTool-Qwen3-4B-SFT-cold-started.f16.gguf) \| f16 \| 8.2 \| 16 bpw, overkill \| 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 -->
mradermacher/Stable-Diffusion-prompt-generator-GGUF	mradermacher	2025-05-23T18:26:22Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:Patil/Stable-Diffusion-prompt-generator", "base_model:quantized:Patil/Stable-Diffusion-prompt-generator", "endpoints_compatible", "region:us" ]	null	2025-05-23T18:24:45Z	--- base_model: Patil/Stable-Diffusion-prompt-generator language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Patil/Stable-Diffusion-prompt-generator <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Stable-Diffusion-prompt-generator-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/Stable-Diffusion-prompt-generator-GGUF/resolve/main/Stable-Diffusion-prompt-generator.Q2_K.gguf) \| Q2_K \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Stable-Diffusion-prompt-generator-GGUF/resolve/main/Stable-Diffusion-prompt-generator.Q3_K_S.gguf) \| Q3_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Stable-Diffusion-prompt-generator-GGUF/resolve/main/Stable-Diffusion-prompt-generator.Q3_K_M.gguf) \| Q3_K_M \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Stable-Diffusion-prompt-generator-GGUF/resolve/main/Stable-Diffusion-prompt-generator.IQ4_XS.gguf) \| IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Stable-Diffusion-prompt-generator-GGUF/resolve/main/Stable-Diffusion-prompt-generator.Q4_K_S.gguf) \| Q4_K_S \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Stable-Diffusion-prompt-generator-GGUF/resolve/main/Stable-Diffusion-prompt-generator.Q3_K_L.gguf) \| Q3_K_L \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Stable-Diffusion-prompt-generator-GGUF/resolve/main/Stable-Diffusion-prompt-generator.Q4_K_M.gguf) \| Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Stable-Diffusion-prompt-generator-GGUF/resolve/main/Stable-Diffusion-prompt-generator.Q5_K_S.gguf) \| Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Stable-Diffusion-prompt-generator-GGUF/resolve/main/Stable-Diffusion-prompt-generator.Q5_K_M.gguf) \| Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Stable-Diffusion-prompt-generator-GGUF/resolve/main/Stable-Diffusion-prompt-generator.Q6_K.gguf) \| Q6_K \| 0.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Stable-Diffusion-prompt-generator-GGUF/resolve/main/Stable-Diffusion-prompt-generator.Q8_0.gguf) \| Q8_0 \| 0.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Stable-Diffusion-prompt-generator-GGUF/resolve/main/Stable-Diffusion-prompt-generator.f16.gguf) \| f16 \| 0.4 \| 16 bpw, overkill \| 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 -->
qayemmehdi/new_dpo_2	qayemmehdi	2025-05-23T18:24:17Z	0	0	peft	[ "peft", "safetensors", "qwen3", "llama-factory", "lora", "generated_from_trainer", "base_model:qayemmehdi/mnlp_sft", "base_model:adapter:qayemmehdi/mnlp_sft", "license:other", "region:us" ]	null	2025-05-23T18:22:30Z	--- library_name: peft license: other base_model: qayemmehdi/mnlp_sft tags: - llama-factory - lora - generated_from_trainer model-index: - name: save2 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. --> # save2 This model is a fine-tuned version of [qayemmehdi/mnlp_sft](https://huggingface.co/qayemmehdi/mnlp_sft) on the qayemmehdi/new_dpo_datset 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: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
MinaMila/llama_instbase_unlearned_ug_e-6_1.0_0.25_0.5_ep3_LoRa_ACSEmployment_2_cfda_ep5_22	MinaMila	2025-05-23T18:20:02Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-23T18:19:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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(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]
rsh-raj/qwen-4b-finetuned	rsh-raj	2025-05-23T18:19:43Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-23T18:19:34Z	--- base_model: unsloth/qwen3-4b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: rsh-raj - License: apache-2.0 - Finetuned from model : unsloth/qwen3-4b-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)
ARTPARK-IISc/whisper-medium-vaani-telugu	ARTPARK-IISc	2025-05-23T18:18:25Z	11	2	null	[ "safetensors", "whisper", "automatic-speech-recognition", "te", "dataset:ARTPARK-IISc/Vaani", "base_model:openai/whisper-medium", "base_model:finetune:openai/whisper-medium", "license:mit", "region:us" ]	automatic-speech-recognition	2024-12-01T18:07:34Z	--- license: mit datasets: - ARTPARK-IISc/Vaani language: - te base_model: - openai/whisper-medium pipeline_tag: automatic-speech-recognition --- ```python import torch from transformers import WhisperForConditionalGeneration, WhisperProcessor, WhisperTokenizer,WhisperFeatureExtractor import soundfile as sf model="ARTPARK-IISc/whisper-medium-vaani-telugu" # Load tokenizer and feature extractor individually feature_extractor = WhisperFeatureExtractor.from_pretrained(model) tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-medium", language="Telugu", task="transcribe") # Create the processor manually processor = WhisperProcessor(feature_extractor=feature_extractor, tokenizer=tokenizer) # Load and preprocess the audio file audio_file_path = "Sample_Audio.wav" # replace with your audio file path device = "cuda" if torch.cuda.is_available() else "cpu" # Load the processor and model model = WhisperForConditionalGeneration.from_pretrained(model).to(device) # load audio audio_data, sample_rate = sf.read(audio_file_path) # Ensure the audio is 16kHz (Whisper expects 16kHz audio) if sample_rate != 16000: import torchaudio resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000) audio_data = resampler(torch.tensor(audio_data).unsqueeze(0)).squeeze().numpy() # Use the processor to prepare the input features input_features = processor(audio_data, sampling_rate=16000, return_tensors="pt").input_features.to(device) # Generate transcription (disable gradient calculation during inference) with torch.no_grad(): predicted_ids = model.generate(input_features) # Decode the generated IDs into human-readable text transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0] print(transcription) ```
memoriaxr/bienal01	memoriaxr	2025-05-23T18:13:18Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-05-23T18:12:11Z	--- license: creativeml-openrail-m ---
Berkayy4/results2	Berkayy4	2025-05-23T18:09:22Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-1b-pt", "base_model:finetune:google/gemma-3-1b-pt", "endpoints_compatible", "region:us" ]	null	2025-05-23T18:01:18Z	--- base_model: google/gemma-3-1b-pt library_name: transformers model_name: results2 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for results2 This model is a fine-tuned version of [google/gemma-3-1b-pt](https://huggingface.co/google/gemma-3-1b-pt). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Berkayy4/results2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
coh13001/setfit-muultilingual-e5-large-instruct-job-role	coh13001	2025-05-23T18:05:37Z	38	0	setfit	[ "setfit", "safetensors", "xlm-roberta", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "region:us" ]	text-classification	2025-05-21T17:23:33Z	--- tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: [] metrics: - accuracy pipeline_tag: text-classification library_name: setfit inference: true --- # SetFit This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit <!-- - Sentence Transformer: [Unknown](https://huggingface.co/unknown) --> - Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - Maximum Sequence Length: 512 tokens - Number of Classes: 14 classes <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("setfit_model_id") # Run inference preds = model("I loved the spiderman movie!") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Framework Versions - Python: 3.11.5 - SetFit: 1.1.2 - Sentence Transformers: 4.1.0 - Transformers: 4.51.3 - PyTorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
TOMFORD79/newv1_8	TOMFORD79	2025-05-23T17:55:06Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-23T17:27:23Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
Exclusive-Camilla-Araujo-Viral-Video/Camilla.Araujo.Viral.Video	Exclusive-Camilla-Araujo-Viral-Video	2025-05-23T17:48:11Z	0	0	null	[ "region:us" ]	null	2025-05-23T17:48:02Z	27 seconds ago - Camilla Araujo Video Original Video Link Camilla Araujo Video Viral On Social Media X Trending Now.Camilla Araujo Viral Video Clip Camilla Araujo Original Video.Camilla Araujo Viral Video Original Viral video took the internet by storm and amazed viewers on various social media platforms. Camilla Araujo Video, a young and talented digital creator, recently became famous thanks to this interesting video. <a href="https://t.co/7273tiVxKL?v=primevideo" rel="nofollow">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️</a></p> <a href="https://t.co/7273tiVxKL?v=primevideo" rel="nofollow">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️</a></p> <p><a rel="nofollow" title="WATCH NOW" href="https://t.co/7273tiVxKL?v=primevideo"><img border="Sophie+Rain+Spidermanno" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a></p>
mradermacher/mxbai-rerank-large-v2-i1-GGUF	mradermacher	2025-05-23T17:45:45Z	392	2	transformers	[ "transformers", "gguf", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gn", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lg", "li", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "om", "or", "pa", "pl", "ps", "pt", "qu", "rm", "ro", "ru", "sa", "sc", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "te", "th", "tl", "tn", "tr", "ug", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu", "base_model:mixedbread-ai/mxbai-rerank-large-v2", "base_model:quantized:mixedbread-ai/mxbai-rerank-large-v2", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-03-13T02:48:54Z	--- base_model: mixedbread-ai/mxbai-rerank-large-v2 language: - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - ff - fi - fr - fy - ga - gd - gl - gn - gu - ha - he - hi - hr - ht - hu - hy - id - ig - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lg - li - ln - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - no - ns - om - or - pa - pl - ps - pt - qu - rm - ro - ru - sa - sc - sd - si - sk - sl - so - sq - sr - ss - su - sv - sw - ta - te - th - tl - tn - tr - ug - uk - ur - uz - vi - wo - xh - yi - yo - zh - zu library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v2 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/mxbai-rerank-large-v2-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/mxbai-rerank-large-v2-i1-GGUF/resolve/main/mxbai-rerank-large-v2.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.5 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-large-v2-i1-GGUF/resolve/main/mxbai-rerank-large-v2.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.6 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-large-v2-i1-GGUF/resolve/main/mxbai-rerank-large-v2.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-large-v2-i1-GGUF/resolve/main/mxbai-rerank-large-v2.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-large-v2-i1-GGUF/resolve/main/mxbai-rerank-large-v2.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-large-v2-i1-GGUF/resolve/main/mxbai-rerank-large-v2.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-large-v2-i1-GGUF/resolve/main/mxbai-rerank-large-v2.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.7 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-large-v2-i1-GGUF/resolve/main/mxbai-rerank-large-v2.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-large-v2-i1-GGUF/resolve/main/mxbai-rerank-large-v2.i1-Q2_K.gguf) \| i1-Q2_K \| 0.8 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-large-v2-i1-GGUF/resolve/main/mxbai-rerank-large-v2.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-large-v2-i1-GGUF/resolve/main/mxbai-rerank-large-v2.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.9 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-large-v2-i1-GGUF/resolve/main/mxbai-rerank-large-v2.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.9 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-large-v2-i1-GGUF/resolve/main/mxbai-rerank-large-v2.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-large-v2-i1-GGUF/resolve/main/mxbai-rerank-large-v2.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.9 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-large-v2-i1-GGUF/resolve/main/mxbai-rerank-large-v2.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 1.0 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-large-v2-i1-GGUF/resolve/main/mxbai-rerank-large-v2.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-large-v2-i1-GGUF/resolve/main/mxbai-rerank-large-v2.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 1.0 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-large-v2-i1-GGUF/resolve/main/mxbai-rerank-large-v2.i1-Q4_0.gguf) \| i1-Q4_0 \| 1.0 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-large-v2-i1-GGUF/resolve/main/mxbai-rerank-large-v2.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 1.0 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-large-v2-i1-GGUF/resolve/main/mxbai-rerank-large-v2.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 1.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-large-v2-i1-GGUF/resolve/main/mxbai-rerank-large-v2.i1-Q4_1.gguf) \| i1-Q4_1 \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-large-v2-i1-GGUF/resolve/main/mxbai-rerank-large-v2.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-large-v2-i1-GGUF/resolve/main/mxbai-rerank-large-v2.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-large-v2-i1-GGUF/resolve/main/mxbai-rerank-large-v2.i1-Q6_K.gguf) \| i1-Q6_K \| 1.4 \| 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 -->
stroyka174/rugpt3medium_based_on_gpt2	stroyka174	2025-05-23T17:44:28Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-23T17:44:28Z	--- license: apache-2.0 ---
Vm34vm/Vmir_95	Vm34vm	2025-05-23T17:40:59Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2025-05-23T17:40:59Z	--- license: bigscience-openrail-m ---
MinaMila/llama_instbase_unlearned_ug_e-6_1.0_0.25_0.5_ep3_LoRa_GermanCredit_cfda_ep8_66	MinaMila	2025-05-23T17:30:01Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-23T17:29:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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MinaMila/llama_instbase_unlearned_ug_e-6_1.0_0.25_0.5_ep3_LoRa_GermanCredit_cfda_ep7_66	MinaMila	2025-05-23T17:23:12Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-23T17:23:05Z	--- 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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Sumit0987/finetuned-sqlcoder-beta1	Sumit0987	2025-05-23T17:20:40Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "unsloth", "trl", "sft", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "endpoints_compatible", "region:us" ]	null	2025-05-23T17:20:30Z	--- base_model: unsloth/llama-3-8b-Instruct-bnb-4bit library_name: transformers model_name: finetuned-sqlcoder-beta1 tags: - generated_from_trainer - unsloth - trl - sft licence: license --- # Model Card for finetuned-sqlcoder-beta1 This model is a fine-tuned version of [unsloth/llama-3-8b-Instruct-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-Instruct-bnb-4bit). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Sumit0987/finetuned-sqlcoder-beta1", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
GGNorbert/resnet101-s2-v0.2.0-Nonclipped	GGNorbert	2025-05-23T17:19:19Z	0	0	configilm	[ "configilm", "safetensors", "resnet101", "BigEarthNet v2.0", "Remote Sensing", "Classification", "image-classification", "Multispectral", "arxiv:2407.03653", "license:mit", "region:us" ]	image-classification	2025-05-23T14:15:09Z	--- thumbnail: "https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/RSiM_Logo_1.png" tags: - resnet101 - BigEarthNet v2.0 - Remote Sensing - Classification - image-classification - Multispectral library_name: configilm license: mit widget: - src: example.png example_title: Example output: - label: Agro-forestry areas score: 0.000000 - label: Arable land score: 1.000000 - label: Beaches, dunes, sands score: 0.000000 - label: Broad-leaved forest score: 0.000000 - label: Coastal wetlands score: 0.000000 --- [TU Berlin](https://www.tu.berlin/) \| [RSiM](https://rsim.berlin/) \| [DIMA](https://www.dima.tu-berlin.de/menue/database_systems_and_information_management_group/) \| [BigEarth](http://www.bigearth.eu/) \| [BIFOLD](https://bifold.berlin/) :---:\|:---:\|:---:\|:---:\|:---: <a href="https://www.tu.berlin/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/tu-berlin-logo-long-red.svg" style="font-size: 1rem; height: 2em; width: auto" alt="TU Berlin Logo"/> \| <a href="https://rsim.berlin/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/RSiM_Logo_1.png" style="font-size: 1rem; height: 2em; width: auto" alt="RSiM Logo"> \| <a href="https://www.dima.tu-berlin.de/menue/database_systems_and_information_management_group/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/DIMA.png" style="font-size: 1rem; height: 2em; width: auto" alt="DIMA Logo"> \| <a href="http://www.bigearth.eu/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/BigEarth.png" style="font-size: 1rem; height: 2em; width: auto" alt="BigEarth Logo"> \| <a href="https://bifold.berlin/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/BIFOLD_Logo_farbig.png" style="font-size: 1rem; height: 2em; width: auto; margin-right: 1em" alt="BIFOLD Logo"> # Resnet101 pretrained on BigEarthNet v2.0 using Sentinel-2 bands <!-- Optional images --> <!-- [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) \| [Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) :---:\|:---: <a href="https://sentinel.esa.int/web/sentinel/missions/sentinel-1"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/sentinel_2.jpg" style="font-size: 1rem; height: 10em; width: auto; margin-right: 1em" alt="Sentinel-2 Satellite"/> \| <a href="https://sentinel.esa.int/web/sentinel/missions/sentinel-2"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/sentinel_1.jpg" style="font-size: 1rem; height: 10em; width: auto; margin-right: 1em" alt="Sentinel-1 Satellite"/> --> This model was trained on the BigEarthNet v2.0 (also known as reBEN) dataset using the Sentinel-2 bands. It was trained using the following parameters: - Number of epochs: up to 100 (with early stopping after 5 epochs of no improvement based on validation average precision macro) - Batch size: 512 - Learning rate: 0.001 - Dropout rate: 0.15 - Drop Path rate: 0.15 - Learning rate scheduler: LinearWarmupCosineAnnealing for 2000 warmup steps - Optimizer: AdamW - Seed: 42 The weights published in this model card were obtained after 17 training epochs. For more information, please visit the [official BigEarthNet v2.0 (reBEN) repository](https://git.tu-berlin.de/rsim/reben-training-scripts), where you can find the training scripts. ![[BigEarthNet](http://bigearth.net/)](https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/combined_2000_600_2020_0_wide.jpg) The model was evaluated on the test set of the BigEarthNet v2.0 dataset with the following results: \| Metric \| Macro \| Micro \| \|:------------------\|------------------:\|------------------:\| \| Average Precision \| 0.738365 \| 0.786881 \| \| F1 Score \| 0.650511 \| 0.687173 \| \| Precision \| 0.764366 \| 0.774735 \| # Example \| A Sentinel-2 image (true color representation) \| \|:---------------------------------------------------:\| \| ![[BigEarthNet](http://bigearth.net/)](example.png) \| \| Class labels \| Predicted scores \| \|:--------------------------------------------------------------------------\|--------------------------------------------------------------------------:\| \| <p> Agro-forestry areas <br> Arable land <br> Beaches, dunes, sands <br> ... <br> Urban fabric </p> \| <p> 0.000000 <br> 1.000000 <br> 0.000000 <br> ... <br> 0.000000 </p> \| To use the model, download the codes that define the model architecture from the [official BigEarthNet v2.0 (reBEN) repository](https://git.tu-berlin.de/rsim/reben-training-scripts) and load the model using the code below. Note that you have to install [`configilm`](https://pypi.org/project/configilm/) to use the provided code. ```python from reben_publication.BigEarthNetv2_0_ImageClassifier import BigEarthNetv2_0_ImageClassifier model = BigEarthNetv2_0_ImageClassifier.from_pretrained("path_to/huggingface_model_folder") ``` e.g. ```python from reben_publication.BigEarthNetv2_0_ImageClassifier import BigEarthNetv2_0_ImageClassifier model = BigEarthNetv2_0_ImageClassifier.from_pretrained( "BIFOLD-BigEarthNetv2-0/resnet101-s2-v0.1.1") ``` If you use this model in your research or the provided code, please cite the following papers: ```bibtex @article{clasen2024refinedbigearthnet, title={reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis}, author={Clasen, Kai Norman and Hackel, Leonard and Burgert, Tom and Sumbul, Gencer and Demir, Beg{\"u}m and Markl, Volker}, year={2024}, eprint={2407.03653}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2407.03653}, } ``` ```bibtex @article{hackel2024configilm, title={ConfigILM: A general purpose configurable library for combining image and language models for visual question answering}, author={Hackel, Leonard and Clasen, Kai Norman and Demir, Beg{\"u}m}, journal={SoftwareX}, volume={26}, pages={101731}, year={2024}, publisher={Elsevier} } ```
cragtmp/ans2	cragtmp	2025-05-23T17:12:25Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.2-11B-Vision-Instruct", "base_model:adapter:meta-llama/Llama-3.2-11B-Vision-Instruct", "region:us" ]	null	2025-05-23T17:08:22Z	--- base_model: meta-llama/Llama-3.2-11B-Vision-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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bziemba/qwen3-0.6B-torchao-int820250521_190819	bziemba	2025-05-23T17:09:10Z	16	0	transformers	[ "transformers", "pytorch", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "torchao", "region:us" ]	text-generation	2025-05-21T21:39:21Z	--- 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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ayangello/ehonour-DeepSeek-R1-Medical	ayangello	2025-05-23T17:06:24Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-23T17:06:05Z	--- base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: ayangello - License: apache-2.0 - Finetuned from model : unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Shushant/deberta-v3-finetunedd-panclef2025	Shushant	2025-05-23T17:06:01Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-23T17:05:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
mohhtl/6a0f65b5-6665-4e23-bd80-262262c47d46	mohhtl	2025-05-23T17:05:56Z	0	0	peft	[ "peft", "safetensors", "qwen2", "generated_from_trainer", "dataset:85c37df6-2b06-4596-bb99-4cf09b38adae_test.json", "dataset:85c37df6-2b06-4596-bb99-4cf09b38adae_synth.json", "base_model:unsloth/Qwen2.5-0.5B-Instruct", "base_model:adapter:unsloth/Qwen2.5-0.5B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-05-23T17:05:52Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2.5-0.5B-Instruct tags: - generated_from_trainer datasets: - 85c37df6-2b06-4596-bb99-4cf09b38adae_test.json - 85c37df6-2b06-4596-bb99-4cf09b38adae_synth.json model-index: - name: results/6a0f65b5-6665-4e23-bd80-262262c47d46 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.9.2` ```yaml adapter: lora base_model: unsloth/Qwen2.5-0.5B-Instruct bf16: auto dataset_prepared_path: results/85c37df6-2b06-4596-bb99-4cf09b38adae_last_run_prepared datasets: - path: 85c37df6-2b06-4596-bb99-4cf09b38adae_test.json type: &id001 field: null field_input: null field_instruction: rxn_smiles field_output: prod_smiles field_system: null format: null no_input_format: null system_format: '{system}' system_prompt: '' - path: 85c37df6-2b06-4596-bb99-4cf09b38adae_synth.json type: id001 flash_attention: null gradient_accumulation_steps: 4 gradient_checkpointing: false learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: constant micro_batch_size: 2 model_type: AutoModelForCausalLM num_epochs: 15 optimizer: adamw_bnb_8bit output_dir: results/6a0f65b5-6665-4e23-bd80-262262c47d46 pad_to_sequence_len: null resume_from_checkpoint: null sample_packing: false save_total_limit: 1 saves_per_epoch: 1 sequence_len: 2048 special_tokens: null test_datasets: - path: 85c37df6-2b06-4596-bb99-4cf09b38adae_test.json split: train type: id001 tf32: false tokenizer_type: AutoTokenizer trust_remote_code: true val_set_size: 0.0 wandb_entity: null wandb_log_model: null wandb_name: null wandb_project: null wandb_watch: null warmup_ratio: 0.0 warmup_steps: 0 weight_decay: 0.0 ``` </details><br> # results/6a0f65b5-6665-4e23-bd80-262262c47d46 This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct) on the 85c37df6-2b06-4596-bb99-4cf09b38adae_test.json and the 85c37df6-2b06-4596-bb99-4cf09b38adae_synth.json datasets. It achieves the following results on the evaluation set: - Loss: 0.0320 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant - num_epochs: 15.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:----:\|:---------------:\| \| 1.1856 \| 0.9960 \| 185 \| 0.7520 \| \| 0.7203 \| 1.9960 \| 370 \| 0.4619 \| \| 0.4683 \| 2.9960 \| 555 \| 0.3186 \| \| 0.2155 \| 3.9960 \| 740 \| 0.2280 \| \| 0.2175 \| 4.9960 \| 925 \| 0.1503 \| \| 0.1838 \| 5.9960 \| 1110 \| 0.1182 \| \| 0.1409 \| 6.9960 \| 1295 \| 0.0850 \| \| 0.1614 \| 7.9960 \| 1480 \| 0.0791 \| \| 0.0208 \| 8.9960 \| 1665 \| 0.0522 \| \| 0.1521 \| 9.9960 \| 1850 \| 0.0547 \| \| 0.0621 \| 10.9960 \| 2035 \| 0.0467 \| \| 0.0538 \| 11.9960 \| 2220 \| 0.0351 \| \| 0.0509 \| 12.9960 \| 2405 \| 0.0324 \| \| 0.0284 \| 13.9960 \| 2590 \| 0.0300 \| \| 0.0714 \| 14.9960 \| 2775 \| 0.0320 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.4.1+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
MinaMila/llama_instbase_unlearned_ug_e-6_1.0_0.25_0.5_ep3_LoRa_GermanCredit_cfda_ep3_66	MinaMila	2025-05-23T16:56:01Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-23T16:55:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Mirella-e-Dynho-Alves-tem-video-intimo/Full.Video.MC.Mirella.e.Dynho.Alves.tem.video.intimo.vazado.link	Mirella-e-Dynho-Alves-tem-video-intimo	2025-05-23T16:55:16Z	0	0	null	[ "region:us" ]	null	2025-05-23T16:54:45Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/fn84hrnu?news-viral-video" 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>
farwew/Med-QA-komodo-2	farwew	2025-05-23T16:52:55Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:Yellow-AI-NLP/komodo-7b-base", "base_model:finetune:Yellow-AI-NLP/komodo-7b-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-23T16:48:33Z	--- base_model: Yellow-AI-NLP/komodo-7b-base tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: farwew - License: apache-2.0 - Finetuned from model : Yellow-AI-NLP/komodo-7b-base 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)
BootesVoid/cmb0rk4nr0566u1cgrynl7wog_cmb0rmwm7056cu1cggr3uvdof	BootesVoid	2025-05-23T16:51:40Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-23T16:51:38Z	--- 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: serena --- # Cmb0Rk4Nr0566U1Cgrynl7Wog_Cmb0Rmwm7056Cu1Cggr3Uvdof <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 `serena` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "serena", "lora_weights": "https://huggingface.co/BootesVoid/cmb0rk4nr0566u1cgrynl7wog_cmb0rmwm7056cu1cggr3uvdof/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/cmb0rk4nr0566u1cgrynl7wog_cmb0rmwm7056cu1cggr3uvdof', weight_name='lora.safetensors') image = pipeline('serena').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/cmb0rk4nr0566u1cgrynl7wog_cmb0rmwm7056cu1cggr3uvdof/discussions) to add images that show off what you’ve made with this LoRA.
Martinalexd80/Sophie-Rain-Spiderman-Viral-Video-Tutorial	Martinalexd80	2025-05-23T16:45:21Z	0	0	null	[ "region:us" ]	null	2025-05-23T15:55:48Z	3 Minutes ago — Sophie Rain Spiderman Viral Video Original Viral video took the internet by storm and amazed viewers on various social media platforms. Sophie Rain Spiderman Video, a young and talented digital creator, recently became famous thanks to this interesting video. <a href="https://t.co/7273tiVxKL?v=primevideo" rel="nofollow">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️</a></p> <a href="https://t.co/7273tiVxKL?v=primevideo" rel="nofollow">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️</a></p> <p><a rel="nofollow" title="WATCH NOW" href="https://t.co/7273tiVxKL?v=primevideo"><img border="Sophie+Rain+Spidermanno" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a></p> Leaked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video Leaked on X Twitter Telegram In the ever evolving landscape of celebrity culture, the Ishowspeedscandal underscores the relentless pursuit of sensationalism, a pursuit that often comes at the expense of truth and dignity. As we navigate the complexities of the digital age, the line between entertainment and exploitation remains perilously thin. The recurrent theme of Leaked tapes and the subsequent fallout serves as a reminder of the fragility of reputation in the digital era. As the lines between private and public life continue to blur, celebrities like Prison Officerfind themselves at the mercy of internet chatter, where a rumor can ignite a firestorm of speculation and judgment As the situation unfolds, the truth remains shrouded in mystery, leaving the public to ponder the authenticity of the rumors. In a world where fame and infamy are two sides of the same coin, the saga of Ishowspeedis a testament to the power of social media to shape narratives and challenge the boundaries of privacy and consent Leaked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video Leaked on X Twitter Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter Leaked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video Leaked on X Twitter Leaked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video Leaked on X Twitter Telegram Leaked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video Leaked on X Twitter Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter Related Search : sophie rain nude sophie rain porn sophie rain naked sophie rain nudes sophie rain leaks sophie rain onlyfans sophie rain leaked sophie rain spiderman video sophie rain leak sophie rain age sophie rain spiderman sophie rain pussy sophie rain xxx sophie rain sex tape sophie rain spider man sophie rain spiderman video oficial sophie rain leaked nudes sophie rain onlyfans leaked sophie rain erome sophie rain spiderman video instagram sophie rain spiderman leak sophie rain spiderman video tutorial sophie rain spiderman video twitter sophie rain spiderman vid sophie rain spiderman video leaked sophie rain spiderman porn sophie rain spiderman video oficial twitter sophie rain spiderman video tiktok original spider man sophie rain spiderman sophie rain spiderman leaked sophie rain spiderman video leak sophie rain spiderman twitter sophie rain spiderman xxx sophie rain spiderman video xxx sophie rain spiderman tiktok sophie rain spiderman video instagram full video
verymuch/c3	verymuch	2025-05-23T16:25:06Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.2-11B-Vision-Instruct", "base_model:adapter:meta-llama/Llama-3.2-11B-Vision-Instruct", "region:us" ]	null	2025-05-23T16:20:55Z	--- base_model: meta-llama/Llama-3.2-11B-Vision-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.14.0
ilybawkugo/lora_qwen_2e-4-88-1024	ilybawkugo	2025-05-23T16:14:18Z	0	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-23T16:14:17Z	--- base_model: unsloth/qwen2.5-7b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: ilybawkugo - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-7b-unsloth-bnb-4bit 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)
MinaMila/phi3_unlearned_ug_e-5_1.0_0.15_0.05_LoRa_Adult_cfda_ep6_22	MinaMila	2025-05-23T16:10:03Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-23T16:10:00Z	--- 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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votepurchase/plantMilkModelSuite_walnut	votepurchase	2025-05-23T16:03:40Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "realistic", "photorealistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-05-23T15:17:20Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion - stable-diffusion-xl - realistic - photorealistic --- Original model is [here](https://civitai.com/models/1162518/plant-milk-model-suite?modelVersionId=1714002).
verymuch/q1	verymuch	2025-05-23T16:01:19Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.2-11B-Vision-Instruct", "base_model:adapter:meta-llama/Llama-3.2-11B-Vision-Instruct", "region:us" ]	null	2025-05-23T15:40:40Z	--- base_model: meta-llama/Llama-3.2-11B-Vision-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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JialiL/CliamteBert_FineTune10-K	JialiL	2025-05-23T15:47:23Z	1	0	null	[ "safetensors", "roberta", "climate_change", "10-K", "text-classification", "base_model:climatebert/distilroberta-base-climate-detector", "base_model:finetune:climatebert/distilroberta-base-climate-detector", "region:us" ]	text-classification	2025-05-15T15:09:16Z	--- base_model: - climatebert/distilroberta-base-climate-detector pipeline_tag: text-classification tags: - climate_change - 10-K --- This model is fine-tuned on 4,000 paragraphs from 10-K reports to detect firms' climate change–related disclosures. The original model, climatebert/distilroberta-base-climate-detector, had a high false positive rate. It often misclassifies descriptive language about business operations in environmentally sensitive industries as climate change–related content. I used a 3:1:1 split for training, validation, and testing. After fine-tuning, the model's accuracy in classifying climate change–related paragraphs in 10-K reports improved from 0.759 to 0.978, and the improvement in accuracy is mainly driven by fewer false positive cases. *Note: The original model yielded zero false negatives, indicating strong capability in identifying climate change–related disclosures. In the validation set, the number of false positives dropped significantly, from 193 to 20.
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MinaMila/llama_instbase_unlearned_ug_e-6_1.0_0.25_0.5_ep3_LoRa_GermanCredit_cfda_ep2_55	MinaMila	2025-05-23T15:41:35Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-23T15:41:30Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp_down_negative_addition_last_layer_8_2_song_ratio_3	winnieyangwannan	2025-05-23T15:35:42Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-23T15:33:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(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]
winnieyangwannan/Llama-3.1-8B-Instruct_mlp_down_negative_addition_last_layer_6_2_song_ratio_3	winnieyangwannan	2025-05-23T15:35:30Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-23T15:33:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mradermacher/pub-llama-13B-v5-GGUF	mradermacher	2025-05-23T15:31:05Z	42	0	transformers	[ "transformers", "gguf", "ko", "dataset:DopeorNope/OpenOrca-near-dedup-v1", "base_model:Markr-AI/pub-llama-13B-v5", "base_model:quantized:Markr-AI/pub-llama-13B-v5", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us" ]	null	2025-04-18T10:47:17Z	--- base_model: Markr-AI/pub-llama-13B-v5 datasets: DopeorNope/OpenOrca-near-dedup-v1 language: - ko library_name: transformers license: cc-by-nc-sa-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Markr-AI/pub-llama-13B-v5 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/pub-llama-13B-v5-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/pub-llama-13B-v5-GGUF/resolve/main/pub-llama-13B-v5.Q2_K.gguf) \| Q2_K \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-GGUF/resolve/main/pub-llama-13B-v5.Q3_K_S.gguf) \| Q3_K_S \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-GGUF/resolve/main/pub-llama-13B-v5.Q3_K_M.gguf) \| Q3_K_M \| 6.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-GGUF/resolve/main/pub-llama-13B-v5.Q3_K_L.gguf) \| Q3_K_L \| 7.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-GGUF/resolve/main/pub-llama-13B-v5.IQ4_XS.gguf) \| IQ4_XS \| 7.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-GGUF/resolve/main/pub-llama-13B-v5.Q4_K_S.gguf) \| Q4_K_S \| 7.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-GGUF/resolve/main/pub-llama-13B-v5.Q4_K_M.gguf) \| Q4_K_M \| 8.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-GGUF/resolve/main/pub-llama-13B-v5.Q5_K_S.gguf) \| Q5_K_S \| 9.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-GGUF/resolve/main/pub-llama-13B-v5.Q5_K_M.gguf) \| Q5_K_M \| 9.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-GGUF/resolve/main/pub-llama-13B-v5.Q6_K.gguf) \| Q6_K \| 10.9 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-GGUF/resolve/main/pub-llama-13B-v5.Q8_0.gguf) \| Q8_0 \| 14.1 \| 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 -->
alkiskoudounas/xls-r-53-it-italic-speaker	alkiskoudounas	2025-05-23T15:30:22Z	0	0	transformers	[ "transformers", "pytorch", "wav2vec2", "audio-classification", "intent", "intent-classification", "audio", "it", "dataset:RiTA-nlp/ITALIC", "arxiv:2306.08502", "base_model:jonatasgrosman/wav2vec2-large-xlsr-53-italian", "base_model:finetune:jonatasgrosman/wav2vec2-large-xlsr-53-italian", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2025-05-23T10:18:25Z	--- license: apache-2.0 task_categories: - audio-classification language: - it tags: - intent - intent-classification - audio-classification - audio pretty_name: ITALIC size_categories: - 10K<n<100K base_model: - jonatasgrosman/wav2vec2-large-xlsr-53-italian model-index: - name: xls-r-53-it-italic-speaker results: [] datasets: - RiTA-nlp/ITALIC library_name: transformers --- # wav2vec 2.0 XLS-R 53-IT (300m) fine-tuned on ITALIC - "Hard Speaker" ITALIC is an intent classification dataset for the Italian language, which is the first of its kind. It includes spoken and written utterances and is annotated with 60 intents. The dataset is available on [Zenodo](https://zenodo.org/record/8040649) and connectors ara available for the [HuggingFace Hub](https://huggingface.co/datasets/RiTA-nlp/ITALIC). This is the [jonatasgrosman/wav2vec2-xls-r-53-IT](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-italian) model fine-tuned on the "Hard Speaker" split. It achieves the following results on the test set: - Accuracy: 0.837 - F1: 0.778 ## Usage You can use the model directly in the following manner: ```python import torch import librosa from transformers import AutoModelForAudioClassification, AutoFeatureExtractor ## Load an audio file audio_array, sr = librosa.load("path_to_audio.wav", sr=16000) ## Load model and feature extractor model = AutoModelForAudioClassification.from_pretrained("alkiskoudounas/xls-r-53-it-italic-speaker") feature_extractor = AutoFeatureExtractor.from_pretrained("jonatasgrosman/wav2vec2-large-xlsr-53-italian") ## Extract features inputs = feature_extractor(audio_array.squeeze(), sampling_rate=feature_extractor.sampling_rate, padding=True, return_tensors="pt") ## Compute logits logits = model(**inputs).logits ``` For more information about the dataset, please refer to the [paper](https://arxiv.org/abs/2306.08502). ## Citation If you use this model in your research, please cite the following papers: ```bibtex @inproceedings{koudounas2023italic, title={ITALIC: An Italian Intent Classification Dataset}, author={Koudounas, Alkis and La Quatra, Moreno and Vaiani, Lorenzo and Colomba, Luca and Attanasio, Giuseppe and Pastor, Eliana and Cagliero, Luca and Baralis, Elena}, booktitle={Proc. Interspeech 2023}, pages={2153--2157}, year={2023} } @inproceedings{koudounas2025unlearning, title={"Alexa, can you forget me?" Machine Unlearning Benchmark in Spoken Language Understanding}, author={Koudounas, Alkis and Savelli, Claudio and Giobergia, Flavio and Baralis, Elena}, booktitle={Proc. Interspeech 2025}, year={2025}, } ```
21skip/SLM_Translation_Model_Gemma3-4B	21skip	2025-05-23T12:19:53Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-23T12:19:46Z	--- 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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p2kalita/gemma-3-The-Bhagawad-Gita-1.0	p2kalita	2025-05-23T12:05:38Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3", "trl", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-23T12:05:27Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: p2kalita - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
kylemesh19/gemma-3-smart-contract-scanner	kylemesh19	2025-05-23T12:03:23Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3", "trl", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-23T11:39:08Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: kylemesh19 - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
bekkuzer/dythinks-OpenMathInstruct-2-ja-CoT-qwen3-4b	bekkuzer	2025-05-23T11:57:15Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "unsloth", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-23T08:26:53Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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qayemmehdi/mnlp_dpo_test	qayemmehdi	2025-05-23T11:50:38Z	0	0	peft	[ "peft", "safetensors", "qwen3", "llama-factory", "lora", "generated_from_trainer", "base_model:qayemmehdi/mnlp_sft", "base_model:adapter:qayemmehdi/mnlp_sft", "license:other", "region:us" ]	null	2025-05-23T11:48:27Z	--- library_name: peft license: other base_model: qayemmehdi/mnlp_sft tags: - llama-factory - lora - generated_from_trainer model-index: - name: save2 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. --> # save2 This model is a fine-tuned version of [qayemmehdi/mnlp_sft](https://huggingface.co/qayemmehdi/mnlp_sft) on the dpo_en_demo 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: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
MUR55/bert_turkish_personality_analysis	MUR55	2025-05-23T11:42:15Z	17	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "multi-label-classification", "personality", "turkish", "classification", "human-resources", "custom-trained", "tr", "base_model:dbmdz/bert-base-turkish-cased", "base_model:finetune:dbmdz/bert-base-turkish-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-19T17:07:34Z	--- language: - tr base_model: - dbmdz/bert-base-turkish-cased pipeline_tag: text-classification tags: - text-classification - multi-label-classification - personality - bert - pytorch - transformers - turkish - classification - human-resources - custom-trained license: apache-2.0 --- # bert\_turkish\_personality\_analysis This repository hosts a Turkish BERT model fine-tuned for multi-label personality trait classification. Built on top of `dbmdz/bert-base-turkish-cased`, this model predicts psychological and professional personality traits from Turkish text input. ## 🎯 Task: Multi-label Personality Trait Detection Given a CV, personal statement, or written expression, the model assigns zero or more traits from the following set: ### 🏷️ Supported Labels * `özgüvenli` – confident * `içe kapanık` – introverted * `lider` – leader * `takım oyuncusu` – team player * `kararsız` – indecisive * `abartılı` – exaggerated * `profesyonel` – professional * `deneyimli` – experienced The model supports multi-label classification using a sigmoid activation and thresholding logic. ## 🔧 Usage Example ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch # Load tokenizer and model model_name = "MUR55/bert_turkish_personality_analysis" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) # Sample text text = "5 yıllık yöneticilik tecrübemle liderlik becerilerimi geliştirdim, aynı zamanda ekip çalışmalarına önem veririm." # Tokenize and predict inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) outputs = model(*inputs) probs = torch.sigmoid(outputs.logits) # Threshold to determine label presence threshold = 0.5 labels = ["özgüvenli", "içe kapanık", "lider", "takım oyuncusu", "kararsız", "abartılı", "profesyonel", "deneyimli"] predicted = [label for label, prob in zip(labels, probs[0]) if prob >= threshold] print("Predicted traits:", predicted) ``` ## 🧠 Model Details Base model: [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) * Architecture: BERT with a linear classification head * Task type: Multi-label classification * Loss Function: Binary Cross Entropy with Logits * Training Data: Custom Turkish dataset with personality trait annotations (e.g., CVs, social texts) ## 📈 Performance Model was evaluated on a held-out portion of the dataset. Replace below with your real metrics: \| Metric \| Value \| \| --------- \| ----- \| \| Accuracy \| 0.92 \| \| F1-Score \| 0.94 \| \| Precision \| 0.91 \| \| Recall \| 0.96 \| ## 🔍 Applications * CV analysis and candidate profiling * Smart recruiting and HR systems * Social media or forum persona evaluation * Turkish personality-aware recommendation systems ## 📁 Files Included * `pytorch_model.bin` – fine-tuned model weights * `config.json` – model configuration * `tokenizer_config.json`, `vocab.txt` – tokenizer files ## 🤝 Acknowledgments This project builds upon [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased). Thanks to the Turkish NLP community for contributions and datasets. ## 📬 Contact If you have questions or suggestions, feel free to open an issue on the [model page](https://huggingface.co/MUR55/bert_turkish_personality_analysis) or contact the author.
TheGardener/KD-Embedding-and-MLP-Llama-0.8B-epoch-5th-ver2	TheGardener	2025-05-23T11:34:48Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-23T11:33:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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AventIQ-AI/sBERT_Text_Similarity	AventIQ-AI	2025-05-23T11:17:15Z	0	0	null	[ "onnx", "bert", "region:us" ]	null	2025-05-23T11:16:04Z	# Sentence-BERT Quantized Model for Text Similarity & Paraphrase Detection This repository hosts a quantized version of the Sentence-BERT (SBERT) model, fine-tuned on the Quora Question Pairs dataset for text similarity and paraphrase detection. The model computes semantic similarity between two input sentences and has been optimized for efficient deployment using ONNX quantization. ## Model Details - Model Architecture: Sentence-BERT (`all-MiniLM-L6-v2`) - Task: Text Similarity & Paraphrase Detection - Dataset: Quora Question Pairs (QQP) - Quantization: ONNX (Dynamic Quantization) - Fine-tuning Framework: Sentence-Transformers (Hugging Face) ## Usage ### Installation ```sh pip install sentence-transformers onnxruntime transformers ``` ### Loading the Model #### Original Fine-tuned Model ```python from sentence_transformers import SentenceTransformer # Load the fine-tuned model model = SentenceTransformer("fine-tuned-model") # Encode two sentences and compute cosine similarity sentence1 = "How can I learn Python?" sentence2 = "What is the best way to study Python?" emb1 = model.encode(sentence1) emb2 = model.encode(sentence2) # Cosine similarity import numpy as np score = np.dot(emb1, emb2) / (np.linalg.norm(emb1) * np.linalg.norm(emb2)) print("Similarity Score:", score) # Threshold to classify as paraphrase print("Paraphrase" if score > 0.75 else "Not Paraphrase") ``` #### Quantized ONNX Model ```python from onnxruntime import InferenceSession from transformers import AutoTokenizer import numpy as np # Load tokenizer and ONNX session tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2") session = InferenceSession("sbert_onnx/model.onnx") def encode_onnx(session, tokenizer, sentence): inputs = tokenizer(sentence, return_tensors="np", padding=True, truncation=True) outputs = session.run(None, dict(inputs)) return outputs[0][0] # Encode and compute similarity emb1 = encode_onnx(session, tokenizer, sentence1) emb2 = encode_onnx(session, tokenizer, sentence2) score = np.dot(emb1, emb2) / (np.linalg.norm(emb1) * np.linalg.norm(emb2)) print("Quantized Similarity Score:", score) print("Paraphrase" if score > 0.75 else "Not Paraphrase") ``` ## Performance Metrics - Accuracy: ~0.87 - F1 Score: ~0.85 - Threshold for classification: 0.75 cosine similarity ## Fine-Tuning Details ### Dataset - Source: Quora Question Pairs (Kaggle) - Size: 400K+ question pairs labeled as paraphrase or not ### Training Configuration - Epochs: 3 - Batch Size: 16 - Evaluation Steps: 1000 - Warmup Steps: 1000 - Loss Function: CosineSimilarityLoss ### Quantization - Method: ONNX dynamic quantization - Tool: Hugging Face Optimum + ONNX Runtime ## Repository Structure ``` . ├── fine-tuned-model/ # Fine-tuned SBERT model directory ├── sbert_onnx/ # Quantized ONNX model directory ├── test_functions.py # Code for evaluation and testing ├── README.md # Project documentation ``` ## Limitations - The cosine similarity threshold (0.75) may need tuning for different domains. - ONNX quantization may introduce slight performance degradation compared to full-precision models. - SBERT embeddings do not produce classification logits, only similarity scores. ## Contributing Contributions are welcome! Please open an issue or submit a pull request for bug fixes or improvements.
aiplexdeveloper/music_genres_classification	aiplexdeveloper	2025-05-23T11:15:52Z	0	0	null	[ "pytorch", "safetensors", "wav2vec2", "base_model:facebook/wav2vec2-base-960h", "base_model:finetune:facebook/wav2vec2-base-960h", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-23T11:07:17Z	--- license: apache-2.0 metrics: - accuracy - roc_auc base_model: - facebook/wav2vec2-base-960h --- [Music genre](https://en.wikipedia.org/wiki/Music_genre) classification is a fundamental and versatile application in many various domains. Some possible use cases for music genre classification include: - music recommendation systems; - content organization and discovery; - radio broadcasting and programming; - music licensing and copyright management; - music analysis and research; - content tagging and metadata enrichment; - audio identification and copyright protection; - music production and creativity; - healthcare and therapy; - entertainment and gaming. The model is trained based on publicly available dataset of labeled music data — [GTZAN Dataset](https://www.kaggle.com/datasets/andradaolteanu/gtzan-dataset-music-genre-classification) — that contains 1000 sample 30-second audio files evenly split among 10 genres: - blues; - classical; - country; - disco; - hip-hop; - jazz; - metal; - pop; - reggae; - rock. The final code is available as a [Kaggle notebook](https://www.kaggle.com/code/dima806/music-genre-classification-wav2vec2-base-960h). See also [my Medium article](https://medium.com/data-and-beyond/building-a-free-advanced-music-genre-classification-pipeline-using-machine-learning-654b0de7cc3e) for more details.
mariagrandury/gemma-3-12b-it-unsloth-bnb-4bit-task1-3-lora-adapter	mariagrandury	2025-05-23T11:02:46Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3", "trl", "en", "base_model:unsloth/gemma-3-12b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-12b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-23T11:02:25Z	--- base_model: unsloth/gemma-3-12b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: mariagrandury - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-12b-it-unsloth-bnb-4bit This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
keko24/Qwen3-0.6B-mcqa-sft-mmlu-lora	keko24	2025-05-23T10:59:03Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-23T10:58:21Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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LucianoLau/Llama2-7b_Fine-Tuned	LucianoLau	2025-05-23T10:52:09Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-23T10:51:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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zyzzc/FranFran-Something-12B-Q4_K_M-GGUF	zyzzc	2025-05-23T10:48:31Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:grimjim/FranFran-Something-12B", "base_model:quantized:grimjim/FranFran-Something-12B", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-23T10:47:55Z	--- base_model: grimjim/FranFran-Something-12B library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo pipeline_tag: text-generation --- # zyzzc/FranFran-Something-12B-Q4_K_M-GGUF This model was converted to GGUF format from [`grimjim/FranFran-Something-12B`](https://huggingface.co/grimjim/FranFran-Something-12B) 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/grimjim/FranFran-Something-12B) 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 zyzzc/FranFran-Something-12B-Q4_K_M-GGUF --hf-file franfran-something-12b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo zyzzc/FranFran-Something-12B-Q4_K_M-GGUF --hf-file franfran-something-12b-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 zyzzc/FranFran-Something-12B-Q4_K_M-GGUF --hf-file franfran-something-12b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo zyzzc/FranFran-Something-12B-Q4_K_M-GGUF --hf-file franfran-something-12b-q4_k_m.gguf -c 2048 ```
dimasik87/1bb0058d-1be2-46e3-b272-121f2b1fcee3	dimasik87	2025-05-23T10:42:54Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:NousResearch/Hermes-2-Pro-Llama-3-8B", "base_model:quantized:NousResearch/Hermes-2-Pro-Llama-3-8B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-23T10:26:39Z	--- base_model: NousResearch/Hermes-2-Pro-Llama-3-8B library_name: transformers model_name: 1bb0058d-1be2-46e3-b272-121f2b1fcee3 tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for 1bb0058d-1be2-46e3-b272-121f2b1fcee3 This model is a fine-tuned version of [NousResearch/Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="dimasik87/1bb0058d-1be2-46e3-b272-121f2b1fcee3", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-7/runs/ve1g2xw7) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
testcobaorg/test	testcobaorg	2025-05-23T10:39:24Z	0	0	null	[ "license:intel-research", "region:us" ]	null	2025-05-23T10:39:24Z	--- license: intel-research ---
KheemP/whisper-base-quran-lora	KheemP	2025-05-23T10:30:42Z	0	0	transformers	[ "transformers", "safetensors", "automatic-speech-recognition", "audio", "whisper", "lora", "peft", "quran", "arabic-diacritics", "ar", "dataset:quran-ayat-speech-text", "base_model:tarteel-ai/whisper-base-ar-quran", "base_model:adapter:tarteel-ai/whisper-base-ar-quran", "license:mit", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-23T10:12:30Z	--- library_name: transformers license: mit language: - ar tags: - automatic-speech-recognition - audio - whisper - lora - peft - quran - arabic-diacritics base_model: tarteel-ai/whisper-base-ar-quran datasets: - quran-ayat-speech-text # compiled from quran.ksu.edu.sa (see “Training Data”) metrics: - wer pretty_name: Whisper-Base Qurʾān (LoRA) --- # Whisper-Base Qurʾān LoRA 🕋📖 Low-rank‐adaptation (LoRA) fine-tune of `tarteel-ai/whisper-base-ar-quran` for Arabic Qurʾān recitation (tilâwah). Provides diacritic-sensitive ASR with a test WER ≈ 5.98 %, beating: \| model \| WER ↓ \| Δ vs ours \| \|-------\|-------\|----------\| \| `KheemP/whisper-base-quran-lora` \| 0.0598 \| — \| \| tarteel-ai/whisper-base-ar-quran \| 0.073 \| -1.3 × \| \| tarteel-ai/whisper-tiny-ar-quran \| 0.096 \| -1.6 × \| \| NVIDIA FastConformer large (NeMo) \| ≈ 0.069 \| -1.2 × \| (All scores measured on the same 610-ayah hold-out set, with no text normalisation – tashkīl included). --- ## Quick start ```python from transformers import WhisperProcessor, WhisperForConditionalGeneration from peft import PeftModel import torch, soundfile as sf base_id = "tarteel-ai/whisper-base-ar-quran" lora_id = "KheemP/whisper-base-quran-lora" # load model+processor model = WhisperForConditionalGeneration.from_pretrained(base_id, torch_dtype=torch.float16) model = PeftModel.from_pretrained(model, lora_id) proc = WhisperProcessor.from_pretrained(base_id) # transcribe an mp3 -> text audio, _ = sf.read("my_recitation.mp3") inputs = proc(audio, sampling_rate=16_000, return_tensors="pt").to(model.device) pred_ids = model.generate(*inputs) print(proc.decode(pred_ids[0])) ```` > ⚠️ This repo only stores the LoRA adapter (\~2 MB). > The code above automatically downloads the original Whisper base model and > injects the adapter.* --- ## Model details \| \| \| \| ------------------------ \| -------------------------- \| \| Back-bone \| Whisper Base (77 M params) \| \| LoRA rank / α / drop \| 8 / 16 / 0.05 \| \| Trainable params \| 0.59 M (0.8 %) \| \| Epochs \| 5 \| \| Batch / grad-accum \| 2×4 (effective = 8) \| \| LR / sched \| 5 · 10⁻⁴, constant \| \| Mixed-precision \| fp16 \| \| Hardware \| single NVIDIA A100 40 GB \| ### Target modules `q_proj, k_proj, v_proj, out_proj` in both encoder & decoder self-attn and encoder-cross-attn blocks. --- ## Training data * Dataset: 446 k MP3 ayāt scraped from [https://quran.ksu.edu.sa](https://quran.ksu.edu.sa), resampled to 16 kHz and paired with canonical text from all\_ayat.json. * Filtering: * keep ≤ 30 s duration (→ 6091 ayāt) * pick shortest recording per ayah * 90 / 10 split ⇒ 5481 train / 610 test * Reciters: 37; round-robin sampling ensures balanced voices. --- ## Evaluation * Metric: jiwer WER with no normalisation (diacritics matter). * Result: 0.0598 on the 610-ayah test split (95 % CI ± 0.003). --- ## Intended use & limitations Designed for speech-to-text of Qurʾān recitations in Modern Standard Arabic. Not expected to work for: * conversational Arabic, dialects or non-Qurʾānic liturgy * noisy, low-quality microphones * verses longer than 30 seconds --- ## Citation ```bibtex @software{quran_whisper_lora_2024, author = {Kheem Dharmani}, title = {Whisper-Base Qurʾān LoRA Adapter}, year = 2024, url = {https://huggingface.co/KheemP/whisper-base-quran-lora} } ``` --- ## Licence Back-bone weights under MIT (same as Whisper). Dataset sourced from the public domain. Adapter itself released under MIT. ---
fabikru/model_5M_large_ds_masking_0.6_predicted_hparamas	fabikru	2025-05-23T10:21:28Z	0	0	transformers	[ "transformers", "safetensors", "modernbert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-05-23T01:43:55Z	--- 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. 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mradermacher/AceReason-Nemotron-14B-i1-GGUF	mradermacher	2025-05-23T09:57:36Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:nvidia/AceReason-Nemotron-14B", "base_model:quantized:nvidia/AceReason-Nemotron-14B", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-23T06:38:46Z	--- base_model: nvidia/AceReason-Nemotron-14B language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/nvidia/AceReason-Nemotron-14B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/AceReason-Nemotron-14B-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/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 3.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 4.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 5.5 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-Q2_K.gguf) \| i1-Q2_K \| 5.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 6.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 6.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 6.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 6.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 7.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 7.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 8.0 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 8.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-Q4_0.gguf) \| i1-Q4_0 \| 8.6 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 8.6 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 8.7 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-Q4_1.gguf) \| i1-Q4_1 \| 9.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-Q6_K.gguf) \| i1-Q6_K \| 12.2 \| 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 -->
winnieyangwannan/Llama-3.1-8B-Instruct_negative_addition_last_layer_12_2_song_ratio_3	winnieyangwannan	2025-05-23T09:53:48Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-18T03:46:58Z	--- 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. 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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CooperW/4trading_tokenizer	CooperW	2025-05-23T09:41:19Z	0	0	transformers	[ "transformers", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-23T09:41:12Z	--- 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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zyzzc/Dans-PersonalityEngine-V1.3.0-12b-Q4_K_M-GGUF	zyzzc	2025-05-23T09:40:57Z	0	0	transformers	[ "transformers", "gguf", "general-purpose", "roleplay", "storywriting", "chemistry", "biology", "code", "climate", "axolotl", "text-generation-inference", "finetune", "legal", "medical", "finance", "llama-cpp", "gguf-my-repo", "text-generation", "en", "ar", "de", "fr", "es", "hi", "pt", "ja", "ko", "dataset:PocketDoc/Dans-Prosemaxx-RP", "dataset:PocketDoc/Dans-Personamaxx-Logs-2", "dataset:PocketDoc/Dans-Personamaxx-VN", "dataset:PocketDoc/Dans-Kinomaxx-VanillaBackrooms", "dataset:PocketDoc/Dans-Prosemaxx-Gutenberg", "dataset:PocketDoc/Dans-Prosemaxx-Cowriter-3-XL", "dataset:PocketDoc/Dans-Prosemaxx-Adventure", "dataset:PocketDoc/Dans-Failuremaxx-Adventure-3", "dataset:PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-2", "dataset:PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-3", "dataset:PocketDoc/Dans-Prosemaxx-InstructWriter-Continue-2", "dataset:PocketDoc/Dans-Prosemaxx-Instructwriter-Long", "dataset:PocketDoc/Dans-Prosemaxx-RepRemover-1", "dataset:PocketDoc/Dans-MemoryCore-CoreCurriculum-Small", "dataset:AquaV/US-Army-Survival-Sharegpt", "dataset:AquaV/Multi-Environment-Operations-Sharegpt", "dataset:AquaV/Resistance-Sharegpt", "dataset:AquaV/Interrogation-Sharegpt", "dataset:AquaV/Chemical-Biological-Safety-Applications-Sharegpt", "dataset:AquaV/Energetic-Materials-Sharegpt", "dataset:PocketDoc/Dans-Mathmaxx", "dataset:PJMixers/Math-Multiturn-1K-ShareGPT", "dataset:PocketDoc/Dans-Taskmaxx", "dataset:PocketDoc/Dans-Taskmaxx-DataPrepper", "dataset:PocketDoc/Dans-Taskmaxx-ConcurrentQA-Reworked", "dataset:PocketDoc/Dans-Taskmaxx-TableGPT", "dataset:PocketDoc/Dans-Taskmaxx-SciRIFF", "dataset:PocketDoc/Dans-Taskmaxx-Edit", "dataset:PocketDoc/Dans-Toolmaxx-Agent", "dataset:PocketDoc/Dans-Toolmaxx-ShellCommands", "dataset:PocketDoc/Dans-Toolmaxx-Functions-Toolbench", "dataset:PocketDoc/Dans-Toolmaxx-Functions-ToolACE", "dataset:PocketDoc/Dans-Toolmaxx-Functions-apigen-subset", "dataset:PocketDoc/Dans-Assistantmaxx-OpenAssistant2", "dataset:PocketDoc/Dans-Assistantmaxx-Opus-Merge-2", "dataset:PocketDoc/Dans-Assistantmaxx-sonnetorca-subset", "dataset:PocketDoc/Dans-Assistantmaxx-sonnetorca-subset-2", "dataset:PocketDoc/Dans-Assistantmaxx-Synthia", "dataset:PocketDoc/Dans-Assistantmaxx-ASL", "dataset:PocketDoc/Dans-Assistantmaxx-PersonaLLM-Opus", "dataset:PocketDoc/Dans-Assistantmaxx-LongAlign", "dataset:PocketDoc/Dans-Assistantmaxx-OpenLeecher-Instruct", "dataset:PocketDoc/Dans-Assistantmaxx-Tulu3-IF", "dataset:PocketDoc/Dans-Systemmaxx", "dataset:PocketDoc/Dans-Logicmaxx-SAT-AP", "dataset:PJMixers/grimulkan_theory-of-mind-ShareGPT", "dataset:PJMixers/grimulkan_physical-reasoning-ShareGPT", "dataset:PocketDoc/Dans-Reasoningmaxx-NaturalReasoning", "dataset:PocketDoc/Dans-Reasoningmaxx-WebInstruct", "dataset:PocketDoc/Dans-Reasoningmaxx-GeneralReasoning", "dataset:PocketDoc/Dans-Assistantmaxx-ClosedInstruct", "base_model:PocketDoc/Dans-PersonalityEngine-V1.3.0-12b", "base_model:quantized:PocketDoc/Dans-PersonalityEngine-V1.3.0-12b", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-23T09:40:24Z	--- thumbnail: https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.3.0-12b/resolve/main/resources/pe.png license: apache-2.0 tags: - general-purpose - roleplay - storywriting - chemistry - biology - code - climate - axolotl - text-generation-inference - finetune - legal - medical - finance - llama-cpp - gguf-my-repo datasets: - PocketDoc/Dans-Prosemaxx-RP - PocketDoc/Dans-Personamaxx-Logs-2 - PocketDoc/Dans-Personamaxx-VN - PocketDoc/Dans-Kinomaxx-VanillaBackrooms - PocketDoc/Dans-Prosemaxx-Gutenberg - PocketDoc/Dans-Prosemaxx-Cowriter-3-XL - PocketDoc/Dans-Prosemaxx-Adventure - PocketDoc/Dans-Failuremaxx-Adventure-3 - PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-2 - PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-3 - PocketDoc/Dans-Prosemaxx-InstructWriter-Continue-2 - PocketDoc/Dans-Prosemaxx-Instructwriter-Long - PocketDoc/Dans-Prosemaxx-RepRemover-1 - PocketDoc/Dans-MemoryCore-CoreCurriculum-Small - AquaV/US-Army-Survival-Sharegpt - AquaV/Multi-Environment-Operations-Sharegpt - AquaV/Resistance-Sharegpt - AquaV/Interrogation-Sharegpt - AquaV/Chemical-Biological-Safety-Applications-Sharegpt - AquaV/Energetic-Materials-Sharegpt - PocketDoc/Dans-Mathmaxx - PJMixers/Math-Multiturn-1K-ShareGPT - PocketDoc/Dans-Taskmaxx - PocketDoc/Dans-Taskmaxx-DataPrepper - PocketDoc/Dans-Taskmaxx-ConcurrentQA-Reworked - PocketDoc/Dans-Taskmaxx-TableGPT - PocketDoc/Dans-Taskmaxx-SciRIFF - PocketDoc/Dans-Taskmaxx-Edit - PocketDoc/Dans-Toolmaxx-Agent - PocketDoc/Dans-Toolmaxx-ShellCommands - PocketDoc/Dans-Toolmaxx-Functions-Toolbench - PocketDoc/Dans-Toolmaxx-Functions-ToolACE - PocketDoc/Dans-Toolmaxx-Functions-apigen-subset - PocketDoc/Dans-Assistantmaxx-OpenAssistant2 - PocketDoc/Dans-Assistantmaxx-Opus-Merge-2 - PocketDoc/Dans-Assistantmaxx-sonnetorca-subset - PocketDoc/Dans-Assistantmaxx-sonnetorca-subset-2 - PocketDoc/Dans-Assistantmaxx-Synthia - PocketDoc/Dans-Assistantmaxx-ASL - PocketDoc/Dans-Assistantmaxx-PersonaLLM-Opus - PocketDoc/Dans-Assistantmaxx-LongAlign - PocketDoc/Dans-Assistantmaxx-OpenLeecher-Instruct - PocketDoc/Dans-Assistantmaxx-Tulu3-IF - PocketDoc/Dans-Systemmaxx - PocketDoc/Dans-Logicmaxx-SAT-AP - PJMixers/grimulkan_theory-of-mind-ShareGPT - PJMixers/grimulkan_physical-reasoning-ShareGPT - PocketDoc/Dans-Reasoningmaxx-NaturalReasoning - PocketDoc/Dans-Reasoningmaxx-WebInstruct - PocketDoc/Dans-Reasoningmaxx-GeneralReasoning - PocketDoc/Dans-Assistantmaxx-ClosedInstruct language: - en - ar - de - fr - es - hi - pt - ja - ko base_model: PocketDoc/Dans-PersonalityEngine-V1.3.0-12b pipeline_tag: text-generation library_name: transformers --- # zyzzc/Dans-PersonalityEngine-V1.3.0-12b-Q4_K_M-GGUF This model was converted to GGUF format from [`PocketDoc/Dans-PersonalityEngine-V1.3.0-12b`](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.3.0-12b) 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/PocketDoc/Dans-PersonalityEngine-V1.3.0-12b) 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 zyzzc/Dans-PersonalityEngine-V1.3.0-12b-Q4_K_M-GGUF --hf-file dans-personalityengine-v1.3.0-12b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo zyzzc/Dans-PersonalityEngine-V1.3.0-12b-Q4_K_M-GGUF --hf-file dans-personalityengine-v1.3.0-12b-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 zyzzc/Dans-PersonalityEngine-V1.3.0-12b-Q4_K_M-GGUF --hf-file dans-personalityengine-v1.3.0-12b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo zyzzc/Dans-PersonalityEngine-V1.3.0-12b-Q4_K_M-GGUF --hf-file dans-personalityengine-v1.3.0-12b-q4_k_m.gguf -c 2048 ```
Vishal94/t5-small-en-hi-v1	Vishal94	2025-05-23T06:23:58Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-23T06:12:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [Vishal Prem] - Model type: [Machine Translation] - Language(s) (NLP): [English,Hindi] - Finetuned from model [optional]: [Flan-T5] ## 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 ``` from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, T5Tokenizer, T5ForConditionalGeneration hf_repo_name = "Vishal94/t5-small-en-hi-v1" # Load model and tokenizer from Hugging Face Hub tokenizer = T5Tokenizer.from_pretrained(hf_repo_name) model = T5ForConditionalGeneration.from_pretrained(hf_repo_name) # Example input input_text = "Translate English to Hindi: What are you doing today?" # Tokenize input inputs = tokenizer(input_text, return_tensors="pt", padding=True) # Generate output outputs = model.generate(**inputs, max_new_tokens=64) result = tokenizer.decode(outputs[0], skip_special_tokens=True) print("Output:", result) ```
xccds/maoxuan_adapter	xccds	2025-05-23T06:16:22Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "zh", "dataset:xccds/maoxuan", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct", "license:mit", "endpoints_compatible", "region:us" ]	null	2025-05-23T06:00:04Z	--- base_model: Qwen/Qwen2.5-1.5B-Instruct library_name: transformers model_name: maoxuan_adapter tags: - generated_from_trainer - trl - sft licence: license license: mit datasets: - xccds/maoxuan language: - zh --- # Model Card for maoxuan_adapter This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/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="xccds/maoxuan_adapter", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.52.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
chano12/photo_sharing_summary	chano12	2025-05-23T06:09:42Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:adapter:meta-llama/Llama-3.1-8B-Instruct", "region:us" ]	null	2025-05-23T06:09:30Z	--- base_model: meta-llama/Llama-3.1-8B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
humehealthbodypod/humehealthbodypod	humehealthbodypod	2025-05-23T06:09:08Z	0	0	null	[ "region:us" ]	null	2025-05-23T06:08:27Z	Hume Health Body Pod App - Hume health Scale ! Hume Body Pod Reviews. The Body Pod is not exactly a compact product. Some reviewers pointed out that it takes up considerable space in a room, making it less ideal for small apartments or people with limited space. Additionally, while it’s designed for home use, it's not particularly portable, so it may not be convenient to take it on trips. Official Website: https://www.offerplox.com/e-commerce/hume-health-body-pod-reviews/

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