Split (1)

train · 790k rows

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags sequence	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
mradermacher/gpt2-medium-2-stable-diffusion-prompt-generator-i1-GGUF	mradermacher	2025-05-24T06:15:44Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:Ar4ikov/gpt2-medium-2-stable-diffusion-prompt-generator", "base_model:quantized:Ar4ikov/gpt2-medium-2-stable-diffusion-prompt-generator", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-05-24T06:04:08Z	--- base_model: Ar4ikov/gpt2-medium-2-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-2-stable-diffusion-prompt-generator <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/gpt2-medium-2-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-2-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-2-stable-diffusion-prompt-generator.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.2 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-2-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-2-stable-diffusion-prompt-generator.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.2 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-2-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-2-stable-diffusion-prompt-generator.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-2-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-2-stable-diffusion-prompt-generator.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-2-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-2-stable-diffusion-prompt-generator.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-2-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-2-stable-diffusion-prompt-generator.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-2-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-2-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-2-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-2-stable-diffusion-prompt-generator.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-2-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-2-stable-diffusion-prompt-generator.i1-Q2_K.gguf) \| i1-Q2_K \| 0.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-2-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-2-stable-diffusion-prompt-generator.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-2-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-2-stable-diffusion-prompt-generator.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-2-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-2-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-2-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-2-stable-diffusion-prompt-generator.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-2-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-2-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-2-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-2-stable-diffusion-prompt-generator.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-2-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-2-stable-diffusion-prompt-generator.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 0.3 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-2-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-2-stable-diffusion-prompt-generator.i1-Q4_0.gguf) \| i1-Q4_0 \| 0.3 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-2-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-2-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-2-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-2-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-2-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-2-stable-diffusion-prompt-generator.i1-Q4_1.gguf) \| i1-Q4_1 \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-2-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-2-stable-diffusion-prompt-generator.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 0.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-2-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-2-stable-diffusion-prompt-generator.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-2-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-2-stable-diffusion-prompt-generator.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/gpt2-medium-2-stable-diffusion-prompt-generator-i1-GGUF/resolve/main/gpt2-medium-2-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 -->
gotinha-iml-portal-zacarias-Viral-Video/FULL.VIDEO.LINK.gotinha.iml.Viral.Video.Leaks.Official	gotinha-iml-portal-zacarias-Viral-Video	2025-05-24T06:14:52Z	0	0	null	[ "region:us" ]	null	2025-05-24T06:14:34Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
mci29/sn29_q1m6_fnop	mci29	2025-05-24T06:13:50Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T06:10:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
video-18-katrina-lim-viral-kiffy-viral/video-18-katrina-lim-viral-kiffy-viral-video-full-video-original-clip	video-18-katrina-lim-viral-kiffy-viral	2025-05-24T06:12:46Z	0	0	null	[ "region:us" ]	null	2025-05-24T06:12:19Z	<animated-image data-catalyst=""><a href="https://wtach.club/leakvideo/?h" 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> Katrina Lim Kiffy, a rising digital content creator, recently went viral after a leaked video began circulating across various social media platforms, including Twitter and TikTok. The video quickly gained traction, capturing the attention of thousands of viewers and sparking widespread discussion online. The original clip, which showcases Katrina's talent and presence, was reportedly leaked without her consent, raising concerns about digital privacy and content sharing ethics. Despite the controversy, the viral moment has significantly boosted her visibility online. Viewers continue to search for the original video, making “Katrina Lim Kiffy viral video” a trending topic across major platforms.
khuam/gemma-fine-tuning-confidential	khuam	2025-05-24T06:12:07Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-4b-it", "base_model:finetune:google/gemma-3-4b-it", "endpoints_compatible", "region:us" ]	null	2025-05-18T13:45:56Z	--- base_model: google/gemma-3-4b-it library_name: transformers model_name: gemma-fine-tuning-confidential tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-fine-tuning-confidential This model is a fine-tuned version of [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it). 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="khuam/gemma-fine-tuning-confidential", 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: 2.8.0.dev20250518+cu126 - 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}} } ```
mradermacher/aaronGPTalpha-i1-GGUF	mradermacher	2025-05-24T06:10:53Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:totallynotbrent/aaronGPTalpha", "base_model:quantized:totallynotbrent/aaronGPTalpha", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-05-24T06:05:11Z	--- base_model: totallynotbrent/aaronGPTalpha 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/totallynotbrent/aaronGPTalpha <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/aaronGPTalpha-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/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.2 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.2 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-Q2_K.gguf) \| i1-Q2_K \| 0.2 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.2 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.2 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 0.2 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-Q4_0.gguf) \| i1-Q4_0 \| 0.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 0.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 0.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-Q4_1.gguf) \| i1-Q4_1 \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/aaronGPTalpha-i1-GGUF/resolve/main/aaronGPTalpha.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 -->
DanHauri/model	DanHauri	2025-05-24T06:10:51Z	0	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T05:53:25Z	--- base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: DanHauri - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-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)
mradermacher/Pythia410m-Instruct-SFT-i1-GGUF	mradermacher	2025-05-24T06:08:51Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:SummerSigh/Pythia410m-Instruct-SFT", "base_model:quantized:SummerSigh/Pythia410m-Instruct-SFT", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-05-24T05:53:40Z	--- base_model: SummerSigh/Pythia410m-Instruct-SFT 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/SummerSigh/Pythia410m-Instruct-SFT <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-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/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.2 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.2 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.3 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-Q2_K.gguf) \| i1-Q2_K \| 0.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.3 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.3 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 0.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 0.3 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-Q4_0.gguf) \| i1-Q4_0 \| 0.3 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 0.3 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-Q4_1.gguf) \| i1-Q4_1 \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 0.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Pythia410m-Instruct-SFT-i1-GGUF/resolve/main/Pythia410m-Instruct-SFT.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 -->
ShowMakerTAT/OLMo-1B-sft	ShowMakerTAT	2025-05-24T06:08:31Z	0	0	null	[ "safetensors", "olmo", "license:apache-2.0", "region:us" ]	null	2025-05-23T02:33:46Z	--- license: apache-2.0 ---
feilongfl/Qwen3News	feilongfl	2025-05-24T06:06:28Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-23T22:56:56Z	--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep10_66	MinaMila	2025-05-24T06:05:31Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T06:05:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Jobz-Hunting-Pakistan-Viral-Video/Jobz-Hunting	Jobz-Hunting-Pakistan-Viral-Video	2025-05-24T06:01:55Z	0	0	null	[ "region:us" ]	null	2025-05-24T05:59:45Z	[![My Image](https://github.com/user-attachments/assets/1b861689-da30-4c3d-ad0f-5094026cc9f2)](https://tinyurl.com/ybfu84ub) . .
mradermacher/ad-gpt2-finetuned-dch1-GGUF	mradermacher	2025-05-24T06:00:48Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:refringence/ad-gpt2-finetuned-dch1", "base_model:quantized:refringence/ad-gpt2-finetuned-dch1", "endpoints_compatible", "region:us" ]	null	2025-05-23T18:47:24Z	--- base_model: refringence/ad-gpt2-finetuned-dch1 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/refringence/ad-gpt2-finetuned-dch1 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/ad-gpt2-finetuned-dch1-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/ad-gpt2-finetuned-dch1-GGUF/resolve/main/ad-gpt2-finetuned-dch1.Q2_K.gguf) \| Q2_K \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ad-gpt2-finetuned-dch1-GGUF/resolve/main/ad-gpt2-finetuned-dch1.Q3_K_S.gguf) \| Q3_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ad-gpt2-finetuned-dch1-GGUF/resolve/main/ad-gpt2-finetuned-dch1.Q3_K_M.gguf) \| Q3_K_M \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/ad-gpt2-finetuned-dch1-GGUF/resolve/main/ad-gpt2-finetuned-dch1.IQ4_XS.gguf) \| IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ad-gpt2-finetuned-dch1-GGUF/resolve/main/ad-gpt2-finetuned-dch1.Q4_K_S.gguf) \| Q4_K_S \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ad-gpt2-finetuned-dch1-GGUF/resolve/main/ad-gpt2-finetuned-dch1.Q3_K_L.gguf) \| Q3_K_L \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ad-gpt2-finetuned-dch1-GGUF/resolve/main/ad-gpt2-finetuned-dch1.Q4_K_M.gguf) \| Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ad-gpt2-finetuned-dch1-GGUF/resolve/main/ad-gpt2-finetuned-dch1.Q5_K_S.gguf) \| Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ad-gpt2-finetuned-dch1-GGUF/resolve/main/ad-gpt2-finetuned-dch1.Q5_K_M.gguf) \| Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ad-gpt2-finetuned-dch1-GGUF/resolve/main/ad-gpt2-finetuned-dch1.Q6_K.gguf) \| Q6_K \| 0.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/ad-gpt2-finetuned-dch1-GGUF/resolve/main/ad-gpt2-finetuned-dch1.Q8_0.gguf) \| Q8_0 \| 0.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/ad-gpt2-finetuned-dch1-GGUF/resolve/main/ad-gpt2-finetuned-dch1.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 -->
mradermacher/GPT-Greentext-355m-i1-GGUF	mradermacher	2025-05-24T06:00:46Z	0	0	transformers	[ "transformers", "gguf", "fun", "greentext", "en", "dataset:DarwinAnim8or/greentext", "base_model:DarwinAnim8or/GPT-Greentext-355m", "base_model:quantized:DarwinAnim8or/GPT-Greentext-355m", "license:mit", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-05-24T05:43:49Z	--- base_model: DarwinAnim8or/GPT-Greentext-355m datasets: - DarwinAnim8or/greentext language: - en library_name: transformers license: mit quantized_by: mradermacher tags: - fun - greentext --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/DarwinAnim8or/GPT-Greentext-355m <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/GPT-Greentext-355m-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/GPT-Greentext-355m-i1-GGUF/resolve/main/GPT-Greentext-355m.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.2 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/GPT-Greentext-355m-i1-GGUF/resolve/main/GPT-Greentext-355m.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.2 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/GPT-Greentext-355m-i1-GGUF/resolve/main/GPT-Greentext-355m.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/GPT-Greentext-355m-i1-GGUF/resolve/main/GPT-Greentext-355m.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/GPT-Greentext-355m-i1-GGUF/resolve/main/GPT-Greentext-355m.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/GPT-Greentext-355m-i1-GGUF/resolve/main/GPT-Greentext-355m.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/GPT-Greentext-355m-i1-GGUF/resolve/main/GPT-Greentext-355m.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.3 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/GPT-Greentext-355m-i1-GGUF/resolve/main/GPT-Greentext-355m.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/GPT-Greentext-355m-i1-GGUF/resolve/main/GPT-Greentext-355m.i1-Q2_K.gguf) \| i1-Q2_K \| 0.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/GPT-Greentext-355m-i1-GGUF/resolve/main/GPT-Greentext-355m.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/GPT-Greentext-355m-i1-GGUF/resolve/main/GPT-Greentext-355m.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/GPT-Greentext-355m-i1-GGUF/resolve/main/GPT-Greentext-355m.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.3 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/GPT-Greentext-355m-i1-GGUF/resolve/main/GPT-Greentext-355m.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/GPT-Greentext-355m-i1-GGUF/resolve/main/GPT-Greentext-355m.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.3 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/GPT-Greentext-355m-i1-GGUF/resolve/main/GPT-Greentext-355m.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/GPT-Greentext-355m-i1-GGUF/resolve/main/GPT-Greentext-355m.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 0.3 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/GPT-Greentext-355m-i1-GGUF/resolve/main/GPT-Greentext-355m.i1-Q4_0.gguf) \| i1-Q4_0 \| 0.3 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/GPT-Greentext-355m-i1-GGUF/resolve/main/GPT-Greentext-355m.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 0.3 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/GPT-Greentext-355m-i1-GGUF/resolve/main/GPT-Greentext-355m.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 0.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/GPT-Greentext-355m-i1-GGUF/resolve/main/GPT-Greentext-355m.i1-Q4_1.gguf) \| i1-Q4_1 \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/GPT-Greentext-355m-i1-GGUF/resolve/main/GPT-Greentext-355m.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 0.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/GPT-Greentext-355m-i1-GGUF/resolve/main/GPT-Greentext-355m.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/GPT-Greentext-355m-i1-GGUF/resolve/main/GPT-Greentext-355m.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/GPT-Greentext-355m-i1-GGUF/resolve/main/GPT-Greentext-355m.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 -->
MinaMila/gemma2_2b_unlearned_gu_LoRa_ACSEmployment_2_ep8_22	MinaMila	2025-05-24T05:59:54Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T05:59:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
MinaMila/llama_instbase_unlearned_ug_e-6_1.0_0.25_0.5_ep3_LoRa_ACSEmployment_2_cfda_ep9_22	MinaMila	2025-05-24T05:55:54Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T05:55:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
vinuajeesh/bakllava	vinuajeesh	2025-05-24T05:55:51Z	0	0	null	[ "safetensors", "llava", "generated_from_trainer", "base_model:llava-hf/bakLlava-v1-hf", "base_model:finetune:llava-hf/bakLlava-v1-hf", "region:us" ]	null	2025-05-24T05:54:33Z	--- base_model: llava-hf/bakLlava-v1-hf tags: - generated_from_trainer model-index: - name: llava_bakllava_7b_v2_8192 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. --> # llava_bakllava_7b_v2_8192 This model is a fine-tuned version of [llava-hf/bakLlava-v1-hf](https://huggingface.co/llava-hf/bakLlava-v1-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.39.2 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep7_66	MinaMila	2025-05-24T05:55:08Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T05:55:03Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
mci29/sn29_s2m7_cpou	mci29	2025-05-24T05:54:48Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T05:50:25Z	--- 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. 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]
watch-katrina-lim-kiffy-full-origin/NEWS18-Video-Full-Videos-smriti-jain-all-videos-link-instagram-id	watch-katrina-lim-kiffy-full-origin	2025-05-24T05:54:48Z	0	0	null	[ "region:us" ]	null	2025-05-24T05:54:12Z	Watch 🟢 ➤ ➤ ➤ <a href="https://witvidz.com/originalviralvideo"> 🌐 Click Here To link (Full Viral Video Link) 🔴 ➤►DOWNLOAD👉👉🟢 ➤ Watch 🟢 ➤ ➤ ➤ <a href="https://witvidz.com/originalviralvideo"> 🌐 Click Here To link (Full Viral Video Link) 🔴 ➤►DOWNLOAD👉👉🟢 ➤
izzcw/crafting_sft_fail_new_mem	izzcw	2025-05-24T05:54:16Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "license:llama3.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-23T01:24:33Z	--- library_name: transformers license: llama3.1 base_model: meta-llama/Llama-3.1-8B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: crafting_sft_fail_new_mem 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. --> # crafting_sft_fail_new_mem This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the identity and the crafting_sft_fail_new_mem datasets. It achieves the following results on the evaluation set: - Loss: 0.3208 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - total_eval_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.5341 \| 0.0323 \| 50 \| 0.4793 \| \| 0.5261 \| 0.0646 \| 100 \| 0.4858 \| \| 0.5103 \| 0.0969 \| 150 \| 0.4932 \| \| 0.5236 \| 0.1291 \| 200 \| 0.4820 \| \| 0.524 \| 0.1614 \| 250 \| 0.4623 \| \| 0.5107 \| 0.1937 \| 300 \| 0.4454 \| \| 0.4723 \| 0.2260 \| 350 \| 0.4380 \| \| 0.4771 \| 0.2583 \| 400 \| 0.4323 \| \| 0.4835 \| 0.2906 \| 450 \| 0.4249 \| \| 0.455 \| 0.3229 \| 500 \| 0.4205 \| \| 0.4724 \| 0.3552 \| 550 \| 0.4145 \| \| 0.4579 \| 0.3874 \| 600 \| 0.4005 \| \| 0.4691 \| 0.4197 \| 650 \| 0.4049 \| \| 0.4405 \| 0.4520 \| 700 \| 0.3883 \| \| 0.4443 \| 0.4843 \| 750 \| 0.3845 \| \| 0.4348 \| 0.5166 \| 800 \| 0.3788 \| \| 0.4153 \| 0.5489 \| 850 \| 0.3675 \| \| 0.4123 \| 0.5812 \| 900 \| 0.3647 \| \| 0.3943 \| 0.6134 \| 950 \| 0.3590 \| \| 0.4059 \| 0.6457 \| 1000 \| 0.3495 \| \| 0.3778 \| 0.6780 \| 1050 \| 0.3437 \| \| 0.3734 \| 0.7103 \| 1100 \| 0.3430 \| \| 0.3762 \| 0.7426 \| 1150 \| 0.3367 \| \| 0.3576 \| 0.7749 \| 1200 \| 0.3327 \| \| 0.3794 \| 0.8072 \| 1250 \| 0.3295 \| \| 0.3695 \| 0.8395 \| 1300 \| 0.3265 \| \| 0.3571 \| 0.8717 \| 1350 \| 0.3233 \| \| 0.3655 \| 0.9040 \| 1400 \| 0.3225 \| \| 0.3801 \| 0.9363 \| 1450 \| 0.3211 \| \| 0.3704 \| 0.9686 \| 1500 \| 0.3209 \| ### Framework versions - Transformers 4.49.0 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
mradermacher/nba_pbp_distilgpt2-GGUF	mradermacher	2025-05-24T05:53:57Z	0	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "en", "base_model:arvkevi/nba_pbp_distilgpt2", "base_model:quantized:arvkevi/nba_pbp_distilgpt2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-23T18:43:39Z	--- base_model: arvkevi/nba_pbp_distilgpt2 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - generated_from_trainer --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/arvkevi/nba_pbp_distilgpt2 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/nba_pbp_distilgpt2-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/nba_pbp_distilgpt2-GGUF/resolve/main/nba_pbp_distilgpt2.Q2_K.gguf) \| Q2_K \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/nba_pbp_distilgpt2-GGUF/resolve/main/nba_pbp_distilgpt2.Q3_K_S.gguf) \| Q3_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/nba_pbp_distilgpt2-GGUF/resolve/main/nba_pbp_distilgpt2.Q3_K_M.gguf) \| Q3_K_M \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/nba_pbp_distilgpt2-GGUF/resolve/main/nba_pbp_distilgpt2.IQ4_XS.gguf) \| IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/nba_pbp_distilgpt2-GGUF/resolve/main/nba_pbp_distilgpt2.Q4_K_S.gguf) \| Q4_K_S \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/nba_pbp_distilgpt2-GGUF/resolve/main/nba_pbp_distilgpt2.Q3_K_L.gguf) \| Q3_K_L \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/nba_pbp_distilgpt2-GGUF/resolve/main/nba_pbp_distilgpt2.Q4_K_M.gguf) \| Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/nba_pbp_distilgpt2-GGUF/resolve/main/nba_pbp_distilgpt2.Q5_K_S.gguf) \| Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/nba_pbp_distilgpt2-GGUF/resolve/main/nba_pbp_distilgpt2.Q5_K_M.gguf) \| Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/nba_pbp_distilgpt2-GGUF/resolve/main/nba_pbp_distilgpt2.Q6_K.gguf) \| Q6_K \| 0.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/nba_pbp_distilgpt2-GGUF/resolve/main/nba_pbp_distilgpt2.Q8_0.gguf) \| Q8_0 \| 0.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/nba_pbp_distilgpt2-GGUF/resolve/main/nba_pbp_distilgpt2.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/DA-ctrl-bot-i1-GGUF	mradermacher	2025-05-24T05:53:43Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:imumtozee/DA-ctrl-bot", "base_model:quantized:imumtozee/DA-ctrl-bot", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-05-24T05:43:46Z	--- base_model: imumtozee/DA-ctrl-bot 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/imumtozee/DA-ctrl-bot <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/DA-ctrl-bot-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/DA-ctrl-bot-i1-GGUF/resolve/main/DA-ctrl-bot.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-i1-GGUF/resolve/main/DA-ctrl-bot.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.2 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-i1-GGUF/resolve/main/DA-ctrl-bot.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-i1-GGUF/resolve/main/DA-ctrl-bot.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-i1-GGUF/resolve/main/DA-ctrl-bot.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-i1-GGUF/resolve/main/DA-ctrl-bot.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-i1-GGUF/resolve/main/DA-ctrl-bot.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-i1-GGUF/resolve/main/DA-ctrl-bot.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.2 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-i1-GGUF/resolve/main/DA-ctrl-bot.i1-Q2_K.gguf) \| i1-Q2_K \| 0.2 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-i1-GGUF/resolve/main/DA-ctrl-bot.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-i1-GGUF/resolve/main/DA-ctrl-bot.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.2 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-i1-GGUF/resolve/main/DA-ctrl-bot.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.2 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-i1-GGUF/resolve/main/DA-ctrl-bot.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-i1-GGUF/resolve/main/DA-ctrl-bot.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-i1-GGUF/resolve/main/DA-ctrl-bot.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-i1-GGUF/resolve/main/DA-ctrl-bot.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 0.2 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-i1-GGUF/resolve/main/DA-ctrl-bot.i1-Q4_0.gguf) \| i1-Q4_0 \| 0.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-i1-GGUF/resolve/main/DA-ctrl-bot.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 0.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-i1-GGUF/resolve/main/DA-ctrl-bot.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 0.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-i1-GGUF/resolve/main/DA-ctrl-bot.i1-Q4_1.gguf) \| i1-Q4_1 \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-i1-GGUF/resolve/main/DA-ctrl-bot.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-i1-GGUF/resolve/main/DA-ctrl-bot.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-i1-GGUF/resolve/main/DA-ctrl-bot.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-i1-GGUF/resolve/main/DA-ctrl-bot.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 -->
kairun/Qwen3-vLLM	kairun	2025-05-24T05:53:14Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-24T05:50:22Z	--- base_model: unsloth/qwen3-14b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: kairun - License: apache-2.0 - Finetuned from model : unsloth/qwen3-14b-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mradermacher/DA-ctrl-bot-GGUF	mradermacher	2025-05-24T05:51:05Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:imumtozee/DA-ctrl-bot", "base_model:quantized:imumtozee/DA-ctrl-bot", "endpoints_compatible", "region:us" ]	null	2025-05-23T18:40:08Z	--- base_model: imumtozee/DA-ctrl-bot 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/imumtozee/DA-ctrl-bot <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/DA-ctrl-bot-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/DA-ctrl-bot-GGUF/resolve/main/DA-ctrl-bot.Q2_K.gguf) \| Q2_K \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-GGUF/resolve/main/DA-ctrl-bot.Q3_K_S.gguf) \| Q3_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-GGUF/resolve/main/DA-ctrl-bot.Q3_K_M.gguf) \| Q3_K_M \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-GGUF/resolve/main/DA-ctrl-bot.IQ4_XS.gguf) \| IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-GGUF/resolve/main/DA-ctrl-bot.Q4_K_S.gguf) \| Q4_K_S \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-GGUF/resolve/main/DA-ctrl-bot.Q3_K_L.gguf) \| Q3_K_L \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-GGUF/resolve/main/DA-ctrl-bot.Q4_K_M.gguf) \| Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-GGUF/resolve/main/DA-ctrl-bot.Q5_K_S.gguf) \| Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-GGUF/resolve/main/DA-ctrl-bot.Q5_K_M.gguf) \| Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-GGUF/resolve/main/DA-ctrl-bot.Q6_K.gguf) \| Q6_K \| 0.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-GGUF/resolve/main/DA-ctrl-bot.Q8_0.gguf) \| Q8_0 \| 0.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/DA-ctrl-bot-GGUF/resolve/main/DA-ctrl-bot.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 -->
mradermacher/PELM-JointGPT-i1-GGUF	mradermacher	2025-05-24T05:51:05Z	0	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "en", "base_model:GItaf/PELM-JointGPT", "base_model:quantized:GItaf/PELM-JointGPT", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-05-24T05:40:16Z	--- base_model: GItaf/PELM-JointGPT language: - en library_name: transformers quantized_by: mradermacher tags: - generated_from_trainer --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/GItaf/PELM-JointGPT <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/PELM-JointGPT-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/PELM-JointGPT-i1-GGUF/resolve/main/PELM-JointGPT.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-i1-GGUF/resolve/main/PELM-JointGPT.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.2 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-i1-GGUF/resolve/main/PELM-JointGPT.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-i1-GGUF/resolve/main/PELM-JointGPT.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-i1-GGUF/resolve/main/PELM-JointGPT.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-i1-GGUF/resolve/main/PELM-JointGPT.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-i1-GGUF/resolve/main/PELM-JointGPT.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-i1-GGUF/resolve/main/PELM-JointGPT.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.2 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-i1-GGUF/resolve/main/PELM-JointGPT.i1-Q2_K.gguf) \| i1-Q2_K \| 0.2 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-i1-GGUF/resolve/main/PELM-JointGPT.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-i1-GGUF/resolve/main/PELM-JointGPT.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.2 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-i1-GGUF/resolve/main/PELM-JointGPT.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.2 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-i1-GGUF/resolve/main/PELM-JointGPT.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-i1-GGUF/resolve/main/PELM-JointGPT.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-i1-GGUF/resolve/main/PELM-JointGPT.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-i1-GGUF/resolve/main/PELM-JointGPT.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 0.2 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-i1-GGUF/resolve/main/PELM-JointGPT.i1-Q4_0.gguf) \| i1-Q4_0 \| 0.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-i1-GGUF/resolve/main/PELM-JointGPT.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 0.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-i1-GGUF/resolve/main/PELM-JointGPT.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 0.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-i1-GGUF/resolve/main/PELM-JointGPT.i1-Q4_1.gguf) \| i1-Q4_1 \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-i1-GGUF/resolve/main/PELM-JointGPT.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-i1-GGUF/resolve/main/PELM-JointGPT.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-i1-GGUF/resolve/main/PELM-JointGPT.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-i1-GGUF/resolve/main/PELM-JointGPT.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 -->
mradermacher/ScriptForge-small-i1-GGUF	mradermacher	2025-05-24T05:51:04Z	0	0	transformers	[ "transformers", "gguf", "text-generation", "en", "base_model:SRDdev/ScriptForge-small", "base_model:quantized:SRDdev/ScriptForge-small", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix" ]	text-generation	2025-05-24T05:40:24Z	--- base_model: SRDdev/ScriptForge-small language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/SRDdev/ScriptForge-small <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/ScriptForge-small-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/ScriptForge-small-i1-GGUF/resolve/main/ScriptForge-small.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/ScriptForge-small-i1-GGUF/resolve/main/ScriptForge-small.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.2 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/ScriptForge-small-i1-GGUF/resolve/main/ScriptForge-small.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ScriptForge-small-i1-GGUF/resolve/main/ScriptForge-small.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ScriptForge-small-i1-GGUF/resolve/main/ScriptForge-small.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ScriptForge-small-i1-GGUF/resolve/main/ScriptForge-small.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ScriptForge-small-i1-GGUF/resolve/main/ScriptForge-small.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/ScriptForge-small-i1-GGUF/resolve/main/ScriptForge-small.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.2 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/ScriptForge-small-i1-GGUF/resolve/main/ScriptForge-small.i1-Q2_K.gguf) \| i1-Q2_K \| 0.2 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/ScriptForge-small-i1-GGUF/resolve/main/ScriptForge-small.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ScriptForge-small-i1-GGUF/resolve/main/ScriptForge-small.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.2 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/ScriptForge-small-i1-GGUF/resolve/main/ScriptForge-small.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.2 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/ScriptForge-small-i1-GGUF/resolve/main/ScriptForge-small.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ScriptForge-small-i1-GGUF/resolve/main/ScriptForge-small.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/ScriptForge-small-i1-GGUF/resolve/main/ScriptForge-small.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ScriptForge-small-i1-GGUF/resolve/main/ScriptForge-small.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 0.2 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/ScriptForge-small-i1-GGUF/resolve/main/ScriptForge-small.i1-Q4_0.gguf) \| i1-Q4_0 \| 0.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/ScriptForge-small-i1-GGUF/resolve/main/ScriptForge-small.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 0.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/ScriptForge-small-i1-GGUF/resolve/main/ScriptForge-small.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 0.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/ScriptForge-small-i1-GGUF/resolve/main/ScriptForge-small.i1-Q4_1.gguf) \| i1-Q4_1 \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ScriptForge-small-i1-GGUF/resolve/main/ScriptForge-small.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ScriptForge-small-i1-GGUF/resolve/main/ScriptForge-small.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ScriptForge-small-i1-GGUF/resolve/main/ScriptForge-small.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ScriptForge-small-i1-GGUF/resolve/main/ScriptForge-small.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 -->
Baselhany/Distilation_Whisper_base_CKP_10k	Baselhany	2025-05-24T05:48:39Z	3	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ar", "base_model:openai/whisper-base", "base_model:finetune:openai/whisper-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-22T18:17:54Z	--- library_name: transformers language: - ar license: apache-2.0 base_model: openai/whisper-base tags: - generated_from_trainer metrics: - wer model-index: - name: Whisper base AR - BA results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper base AR - BA This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the quran-ayat-speech-to-text dataset. It achieves the following results on the evaluation set: - Loss: 0.1070 - Wer: 0.2297 ## 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: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:-----:\|:---------------:\|:------:\| \| 78.8449 \| 1.0 \| 313 \| 0.1892 \| 0.7483 \| \| 23.7046 \| 2.0 \| 626 \| 0.1465 \| 0.4188 \| \| 13.1378 \| 3.0 \| 939 \| 0.1347 \| 0.3632 \| \| 8.2072 \| 4.0 \| 1252 \| 0.1312 \| 0.3285 \| \| 5.8166 \| 5.0 \| 1565 \| 0.1316 \| 0.2937 \| \| 4.5461 \| 6.0 \| 1878 \| 0.1339 \| 0.2916 \| \| 3.8785 \| 7.0 \| 2191 \| 0.1276 \| 0.2838 \| \| 3.1975 \| 8.0 \| 2504 \| 0.1253 \| 0.2762 \| \| 2.8784 \| 9.0 \| 2817 \| 0.1240 \| 0.2881 \| \| 2.6303 \| 10.0 \| 3130 \| 0.1238 \| 0.2719 \| \| 2.481 \| 11.0 \| 3443 \| 0.1225 \| 0.2670 \| \| 2.2994 \| 12.0 \| 3756 \| 0.1221 \| 0.2641 \| \| 2.0863 \| 13.0 \| 4069 \| 0.1214 \| 0.2672 \| \| 2.0235 \| 14.0 \| 4382 \| 0.1213 \| 0.2638 \| \| 2.015 \| 14.9536 \| 4680 \| 0.1213 \| 0.2626 \| \| 7.0386 \| 13.0 \| 4875 \| 0.1209 \| 0.2760 \| \| 5.2638 \| 14.0 \| 5250 \| 0.1169 \| 0.2538 \| \| 3.8581 \| 15.0 \| 5625 \| 0.1180 \| 0.2374 \| \| 3.4661 \| 16.0 \| 6000 \| 0.1176 \| 0.2408 \| \| 2.8903 \| 17.0 \| 6375 \| 0.1167 \| 0.2359 \| \| 2.6081 \| 18.0 \| 6750 \| 0.1172 \| 0.2358 \| \| 2.6719 \| 19.0 \| 7125 \| 0.1165 \| 0.2401 \| \| 2.4235 \| 20.0 \| 7500 \| 0.1160 \| 0.2430 \| \| 4.9497 \| 21.0 \| 7875 \| 0.1133 \| 0.2361 \| \| 3.6345 \| 22.0 \| 8250 \| 0.1136 \| 0.2274 \| \| 3.092 \| 23.0 \| 8625 \| 0.1123 \| 0.2305 \| \| 2.606 \| 24.0 \| 9000 \| 0.1098 \| 0.2283 \| \| 2.4858 \| 25.0 \| 9375 \| 0.1103 \| 0.2253 \| \| 2.1898 \| 26.0 \| 9750 \| 0.1109 \| 0.2327 \| \| 2.1861 \| 27.0 \| 10125 \| 0.1088 \| 0.2311 \| \| 1.8994 \| 28.0 \| 10500 \| 0.1084 \| 0.2261 \| \| 1.8208 \| 29.0 \| 10875 \| 0.1078 \| 0.2266 \| \| 1.706 \| 30.0 \| 11250 \| 0.1077 \| 0.2287 \| \| 1.5895 \| 31.0 \| 11625 \| 0.1067 \| 0.2233 \| \| 1.5086 \| 32.0 \| 12000 \| 0.1068 \| 0.2299 \| \| 1.4744 \| 33.0 \| 12375 \| 0.1065 \| 0.2268 \| \| 1.4184 \| 34.0 \| 12750 \| 0.1056 \| 0.2266 \| \| 1.4134 \| 35.0 \| 13125 \| 0.1064 \| 0.2331 \| \| 1.3246 \| 36.0 \| 13500 \| 0.1054 \| 0.2263 \| \| 1.3368 \| 37.0 \| 13875 \| 0.1057 \| 0.2317 \| \| 1.3084 \| 38.0 \| 14250 \| 0.1053 \| 0.2412 \| \| 1.302 \| 39.0 \| 14625 \| 0.1054 \| 0.2309 \| \| 1.2152 \| 40.0 \| 15000 \| 0.1053 \| 0.2297 \| \| 3.6933 \| 37.9994 \| 15314 \| 0.1044 \| 0.2122 \| \| 2.9938 \| 39.0 \| 15718 \| 0.1051 \| 0.2193 \| \| 2.5582 \| 40.0 \| 16122 \| 0.1041 \| 0.2202 \| \| 2.1949 \| 41.0 \| 16526 \| 0.1032 \| 0.2137 \| \| 2.1428 \| 42.0 \| 16930 \| 0.1045 \| 0.2146 \| \| 2.0052 \| 43.0 \| 17334 \| 0.1027 \| 0.2146 \| \| 1.7204 \| 44.0 \| 17738 \| 0.1031 \| 0.2121 \| \| 1.7391 \| 45.0 \| 18142 \| 0.1026 \| 0.2125 \| \| 1.6544 \| 46.0 \| 18546 \| 0.1028 \| 0.2140 \| \| 1.6764 \| 47.0 \| 18950 \| 0.1033 \| 0.2121 \| \| 1.535 \| 48.0 \| 19354 \| 0.1028 \| 0.2122 \| \| 1.5344 \| 49.0 \| 19758 \| 0.1025 \| 0.2163 \| \| 1.5171 \| 49.9721 \| 20150 \| 0.1025 \| 0.2121 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep5_66	MinaMila	2025-05-24T05:48:16Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T05:48:12Z	--- 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]
chloebrandon/results	chloebrandon	2025-05-24T05:44:33Z	0	0	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "generated_from_trainer", "base_model:google/mt5-small", "base_model:finetune:google/mt5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-24T05:43:59Z	--- library_name: transformers license: apache-2.0 base_model: google/mt5-small tags: - generated_from_trainer model-index: - name: results 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. --> # results This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
LexcentraAI/lex-cross-encoder-mbert-10neg	LexcentraAI	2025-05-24T05:44:22Z	0	0	null	[ "safetensors", "bert", "generated_from_trainer", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "region:us" ]	null	2025-05-24T02:45:26Z	--- license: apache-2.0 base_model: google-bert/bert-base-multilingual-cased tags: - generated_from_trainer metrics: - precision - recall model-index: - name: lex-cross-encoder-mbert-10neg 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. --> # lex-cross-encoder-mbert-10neg This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4360 - Precision: 0.6020 - Recall: 0.8593 - F2: 0.7917 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 128 - total_eval_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F2 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\| \| 0.4572 \| 1.0 \| 2317 \| 0.4705 \| 0.4735 \| 0.8620 \| 0.7405 \| \| 0.4283 \| 2.0 \| 4634 \| 0.4515 \| 0.4774 \| 0.9124 \| 0.7718 \| \| 0.4115 \| 3.0 \| 6951 \| 0.4485 \| 0.4796 \| 0.9201 \| 0.7773 \| \| 0.4021 \| 4.0 \| 9268 \| 0.4387 \| 0.5217 \| 0.9068 \| 0.7902 \| \| 0.3918 \| 5.0 \| 11585 \| 0.4466 \| 0.6111 \| 0.8242 \| 0.7705 \| \| 0.3879 \| 6.0 \| 13902 \| 0.4337 \| 0.5783 \| 0.8767 \| 0.7947 \| \| 0.383 \| 7.0 \| 16219 \| 0.4336 \| 0.5633 \| 0.8907 \| 0.7980 \| \| 0.3781 \| 8.0 \| 18536 \| 0.4354 \| 0.5929 \| 0.8660 \| 0.7930 \| \| 0.3767 \| 9.0 \| 20853 \| 0.4353 \| 0.5980 \| 0.8636 \| 0.7931 \| \| 0.3712 \| 10.0 \| 23170 \| 0.4360 \| 0.6020 \| 0.8593 \| 0.7917 \| ### Framework versions - Transformers 4.39.1 - Pytorch 2.5.1+cu121 - Datasets 3.6.0 - Tokenizers 0.15.2
mradermacher/DialoGPT-medium-PowPowGaming-i1-GGUF	mradermacher	2025-05-24T05:43:48Z	0	0	transformers	[ "transformers", "gguf", "conversational", "en", "base_model:kennethhendricks/DialoGPT-medium-PowPowGaming", "base_model:quantized:kennethhendricks/DialoGPT-medium-PowPowGaming", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-05-24T05:34:09Z	--- base_model: kennethhendricks/DialoGPT-medium-PowPowGaming language: - en library_name: transformers quantized_by: mradermacher tags: - conversational --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/kennethhendricks/DialoGPT-medium-PowPowGaming <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/DialoGPT-medium-PowPowGaming-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-medium-PowPowGaming-i1-GGUF/resolve/main/DialoGPT-medium-PowPowGaming.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.2 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-PowPowGaming-i1-GGUF/resolve/main/DialoGPT-medium-PowPowGaming.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.2 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-PowPowGaming-i1-GGUF/resolve/main/DialoGPT-medium-PowPowGaming.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-PowPowGaming-i1-GGUF/resolve/main/DialoGPT-medium-PowPowGaming.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-PowPowGaming-i1-GGUF/resolve/main/DialoGPT-medium-PowPowGaming.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-PowPowGaming-i1-GGUF/resolve/main/DialoGPT-medium-PowPowGaming.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-PowPowGaming-i1-GGUF/resolve/main/DialoGPT-medium-PowPowGaming.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.3 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-PowPowGaming-i1-GGUF/resolve/main/DialoGPT-medium-PowPowGaming.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-PowPowGaming-i1-GGUF/resolve/main/DialoGPT-medium-PowPowGaming.i1-Q2_K.gguf) \| i1-Q2_K \| 0.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-PowPowGaming-i1-GGUF/resolve/main/DialoGPT-medium-PowPowGaming.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-PowPowGaming-i1-GGUF/resolve/main/DialoGPT-medium-PowPowGaming.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-PowPowGaming-i1-GGUF/resolve/main/DialoGPT-medium-PowPowGaming.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.3 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-PowPowGaming-i1-GGUF/resolve/main/DialoGPT-medium-PowPowGaming.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-PowPowGaming-i1-GGUF/resolve/main/DialoGPT-medium-PowPowGaming.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.3 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-PowPowGaming-i1-GGUF/resolve/main/DialoGPT-medium-PowPowGaming.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-PowPowGaming-i1-GGUF/resolve/main/DialoGPT-medium-PowPowGaming.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 0.3 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-PowPowGaming-i1-GGUF/resolve/main/DialoGPT-medium-PowPowGaming.i1-Q4_0.gguf) \| i1-Q4_0 \| 0.3 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-PowPowGaming-i1-GGUF/resolve/main/DialoGPT-medium-PowPowGaming.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 0.3 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-PowPowGaming-i1-GGUF/resolve/main/DialoGPT-medium-PowPowGaming.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 0.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-PowPowGaming-i1-GGUF/resolve/main/DialoGPT-medium-PowPowGaming.i1-Q4_1.gguf) \| i1-Q4_1 \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-PowPowGaming-i1-GGUF/resolve/main/DialoGPT-medium-PowPowGaming.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 0.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-PowPowGaming-i1-GGUF/resolve/main/DialoGPT-medium-PowPowGaming.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-PowPowGaming-i1-GGUF/resolve/main/DialoGPT-medium-PowPowGaming.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-PowPowGaming-i1-GGUF/resolve/main/DialoGPT-medium-PowPowGaming.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/PELM-JointGPT-GGUF	mradermacher	2025-05-24T05:43:48Z	0	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "en", "base_model:GItaf/PELM-JointGPT", "base_model:quantized:GItaf/PELM-JointGPT", "endpoints_compatible", "region:us" ]	null	2025-05-23T18:36:27Z	--- base_model: GItaf/PELM-JointGPT language: - en library_name: transformers quantized_by: mradermacher tags: - generated_from_trainer --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/GItaf/PELM-JointGPT <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/PELM-JointGPT-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/PELM-JointGPT-GGUF/resolve/main/PELM-JointGPT.Q2_K.gguf) \| Q2_K \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-GGUF/resolve/main/PELM-JointGPT.Q3_K_S.gguf) \| Q3_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-GGUF/resolve/main/PELM-JointGPT.Q3_K_M.gguf) \| Q3_K_M \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-GGUF/resolve/main/PELM-JointGPT.IQ4_XS.gguf) \| IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-GGUF/resolve/main/PELM-JointGPT.Q4_K_S.gguf) \| Q4_K_S \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-GGUF/resolve/main/PELM-JointGPT.Q3_K_L.gguf) \| Q3_K_L \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-GGUF/resolve/main/PELM-JointGPT.Q4_K_M.gguf) \| Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-GGUF/resolve/main/PELM-JointGPT.Q5_K_S.gguf) \| Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-GGUF/resolve/main/PELM-JointGPT.Q5_K_M.gguf) \| Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-GGUF/resolve/main/PELM-JointGPT.Q6_K.gguf) \| Q6_K \| 0.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-GGUF/resolve/main/PELM-JointGPT.Q8_0.gguf) \| Q8_0 \| 0.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/PELM-JointGPT-GGUF/resolve/main/PELM-JointGPT.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 -->
watch-katrina-lim-kiffy-full-origin/Nxtwp-Katrina-Lim-Viral-Video-Katrina-Lim-Kiffy-Video-lim-katrina-viral-video-original	watch-katrina-lim-kiffy-full-origin	2025-05-24T05:43:18Z	0	0	null	[ "region:us" ]	null	2025-05-24T05:41:54Z	Watch 🟢 ➤ ➤ ➤ <a href="https://witvidz.com/originalviralvideo"> 🌐 Click Here To link (Full Viral Video Link) 🔴 ➤►DOWNLOAD👉👉🟢 ➤ Watch 🟢 ➤ ➤ ➤ <a href="https://witvidz.com/originalviralvideo"> 🌐 Click Here To link (Full Viral Video Link) 🔴 ➤►DOWNLOAD👉👉🟢 ➤ ![68747470733a2f2f692e696d6775722e636f6d2f644a486b345a712e676966.gif](https://cdn-uploads.huggingface.co/production/uploads/6831511e46229e4f23a4ff73/k3vWSIYbkzVk2RKn4-spJ.gif)
DanHauri/lora_model	DanHauri	2025-05-24T05:42:39Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-22T19:47:06Z	--- base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: DanHauri - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-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)
FormlessAI/2fd46615-52d2-476a-ae64-afa1d97f0bae	FormlessAI	2025-05-24T05:41:58Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "dpo", "arxiv:2305.18290", "base_model:unsloth/Qwen2.5-14B", "base_model:finetune:unsloth/Qwen2.5-14B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T05:40:50Z	--- base_model: unsloth/Qwen2.5-14B library_name: transformers model_name: 2fd46615-52d2-476a-ae64-afa1d97f0bae tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for 2fd46615-52d2-476a-ae64-afa1d97f0bae This model is a fine-tuned version of [unsloth/Qwen2.5-14B](https://huggingface.co/unsloth/Qwen2.5-14B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="FormlessAI/2fd46615-52d2-476a-ae64-afa1d97f0bae", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/phoenix-formless/Gradients/runs/t1lvc7db) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.17.0 - Transformers: 4.52.3 - Pytorch: 2.7.0+cu128 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
VIDEOs-18-Katrina-Lim-Viral-Kiffy/NEW.VIDEOs.LINK.Katrina.Lim.Viral.Video.Leaks.Official.tv	VIDEOs-18-Katrina-Lim-Viral-Kiffy	2025-05-24T05:41:18Z	0	0	null	[ "region:us" ]	null	2025-05-24T05:39:09Z	[![image/gif](https://cdn-uploads.huggingface.co/production/uploads/682ab5947cb97ecc4a471bbc/yvJ4QZ-1RtUvejXXCrIUk.gif)](https://tinyurl.com/Videos-Pinoy)
MaoyueOUO/Cosmos-Reason1-7B-GGUF	MaoyueOUO	2025-05-24T05:39:04Z	0	0	null	[ "gguf", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-24T04:55:43Z	--- license: other license_name: nvidia-open-model-license license_link: >- https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/ ---
mradermacher/homer-bot-GGUF	mradermacher	2025-05-24T05:37:02Z	0	0	transformers	[ "transformers", "gguf", "conversational", "en", "base_model:jesseD/homer-bot", "base_model:quantized:jesseD/homer-bot", "endpoints_compatible", "region:us" ]	null	2025-05-23T18:31:38Z	--- base_model: jesseD/homer-bot language: - en library_name: transformers quantized_by: mradermacher tags: - conversational --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/jesseD/homer-bot <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/homer-bot-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/homer-bot-GGUF/resolve/main/homer-bot.Q2_K.gguf) \| Q2_K \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/homer-bot-GGUF/resolve/main/homer-bot.Q3_K_S.gguf) \| Q3_K_S \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/homer-bot-GGUF/resolve/main/homer-bot.Q3_K_M.gguf) \| Q3_K_M \| 0.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/homer-bot-GGUF/resolve/main/homer-bot.IQ4_XS.gguf) \| IQ4_XS \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/homer-bot-GGUF/resolve/main/homer-bot.Q4_K_S.gguf) \| Q4_K_S \| 0.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/homer-bot-GGUF/resolve/main/homer-bot.Q3_K_L.gguf) \| Q3_K_L \| 0.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/homer-bot-GGUF/resolve/main/homer-bot.Q4_K_M.gguf) \| Q4_K_M \| 0.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/homer-bot-GGUF/resolve/main/homer-bot.Q5_K_S.gguf) \| Q5_K_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/homer-bot-GGUF/resolve/main/homer-bot.Q5_K_M.gguf) \| Q5_K_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/homer-bot-GGUF/resolve/main/homer-bot.Q6_K.gguf) \| Q6_K \| 0.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/homer-bot-GGUF/resolve/main/homer-bot.Q8_0.gguf) \| Q8_0 \| 0.5 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/homer-bot-GGUF/resolve/main/homer-bot.f16.gguf) \| f16 \| 0.8 \| 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/Llama_3.x_70b_Legion_Electra_fusion_v2-i1-GGUF	mradermacher	2025-05-24T05:37:02Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Nexesenex/Llama_3.x_70b_Legion_Electra_fusion_v2", "base_model:quantized:Nexesenex/Llama_3.x_70b_Legion_Electra_fusion_v2", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-23T16:53:57Z	--- base_model: Nexesenex/Llama_3.x_70b_Legion_Electra_fusion_v2 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Nexesenex/Llama_3.x_70b_Legion_Electra_fusion_v2 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Llama_3.x_70b_Legion_Electra_fusion_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/Llama_3.x_70b_Legion_Electra_fusion_v2-i1-GGUF/resolve/main/Llama_3.x_70b_Legion_Electra_fusion_v2.i1-IQ1_S.gguf) \| i1-IQ1_S \| 15.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Legion_Electra_fusion_v2-i1-GGUF/resolve/main/Llama_3.x_70b_Legion_Electra_fusion_v2.i1-IQ1_M.gguf) \| i1-IQ1_M \| 16.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Legion_Electra_fusion_v2-i1-GGUF/resolve/main/Llama_3.x_70b_Legion_Electra_fusion_v2.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 19.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Legion_Electra_fusion_v2-i1-GGUF/resolve/main/Llama_3.x_70b_Legion_Electra_fusion_v2.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 21.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Legion_Electra_fusion_v2-i1-GGUF/resolve/main/Llama_3.x_70b_Legion_Electra_fusion_v2.i1-IQ2_S.gguf) \| i1-IQ2_S \| 22.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Legion_Electra_fusion_v2-i1-GGUF/resolve/main/Llama_3.x_70b_Legion_Electra_fusion_v2.i1-IQ2_M.gguf) \| i1-IQ2_M \| 24.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Legion_Electra_fusion_v2-i1-GGUF/resolve/main/Llama_3.x_70b_Legion_Electra_fusion_v2.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 24.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Legion_Electra_fusion_v2-i1-GGUF/resolve/main/Llama_3.x_70b_Legion_Electra_fusion_v2.i1-Q2_K.gguf) \| i1-Q2_K \| 26.5 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Legion_Electra_fusion_v2-i1-GGUF/resolve/main/Llama_3.x_70b_Legion_Electra_fusion_v2.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 27.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Legion_Electra_fusion_v2-i1-GGUF/resolve/main/Llama_3.x_70b_Legion_Electra_fusion_v2.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 29.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Legion_Electra_fusion_v2-i1-GGUF/resolve/main/Llama_3.x_70b_Legion_Electra_fusion_v2.i1-IQ3_S.gguf) \| i1-IQ3_S \| 31.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Legion_Electra_fusion_v2-i1-GGUF/resolve/main/Llama_3.x_70b_Legion_Electra_fusion_v2.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 31.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Legion_Electra_fusion_v2-i1-GGUF/resolve/main/Llama_3.x_70b_Legion_Electra_fusion_v2.i1-IQ3_M.gguf) \| i1-IQ3_M \| 32.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Legion_Electra_fusion_v2-i1-GGUF/resolve/main/Llama_3.x_70b_Legion_Electra_fusion_v2.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 34.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Legion_Electra_fusion_v2-i1-GGUF/resolve/main/Llama_3.x_70b_Legion_Electra_fusion_v2.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 37.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Legion_Electra_fusion_v2-i1-GGUF/resolve/main/Llama_3.x_70b_Legion_Electra_fusion_v2.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 38.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Legion_Electra_fusion_v2-i1-GGUF/resolve/main/Llama_3.x_70b_Legion_Electra_fusion_v2.i1-Q4_0.gguf) \| i1-Q4_0 \| 40.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Legion_Electra_fusion_v2-i1-GGUF/resolve/main/Llama_3.x_70b_Legion_Electra_fusion_v2.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 40.4 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Legion_Electra_fusion_v2-i1-GGUF/resolve/main/Llama_3.x_70b_Legion_Electra_fusion_v2.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Legion_Electra_fusion_v2-i1-GGUF/resolve/main/Llama_3.x_70b_Legion_Electra_fusion_v2.i1-Q4_1.gguf) \| i1-Q4_1 \| 44.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Legion_Electra_fusion_v2-i1-GGUF/resolve/main/Llama_3.x_70b_Legion_Electra_fusion_v2.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Legion_Electra_fusion_v2-i1-GGUF/resolve/main/Llama_3.x_70b_Legion_Electra_fusion_v2.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 50.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama_3.x_70b_Legion_Electra_fusion_v2-i1-GGUF/resolve/main/Llama_3.x_70b_Legion_Electra_fusion_v2.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama_3.x_70b_Legion_Electra_fusion_v2-i1-GGUF/resolve/main/Llama_3.x_70b_Legion_Electra_fusion_v2.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 58.0 \| 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 -->
duydc/qwen-2.5-7b-formal-alpaca-instruct-2452025	duydc	2025-05-24T05:36:28Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-24T05:25:42Z	--- base_model: Qwen/Qwen2.5-7B-Instruct library_name: transformers model_name: qwen-2.5-7b-formal-alpaca-instruct-2452025 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for qwen-2.5-7b-formal-alpaca-instruct-2452025 This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-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="duydc/qwen-2.5-7b-formal-alpaca-instruct-2452025", 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/duydc/huggingface/runs/nny8kzrz) This model was trained with SFT. ### Framework versions - TRL: 0.12.1 - Transformers: 4.46.3 - Pytorch: 2.4.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## 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}} } ```
MinaMila/llama_instbase_3b_LoRa_Adult_cfda_ep3_22	MinaMila	2025-05-24T05:32:43Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T05:32:40Z	--- 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]
KingEmpire/sn21_omega_2405_1	KingEmpire	2025-05-24T05:31:58Z	0	0	null	[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-24T05:15:23Z	--- 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).
dherya/nanoVLM	dherya	2025-05-24T05:28:21Z	0	0	nanovlm	[ "nanovlm", "safetensors", "vision-language", "multimodal", "research", "image-text-to-text", "license:mit", "region:us" ]	image-text-to-text	2025-05-24T05:27:26Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards library_name: nanovlm license: mit pipeline_tag: image-text-to-text tags: - vision-language - multimodal - research --- nanoVLM is a minimal and lightweight Vision-Language Model (VLM) designed for efficient training and experimentation. Built using pure PyTorch, the entire model architecture and training logic fits within ~750 lines of code. It combines a ViT-based image encoder (SigLIP-B/16-224-85M) with a lightweight causal language model (SmolLM2-135M), resulting in a compact 222M parameter model. For more information, check out the base model on https://huggingface.co/lusxvr/nanoVLM-222M. Usage: Clone the nanoVLM repository: https://github.com/huggingface/nanoVLM. Follow the install instructions and run the following code: ```python from models.vision_language_model import VisionLanguageModel model = VisionLanguageModel.from_pretrained("dherya/nanoVLM") ```
MinaMila/llama_instbase_3b_LoRa_ACSEmployment_2_cfda_ep7_22	MinaMila	2025-05-24T05:26:01Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T05:25:54Z	--- 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]
watch-katrina-lim-kiffy-full-origin/VIDEO-18-Katrina-Lim-Viral-Kiffy-Viral-Video-Full-Video	watch-katrina-lim-kiffy-full-origin	2025-05-24T05:25:53Z	0	0	null	[ "region:us" ]	null	2025-05-24T05:24:03Z	Watch 🟢 ➤ ➤ ➤ <a href="https://witvidz.com/originalviralvideo"> 🌐 Click Here To link (Full Viral Video Link) 🔴 ➤►DOWNLOAD👉👉🟢 ➤ Watch 🟢 ➤ ➤ ➤ <a href="https://witvidz.com/originalviralvideo"> 🌐 Click Here To link (Full Viral Video Link) 🔴 ➤►DOWNLOAD👉👉🟢 ➤ ![68747470733a2f2f692e696d6775722e636f6d2f644a486b345a712e676966.gif](https://cdn-uploads.huggingface.co/production/uploads/6831511e46229e4f23a4ff73/aC1jQMOHfcWImx3anjz5y.gif)
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep8_55	MinaMila	2025-05-24T05:23:59Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T05:23:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
sh1fu-0/distilbert-agnews-classifier	sh1fu-0	2025-05-24T05:21:10Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "ag-news", "news-categorization", "en", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-24T04:43:36Z	--- language: en license: mit pipeline_tag: text-classification tags: - ag-news - text-classification - distilbert - transformers - news-categorization --- # 📰 DistilBERT AG News Classifier This is a fine-tuned [DistilBERT](https://huggingface.co/distilbert-base-uncased) model for news article classification based on the [AG News](https://www.kaggle.com/datasets/amananandrai/ag-news-classification-dataset) dataset. It categorizes news articles into four categories: - 🌍 World - 🏛️ Politics (also known as Business in AG News) - 💻 Tech - 🏈 Sports ## 🧠 Model Details - Base model: `distilbert-base-uncased` - Framework: PyTorch with Hugging Face Transformers - Trained on: AG News dataset - Use case: Classify news snippets or headlines into one of 4 classes ## 🗃️ Dataset AG News is a news classification dataset with 4 categories: 1. World 2. Sports 3. Business 4. Sci/Tech Each sample consists of a title and description. ## 📥 How to Use ### With Transformers (Python): ```python from transformers import pipeline classifier = pipeline("text-classification", model="sh1fu-0/distilbert-agnews-classifier") result = classifier("NASA's new telescope discovers water vapor on a distant exoplanet.") print(result)
fabhiansan/indoBERT-Large-FactChecking-Summarization	fabhiansan	2025-05-24T05:20:46Z	13	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "natural-language-inference", "indonesian", "perturbation-robustness", "id", "dataset:fabhiansan/XSUM-Indonesia-AMR-NLI", "base_model:indobenchmark/indobert-large-p2", "base_model:finetune:indobenchmark/indobert-large-p2", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-08T20:47:31Z	--- license: mit language: - id library_name: transformers tags: - text-classification - natural-language-inference - indonesian - perturbation-robustness - bert datasets: - fabhiansan/XSUM-Indonesia-AMR-NLI pipeline_tag: text-classification widget: - text: 'Premis: [TEKS PREMIS DI SINI]. Hipotesis: [TEKS HIPOTESIS DI SINI]' base_model: - indobenchmark/indobert-large-p2 --- # Indonesian BERT Large for Natural Language Inference (Perturbation Weighted) ## Deskripsi Model Model ini adalah versi fine-tuned dari `indobenchmark/indobert-large-p2` yang dilatih untuk tugas Natural Language Inference (NLI) biner pada data berbahasa Indonesia. Tujuan utama NLI adalah untuk menentukan apakah sebuah "hipotesis" dapat disimpulkan dari sebuah "premis". \ Model ini secara spesifik dilatih dengan strategi pembobotan sampel ganda: 1. Pembobotan untuk menyeimbangkan kelas label utama (entailment vs. non-entailment). 2. Pembobotan tambahan untuk jenis-jenis perturbasi spesifik dalam sampel kelas negatif (label 0), untuk meningkatkan ketahanan model terhadap variasi linguistik atau artefak data tertentu. Model ini menghasilkan salah satu dari dua label (0 untuk non-entailment/kontradiksi, 1 untuk entailment). \| metrik \| score \| \|---------\|--------\| \| accuracy \| 0.9129205120571598 \| \| macro_precision \| 0.9052220320834325 \| \| macro_recall \| 0.8766231236407768 \| \| macro_f1 \| 0.8893040191206835 \| \|average_loss \| 0.5746491376413663 \| \| train_loss_sample_weighted \| 0.07019188567586254 \| ### Penggunaan yang Ditujukan Model ini ditujukan untuk digunakan dalam tugas klasifikasi teks NLI biner dalam bahasa Indonesia. Dapat digunakan untuk: * Memverifikasi apakah suatu klaim (hipotesis) didukung oleh teks sumber (premis). * Menganalisis hubungan logis antara beberapa kalimat teks sumber dan kalimat ringkasannya. * Model akan menganggap ringkasan tidak entails ketika terjadi halusinasi. * Halusinasi yang dapat dideteksi oleh model ini adalah (Pagnoni dkk., 2021): * Predicate error * Discourse link error * Entity Error * Circumstance Error * Out of Article Error ## Cara Menggunakan Anda dapat menggunakan model ini dengan pustaka `transformers` dari Hugging Face: ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch model_name = "fabhiansan/indoBERT-Large-FactChecking-Summarization" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) premise = "Timnas Indonesia berhasil memenangkan pertandingan sepak bola." hypothesis = "Indonesia kalah dalam laga tersebut." inputs = tokenizer(premise, hypothesis, return_tensors="pt", truncation=True, padding=True, max_length=512) inputs = {k: v.to(device) for k, v in inputs.items()} model.eval() # Set model ke mode evaluasi with torch.no_grad(): outputs = model(**inputs) logits = outputs.logits predictions = torch.argmax(logits, dim=-1) # Interpretasi hasil (asumsi label 0 = non-entailment, label 1 = entailment) if predictions.item() == 1: print("Hipotesis dapat disimpulkan dari premis (Entailment).") else: print("Hipotesis TIDAK dapat disimpulkan dari premis (Non-Entailment).")
fullsmritijainreal/VIDEO.18.Katrina.Lim.Viral.Kiffy.Viral.Video.Full.Video	fullsmritijainreal	2025-05-24T05:20:43Z	0	0	null	[ "region:us" ]	null	2025-05-24T05:18:40Z	Watch 🟢 ➤ ➤ ➤ <a href="https://newvidgallery.com/sdfsdfsd"> 🌐 Click Here To link (+VIDEO 18+)* Katrina Lim Viral Kiffy Viral Video Full Video ...) 🔴 ➤►DOWNLOAD👉👉🟢 ➤Watch 🟢 ➤ ➤ ➤ <a href="https://newvidgallery.com/sdfsdfsd"> 🌐 +VIDEO 18+)* Katrina Lim Viral Kiffy Viral Video Full Video ...
mradermacher/Qwen2.5-CoderX-14B-v0.5-GGUF	mradermacher	2025-05-24T05:19:09Z	327	2	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "qwen2", "trl", "sft", "en", "base_model:oscar128372/Qwen2.5-CoderX-14B-v0.5", "base_model:quantized:oscar128372/Qwen2.5-CoderX-14B-v0.5", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-21T14:00:00Z	--- base_model: oscar128372/Qwen2.5-CoderX-14B-v0.5 language: - en library_name: transformers license: apache-2.0 no_imatrix: '[42]9.4104,[43]9.6405,nan detected in blk.47.attn_q.weight' quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - sft --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/oscar128372/Qwen2.5-CoderX-14B-v0.5 <!-- provided-files --> ## 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/Qwen2.5-CoderX-14B-v0.5-GGUF/resolve/main/Qwen2.5-CoderX-14B-v0.5.Q2_K.gguf) \| Q2_K \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-CoderX-14B-v0.5-GGUF/resolve/main/Qwen2.5-CoderX-14B-v0.5.Q3_K_S.gguf) \| Q3_K_S \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-CoderX-14B-v0.5-GGUF/resolve/main/Qwen2.5-CoderX-14B-v0.5.Q3_K_M.gguf) \| Q3_K_M \| 7.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-CoderX-14B-v0.5-GGUF/resolve/main/Qwen2.5-CoderX-14B-v0.5.Q3_K_L.gguf) \| Q3_K_L \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-CoderX-14B-v0.5-GGUF/resolve/main/Qwen2.5-CoderX-14B-v0.5.IQ4_XS.gguf) \| IQ4_XS \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-CoderX-14B-v0.5-GGUF/resolve/main/Qwen2.5-CoderX-14B-v0.5.Q4_K_S.gguf) \| Q4_K_S \| 8.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-CoderX-14B-v0.5-GGUF/resolve/main/Qwen2.5-CoderX-14B-v0.5.Q4_K_M.gguf) \| Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-CoderX-14B-v0.5-GGUF/resolve/main/Qwen2.5-CoderX-14B-v0.5.Q5_K_S.gguf) \| Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-CoderX-14B-v0.5-GGUF/resolve/main/Qwen2.5-CoderX-14B-v0.5.Q5_K_M.gguf) \| Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-CoderX-14B-v0.5-GGUF/resolve/main/Qwen2.5-CoderX-14B-v0.5.Q6_K.gguf) \| Q6_K \| 12.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-CoderX-14B-v0.5-GGUF/resolve/main/Qwen2.5-CoderX-14B-v0.5.Q8_0.gguf) \| Q8_0 \| 15.8 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep6_55	MinaMila	2025-05-24T05:17:03Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T05:17:00Z	--- 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. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Triangle104/Qwen3-30B-A1.5B-High-Speed-Q4_K_S-GGUF	Triangle104	2025-05-24T05:16:16Z	0	0	transformers	[ "transformers", "gguf", "32 k context", "reasoning", "thinking", "qwen3", "4 experts activated", "double speed", "128 experts", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:DavidAU/Qwen3-30B-A1.5B-High-Speed", "base_model:quantized:DavidAU/Qwen3-30B-A1.5B-High-Speed", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-24T05:13:27Z	--- library_name: transformers pipeline_tag: text-generation tags: - 32 k context - reasoning - thinking - qwen3 - 4 experts activated - double speed - 128 experts - llama-cpp - gguf-my-repo base_model: DavidAU/Qwen3-30B-A1.5B-High-Speed --- # Triangle104/Qwen3-30B-A1.5B-High-Speed-Q4_K_S-GGUF This model was converted to GGUF format from [`DavidAU/Qwen3-30B-A1.5B-High-Speed`](https://huggingface.co/DavidAU/Qwen3-30B-A1.5B-High-Speed) 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/DavidAU/Qwen3-30B-A1.5B-High-Speed) for more details on the model. --- This is a simple "finetune" of the Qwen's "Qwen 30B-A3B" (MOE) model, setting the experts in use from 8 to 4 (out of 128 experts). This method close to doubles the speed of the model and uses 1.5B (of 30B) parameters instead of 3B (of 30B) parameters. Depending on the application you may want to use the regular model ("30B-A3B"), and use this model for simpler use case(s) although I did not notice any loss of function during routine (but not extensive) testing. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Qwen3-30B-A1.5B-High-Speed-Q4_K_S-GGUF --hf-file qwen3-30b-a1.5b-high-speed-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Qwen3-30B-A1.5B-High-Speed-Q4_K_S-GGUF --hf-file qwen3-30b-a1.5b-high-speed-q4_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Qwen3-30B-A1.5B-High-Speed-Q4_K_S-GGUF --hf-file qwen3-30b-a1.5b-high-speed-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Qwen3-30B-A1.5B-High-Speed-Q4_K_S-GGUF --hf-file qwen3-30b-a1.5b-high-speed-q4_k_s.gguf -c 2048 ```
DAKARA555/deepfera	DAKARA555	2025-05-24T05:11:30Z	65	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:Wan-AI/Wan2.1-I2V-14B-480P", "base_model:adapter:Wan-AI/Wan2.1-I2V-14B-480P", "license:apache-2.0", "region:us" ]	text-to-image	2025-05-14T16:36:06Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/white.png base_model: Wan-AI/Wan2.1-I2V-14B-480P instance_prompt: null license: apache-2.0 --- # deepfera <Gallery /> ## Model description https://civitai.com/models/1395313/wan-dr34mjob-doublesinglehandy-blowjob?modelVersionId=1610465 https://huggingface.co/DAKARA555/deepfera/resolve/main/WAN_dr34mj0b.safetensors?download=true ## Download model Weights for this model are available in Safetensors format. [Download](/DAKARA555/deepfera/tree/main) them in the Files & versions tab.
atul10/whisper-large-v3-turbo-nepali-v1	atul10	2025-05-24T05:11:24Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ne", "hi", "nl", "base_model:openai/whisper-large-v3-turbo", "base_model:finetune:openai/whisper-large-v3-turbo", "license:mit", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-24T04:56:30Z	--- library_name: transformers language: - ne - hi - nl license: mit base_model: openai/whisper-large-v3-turbo tags: - generated_from_trainer metrics: - wer model-index: - name: Whisper Large v3 Turbo Nepali results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition metrics: - name: Wer type: wer value: 23.63425925925926 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Large v3 Turbo Nepali This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the OpenSLR54 dataset. It achieves the following results on the evaluation set: - Loss: 0.1707 - Wer: 23.6343 - Cer: 5.4903 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\|:-------:\| \| 0.3073 \| 0.3597 \| 300 \| 0.2895 \| 53.2870 \| 13.5643 \| \| 0.2457 \| 0.7194 \| 600 \| 0.2396 \| 45.3704 \| 11.6816 \| \| 0.166 \| 1.0791 \| 900 \| 0.2062 \| 37.9167 \| 9.6668 \| \| 0.1477 \| 1.4388 \| 1200 \| 0.1949 \| 37.4306 \| 9.3071 \| \| 0.1284 \| 1.7986 \| 1500 \| 0.1680 \| 32.6620 \| 8.3235 \| \| 0.0745 \| 2.1583 \| 1800 \| 0.1706 \| 31.1574 \| 7.5272 \| \| 0.0701 \| 2.5180 \| 2100 \| 0.1661 \| 32.0370 \| 7.7217 \| \| 0.0777 \| 2.8777 \| 2400 \| 0.1599 \| 28.6111 \| 7.1308 \| \| 0.0455 \| 3.2374 \| 2700 \| 0.1723 \| 28.7037 \| 7.0097 \| \| 0.0375 \| 3.5971 \| 3000 \| 0.1579 \| 26.9444 \| 6.3674 \| \| 0.0374 \| 3.9568 \| 3300 \| 0.1639 \| 26.8981 \| 6.2794 \| \| 0.0171 \| 4.3165 \| 3600 \| 0.1711 \| 25.3241 \| 6.2280 \| \| 0.0219 \| 4.6763 \| 3900 \| 0.1638 \| 25.0 \| 5.9307 \| \| 0.0089 \| 5.0360 \| 4200 \| 0.1635 \| 24.5139 \| 5.7435 \| \| 0.0072 \| 5.3957 \| 4500 \| 0.1717 \| 24.1898 \| 5.5711 \| \| 0.0059 \| 5.7554 \| 4800 \| 0.1707 \| 23.6343 \| 5.4903 \| ### Framework versions - Transformers 4.46.3 - Pytorch 2.5.1+cxx11.abi - Datasets 3.2.0 - Tokenizers 0.20.3
watch-katrina-lim-kiffy-full-origin/full.smriti.jain.real.video.smriti.jain.viral.video.instagram.id.smriti.jaindd	watch-katrina-lim-kiffy-full-origin	2025-05-24T05:10:15Z	0	0	null	[ "region:us" ]	null	2025-05-24T05:09:26Z	Watch 🟢 ➤ ➤ ➤ <a href="https://witvidz.com/originalviralvideo"> 🌐 Click Here To link (Full Viral Video Link) 🔴 ➤►DOWNLOAD👉👉🟢 ➤ Watch 🟢 ➤ ➤ ➤ <a href="https://witvidz.com/originalviralvideo"> 🌐 Click Here To link (Full Viral Video Link) 🔴 ➤►DOWNLOAD👉👉🟢 ➤
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep4_55	MinaMila	2025-05-24T05:10:12Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T05:10:08Z	--- 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]
Watchkatrinalim/Watch.katrina.lim.kiffy.full.original.viral.leaked.video	Watchkatrinalim	2025-05-24T05:03:26Z	0	0	null	[ "region:us" ]	null	2025-05-24T05:02:35Z	Watch 🟢 ➤ ➤ ➤ <a href="https://viraltrendzzz.com/sdvsdvdd"> 🌐 Click Here To link (Watch.katrina.lim.kiffy.full.original.viral.leaked.video) 🔴 ➤►DOWNLOAD👉👉🟢 ➤Watch 🟢 ➤ ➤ ➤ <a href="https://viraltrendzzz.com/sdvsdvdd"> 🌐 Watch.katrina.lim.kiffy.full.original.viral.leaked.video
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep2_55	MinaMila	2025-05-24T05:03:16Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T05:03:12Z	--- 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]
fats-fme/69031ba1-7feb-4223-8cc8-6f6576f8c4ed	fats-fme	2025-05-24T05:00:15Z	0	0	peft	[ "peft", "safetensors", "gemma", "axolotl", "generated_from_trainer", "base_model:unsloth/codegemma-7b", "base_model:adapter:unsloth/codegemma-7b", "license:apache-2.0", "region:us" ]	null	2025-05-24T04:22:52Z	--- library_name: peft license: apache-2.0 base_model: unsloth/codegemma-7b tags: - axolotl - generated_from_trainer model-index: - name: 69031ba1-7feb-4223-8cc8-6f6576f8c4ed results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/codegemma-7b bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 3a95f0218346ddba_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_input: input field_instruction: instruct field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto early_stopping_patience: 3 eval_max_new_tokens: 128 eval_steps: 100 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 32 gradient_checkpointing: true group_by_length: false hub_model_id: fats-fme/69031ba1-7feb-4223-8cc8-6f6576f8c4ed hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 5.0e-05 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 16 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lora_target_modules: - q_proj - v_proj lr_scheduler: constant_with_warmup max_memory: 0: 130GB max_steps: 100 micro_batch_size: 1 mlflow_experiment_name: /tmp/3a95f0218346ddba_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 100 saves_per_epoch: null sequence_len: 2048 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: dc30820e-a6ab-4a52-b146-21660afc11be wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: dc30820e-a6ab-4a52-b146-21660afc11be warmup_steps: 200 weight_decay: 0.01 xformers_attention: null ``` </details><br> # 69031ba1-7feb-4223-8cc8-6f6576f8c4ed This model is a fine-tuned version of [unsloth/codegemma-7b](https://huggingface.co/unsloth/codegemma-7b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0505 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 32 - 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_with_warmup - lr_scheduler_warmup_steps: 200 - training_steps: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0037 \| 1 \| 3.1381 \| \| 2.0132 \| 0.3743 \| 100 \| 2.0505 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
sand-ai/MAGI-1	sand-ai	2025-05-24T05:00:09Z	0	565	magi-1	[ "magi-1", "diffusers", "safetensors", "image-to-video", "en", "arxiv:2505.13211", "license:apache-2.0", "region:us" ]	image-to-video	2025-04-18T07:49:05Z	--- license: apache-2.0 language: - en pipeline_tag: image-to-video library_name: magi-1 --- ![magi-logo](figures/logo_black.png) ----- <p align="center" style="line-height: 1;"> <a href="https://arxiv.org/abs/2505.13211" target="_blank" style="margin: 2px;"> <img alt="paper" src="https://img.shields.io/badge/Paper-arXiv-B31B1B?logo=arxiv" style="display: inline-block; vertical-align: middle;"> </a> <a href="https://sand.ai" target="_blank" style="margin: 2px;"> <img alt="blog" src="https://img.shields.io/badge/Sand%20AI-Homepage-333333.svg?logo=data:image/svg%2bxml;base64,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" style="display: inline-block; vertical-align: middle;"> </a> <a href="https://magi.sand.ai" target="_blank" style="margin: 2px;"> <img alt="product" src="https://img.shields.io/badge/Magi-Product-logo.svg?logo=data:image/svg%2bxml;base64,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&color=DCBE7E" style="display: inline-block; vertical-align: middle;"> </a> <a href="https://huggingface.co/sand-ai" target="_blank" style="margin: 2px;"> <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Sand AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"> </a> <a href="https://x.com/SandAI_HQ" target="_blank" style="margin: 2px;"> <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-Sand%20AI-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"> </a> <a href="https://discord.gg/hgaZ86D7Wv" target="_blank" style="margin: 2px;"> <img alt="Discord" src="https://img.shields.io/badge/Discord-Sand%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"> </a> <a href="https://github.com/SandAI-org/Magi/LICENSE" target="_blank" style="margin: 2px;"> <img alt="license" src="https://img.shields.io/badge/License-Apache2.0-green?logo=Apache" style="display: inline-block; vertical-align: middle;"> </a> </p> # MAGI-1: Autoregressive Video Generation at Scale This repository contains the [code](https://github.com/SandAI-org/MAGI-1) for the MAGI-1 model, pre-trained weights and inference code. You can find more information on our [technical report](https://static.magi.world/static/files/MAGI_1.pdf) or directly create magic with MAGI-1 [here](http://sand.ai) . 🚀✨ ## 🔥🔥🔥 Latest News - Apr 30, 2025: MAGI-1 4.5B distill and distill+quant models are coming soon 🎉 — we’re putting on the final touches, stay tuned! - Apr 30, 2025: MAGI-1 4.5B model has been released 🎉. We've updated the model weights — check it out! - Apr 21, 2025: MAGI-1 is here 🎉. We've released the model weights and inference code — check it out! ## 1. About We present MAGI-1, a world model that generates videos by *autoregressively* predicting a sequence of video chunks, defined as fixed-length segments of consecutive frames. Trained to denoise per-chunk noise that increases monotonically over time, MAGI-1 enables causal temporal modeling and naturally supports streaming generation. It achieves strong performance on image-to-video (I2V) tasks conditioned on text instructions, providing high temporal consistency and scalability, which are made possible by several algorithmic innovations and a dedicated infrastructure stack. MAGI-1 further supports controllable generation via chunk-wise prompting, enabling smooth scene transitions, long-horizon synthesis, and fine-grained text-driven control. We believe MAGI-1 offers a promising direction for unifying high-fidelity video generation with flexible instruction control and real-time deployment. ## 2. Model Summary ### Transformer-based VAE - Variational autoencoder (VAE) with transformer-based architecture, 8x spatial and 4x temporal compression. - Fastest average decoding time and highly competitive reconstruction quality ### Auto-Regressive Denoising Algorithm MAGI-1 is an autoregressive denoising video generation model generating videos chunk-by-chunk instead of as a whole. Each chunk (24 frames) is denoised holistically, and the generation of the next chunk begins as soon as the current one reaches a certain level of denoising. This pipeline design enables concurrent processing of up to four chunks for efficient video generation. ![auto-regressive denosing algorithm](figures/algorithm.png) ### Diffusion Model Architecture MAGI-1 is built upon the Diffusion Transformer, incorporating several key innovations to enhance training efficiency and stability at scale. These advancements include Block-Causal Attention, Parallel Attention Block, QK-Norm and GQA, Sandwich Normalization in FFN, SwiGLU, and Softcap Modulation. For more details, please refer to the [technical report.](https://static.magi.world/static/files/MAGI_1.pdf) <div align="center"> <img src="figures/dit_architecture.png" alt="diffusion model architecture" width="500" /> </div> ### Distillation Algorithm We adopt a shortcut distillation approach that trains a single velocity-based model to support variable inference budgets. By enforcing a self-consistency constraint—equating one large step with two smaller steps—the model learns to approximate flow-matching trajectories across multiple step sizes. During training, step sizes are cyclically sampled from {64, 32, 16, 8}, and classifier-free guidance distillation is incorporated to preserve conditional alignment. This enables efficient inference with minimal loss in fidelity. ## 3. Model Zoo We provide the pre-trained weights for MAGI-1, including the 24B and 4.5B models, as well as the corresponding distill and distill+quant models. The model weight links are shown in the table. \| Model \| Link \| Recommend Machine \| \| ------------------------------ \| -------------------------------------------------------------------- \| ------------------------------- \| \| T5 \| [T5](https://huggingface.co/sand-ai/MAGI-1/tree/main/ckpt/t5) \| - \| \| MAGI-1-VAE \| [MAGI-1-VAE](https://huggingface.co/sand-ai/MAGI-1/tree/main/ckpt/vae) \| - \| \| MAGI-1-24B \| [MAGI-1-24B](https://huggingface.co/sand-ai/MAGI-1/tree/main/ckpt/magi/24B_base) \| H100/H800 × 8 \| \| MAGI-1-24B-distill \| [MAGI-1-24B-distill](https://huggingface.co/sand-ai/MAGI-1/tree/main/ckpt/magi/24B_distill) \| H100/H800 × 8 \| \| MAGI-1-24B-distill+fp8_quant \| [MAGI-1-24B-distill+quant](https://huggingface.co/sand-ai/MAGI-1/tree/main/ckpt/magi/24B_distill_quant) \| H100/H800 × 4 or RTX 4090 × 8 \| \| MAGI-1-4.5B \| [MAGI-1-4.5B](https://huggingface.co/sand-ai/MAGI-1/tree/main/ckpt/magi/4.5B_base) \| RTX 4090 × 1 \| \| MAGI-1-4.5B-distill \| Coming soon \| RTX 4090 × 1 \| \| MAGI-1-4.5B-distill+fp8_quant \| Coming soon \| RTX 4090 × 1 \| > [!NOTE] > > For 4.5B models, any machine with at least 24GB of GPU memory is sufficient. ## 4. Evaluation ### In-house Human Evaluation MAGI-1 achieves state-of-the-art performance among open-source models like Wan-2.1 and HunyuanVideo and closed-source model like Hailuo (i2v-01), particularly excelling in instruction following and motion quality, positioning it as a strong potential competitor to closed-source commercial models such as Kling. ![inhouse human evaluation](figures/inhouse_human_evaluation.png) ### Physical Evaluation Thanks to the natural advantages of autoregressive architecture, Magi achieves far superior precision in predicting physical behavior on the [Physics-IQ benchmark](https://github.com/google-deepmind/physics-IQ-benchmark) through video continuation—significantly outperforming all existing models. \| Model \| Phys. IQ Score ↑ \| Spatial IoU ↑ \| Spatio Temporal ↑ \| Weighted Spatial IoU ↑ \| MSE ↓ \| \|----------------\|------------------\|---------------\|-------------------\|-------------------------\|--------\| \| V2V Models \| \| \| \| \| \| \| Magi-24B (V2V) \| 56.02 \| 0.367 \| 0.270 \| 0.304 \| 0.005 \| \| Magi-4.5B (V2V) \| 42.44 \| 0.234 \| 0.285 \| 0.188 \| 0.007 \| \| VideoPoet (V2V)\| 29.50 \| 0.204 \| 0.164 \| 0.137 \| 0.010 \| \| I2V Models \| \| \| \| \| \| \| Magi-24B (I2V) \| 30.23 \| 0.203 \| 0.151 \| 0.154 \| 0.012 \| \| Kling1.6 (I2V) \| 23.64 \| 0.197 \| 0.086 \| 0.144 \| 0.025 \| \| VideoPoet (I2V)\| 20.30 \| 0.141 \| 0.126 \| 0.087 \| 0.012 \| \| Gen 3 (I2V) \| 22.80 \| 0.201 \| 0.115 \| 0.116 \| 0.015 \| \| Wan2.1 (I2V) \| 20.89 \| 0.153 \| 0.100 \| 0.112 \| 0.023 \| \| Sora (I2V) \| 10.00 \| 0.138 \| 0.047 \| 0.063 \| 0.030 \| \| GroundTruth\| 100.0 \| 0.678 \| 0.535 \| 0.577 \| 0.002 \| ## 5. How to run ### Environment Preparation We provide two ways to run MAGI-1, with the Docker environment being the recommended option. Run with Docker Environment (Recommend) ```bash docker pull sandai/magi:latest docker run -it --gpus all --privileged --shm-size=32g --name magi --net=host --ipc=host --ulimit memlock=-1 --ulimit stack=6710886 sandai/magi:latest /bin/bash ``` Run with Source Code ```bash # Create a new environment conda create -n magi python==3.10.12 # Install pytorch conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 pytorch-cuda=12.4 -c pytorch -c nvidia # Install other dependencies pip install -r requirements.txt # Install ffmpeg conda install -c conda-forge ffmpeg=4.4 # For GPUs based on the Hopper architecture (e.g., H100/H800), it is recommended to install MagiAttention(https://github.com/SandAI-org/MagiAttention) for acceleration. For non-Hopper GPUs, installing MagiAttention is not necessary. git clone [email protected]:SandAI-org/MagiAttention.git cd MagiAttention git submodule update --init --recursive pip install --no-build-isolation . ``` ### Inference Command To run the `MagiPipeline`, you can control the input and output by modifying the parameters in the `example/24B/run.sh` or `example/4.5B/run.sh` script. Below is an explanation of the key parameters: #### Parameter Descriptions - `--config_file`: Specifies the path to the configuration file, which contains model configuration parameters, e.g., `example/24B/24B_config.json`. - `--mode`: Specifies the mode of operation. Available options are: - `t2v`: Text to Video - `i2v`: Image to Video - `v2v`: Video to Video - `--prompt`: The text prompt used for video generation, e.g., `"Good Boy"`. - `--image_path`: Path to the image file, used only in `i2v` mode. - `--prefix_video_path`: Path to the prefix video file, used only in `v2v` mode. - `--output_path`: Path where the generated video file will be saved. #### Bash Script ```bash #!/bin/bash # Run 24B MAGI-1 model bash example/24B/run.sh # Run 4.5B MAGI-1 model bash example/4.5B/run.sh ``` #### Customizing Parameters You can modify the parameters in `run.sh` as needed. For example: - To use the Image to Video mode (`i2v`), set `--mode` to `i2v` and provide `--image_path`: ```bash --mode i2v \ --image_path example/assets/image.jpeg \ ``` - To use the Video to Video mode (`v2v`), set `--mode` to `v2v` and provide `--prefix_video_path`: ```bash --mode v2v \ --prefix_video_path example/assets/prefix_video.mp4 \ ``` By adjusting these parameters, you can flexibly control the input and output to meet different requirements. ### Some Useful Configs (for config.json) > [!NOTE] > > - If you are running 24B model with RTX 4090 \* 8, please set `pp_size:2 cp_size: 4`. > > - Our model supports arbitrary resolutions. To accelerate inference process, the default resolution for the 4.5B model is set to 720×720 in the `4.5B_config.json`. \| Config \| Help \| \| -------------- \| ------------------------------------------------------------ \| \| seed \| Random seed used for video generation \| \| video_size_h \| Height of the video \| \| video_size_w \| Width of the video \| \| num_frames \| Controls the duration of generated video \| \| fps \| Frames per second, 4 video frames correspond to 1 latent_frame \| \| cfg_number \| Base model uses cfg_number==3, distill and quant model uses cfg_number=1 \| \| load \| Directory containing a model checkpoint. \| \| t5_pretrained \| Path to load pretrained T5 model \| \| vae_pretrained \| Path to load pretrained VAE model \| ## 6. License This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details. ## 7. Citation If you find our code or model useful in your research, please cite: ```bibtex @misc{ai2025magi1autoregressivevideogeneration, title={MAGI-1: Autoregressive Video Generation at Scale}, author={Sand. ai and Hansi Teng and Hongyu Jia and Lei Sun and Lingzhi Li and Maolin Li and Mingqiu Tang and Shuai Han and Tianning Zhang and W. Q. Zhang and Weifeng Luo and Xiaoyang Kang and Yuchen Sun and Yue Cao and Yunpeng Huang and Yutong Lin and Yuxin Fang and Zewei Tao and Zheng Zhang and Zhongshu Wang and Zixun Liu and Dai Shi and Guoli Su and Hanwen Sun and Hong Pan and Jie Wang and Jiexin Sheng and Min Cui and Min Hu and Ming Yan and Shucheng Yin and Siran Zhang and Tingting Liu and Xianping Yin and Xiaoyu Yang and Xin Song and Xuan Hu and Yankai Zhang and Yuqiao Li}, year={2025}, eprint={2505.13211}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2505.13211}, } ``` ## 8. Contact If you have any questions, please feel free to raise an issue or contact us at [[email protected]](mailto:[email protected]) .
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep1_55	MinaMila	2025-05-24T04:59:47Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T04:59:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
johngreendr1/6a90c335-d9b6-414b-bc8f-18a1bb3e6d00	johngreendr1	2025-05-24T04:59:39Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:codellama/CodeLlama-7b-Instruct-hf", "base_model:adapter:codellama/CodeLlama-7b-Instruct-hf", "region:us" ]	null	2025-05-24T04:59:30Z	--- base_model: codellama/CodeLlama-7b-Instruct-hf library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1
VIDEO-18-Shamy-Laura-Viral-Video/wATCH.Shamy.Laura.viral.video.original.Link.Official	VIDEO-18-Shamy-Laura-Viral-Video	2025-05-24T04:59:04Z	0	0	null	[ "region:us" ]	null	2025-05-24T04:57:30Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
featherless-ai-quants/kakaocorp-kanana-1.5-8b-base-GGUF	featherless-ai-quants	2025-05-24T04:57:28Z	0	0	null	[ "gguf", "text-generation", "base_model:kakaocorp/kanana-1.5-8b-base", "base_model:quantized:kakaocorp/kanana-1.5-8b-base", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-24T04:50:32Z	--- base_model: kakaocorp/kanana-1.5-8b-base pipeline_tag: text-generation quantized_by: featherless-ai-quants --- # kakaocorp/kanana-1.5-8b-base GGUF Quantizations 🚀 ![Featherless AI Quants](./featherless-quants.png) Optimized GGUF quantization files for enhanced model performance > Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee. --- ## Available Quantizations 📊 \| Quantization Type \| File \| Size \| \|-------------------\|------\|------\| \| IQ4_XS \| [kakaocorp-kanana-1.5-8b-base-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/kakaocorp-kanana-1.5-8b-base-GGUF/blob/main/kakaocorp-kanana-1.5-8b-base-IQ4_XS.gguf) \| 4276.62 MB \| \| Q2_K \| [kakaocorp-kanana-1.5-8b-base-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/kakaocorp-kanana-1.5-8b-base-GGUF/blob/main/kakaocorp-kanana-1.5-8b-base-Q2_K.gguf) \| 3031.86 MB \| \| Q3_K_L \| [kakaocorp-kanana-1.5-8b-base-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/kakaocorp-kanana-1.5-8b-base-GGUF/blob/main/kakaocorp-kanana-1.5-8b-base-Q3_K_L.gguf) \| 4121.74 MB \| \| Q3_K_M \| [kakaocorp-kanana-1.5-8b-base-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/kakaocorp-kanana-1.5-8b-base-GGUF/blob/main/kakaocorp-kanana-1.5-8b-base-Q3_K_M.gguf) \| 3832.74 MB \| \| Q3_K_S \| [kakaocorp-kanana-1.5-8b-base-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/kakaocorp-kanana-1.5-8b-base-GGUF/blob/main/kakaocorp-kanana-1.5-8b-base-Q3_K_S.gguf) \| 3494.74 MB \| \| Q4_K_M \| [kakaocorp-kanana-1.5-8b-base-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/kakaocorp-kanana-1.5-8b-base-GGUF/blob/main/kakaocorp-kanana-1.5-8b-base-Q4_K_M.gguf) \| 4692.78 MB \| \| Q4_K_S \| [kakaocorp-kanana-1.5-8b-base-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/kakaocorp-kanana-1.5-8b-base-GGUF/blob/main/kakaocorp-kanana-1.5-8b-base-Q4_K_S.gguf) \| 4475.28 MB \| \| Q5_K_M \| [kakaocorp-kanana-1.5-8b-base-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/kakaocorp-kanana-1.5-8b-base-GGUF/blob/main/kakaocorp-kanana-1.5-8b-base-Q5_K_M.gguf) \| 5467.40 MB \| \| Q5_K_S \| [kakaocorp-kanana-1.5-8b-base-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/kakaocorp-kanana-1.5-8b-base-GGUF/blob/main/kakaocorp-kanana-1.5-8b-base-Q5_K_S.gguf) \| 5339.90 MB \| \| Q6_K \| [kakaocorp-kanana-1.5-8b-base-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/kakaocorp-kanana-1.5-8b-base-GGUF/blob/main/kakaocorp-kanana-1.5-8b-base-Q6_K.gguf) \| 6290.44 MB \| \| Q8_0 \| [kakaocorp-kanana-1.5-8b-base-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/kakaocorp-kanana-1.5-8b-base-GGUF/blob/main/kakaocorp-kanana-1.5-8b-base-Q8_0.gguf) \| 8145.11 MB \| --- ## ⚡ Powered by [Featherless AI](https://featherless.ai) ### Key Features - 🔥 Instant Hosting - Deploy any Llama model on HuggingFace instantly - 🛠️ Zero Infrastructure - No server setup or maintenance required - 📚 Vast Compatibility - Support for 2400+ models and counting - 💎 Affordable Pricing - Starting at just $10/month --- Links: [Get Started](https://featherless.ai) \| [Documentation](https://featherless.ai/docs) \| [Models](https://featherless.ai/models)
Elenanana/Qwen3-finetuned	Elenanana	2025-05-24T04:56:23Z	0	0	null	[ "safetensors", "unsloth", "license:mit", "region:us" ]	null	2025-05-24T01:42:38Z	--- license: mit tags: - unsloth ---
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep9_42	MinaMila	2025-05-24T04:52:40Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T04:52:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep8_42	MinaMila	2025-05-24T04:49:14Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T04:49:04Z	--- 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]
phospho-app/TransCabbage-gr00t-Bottle_In_Container-i5xn0	phospho-app	2025-05-24T04:47:36Z	0	0	null	[ "safetensors", "gr00t_n1", "phosphobot", "gr00t", "region:us" ]	null	2025-05-24T04:11:08Z	--- tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## This model was trained using phospho. Training was successfull, try it out on your robot! ## Training parameters: - Dataset: [TransCabbage/Bottle_In_Container](https://huggingface.co/datasets/TransCabbage/Bottle_In_Container) - Wandb run URL: None - Epochs: 10 - Batch size: 49 - Training steps: None 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
vmpsergio/f264e8e2-cf93-4674-a4a2-4d230b56ec37	vmpsergio	2025-05-24T04:46:49Z	0	0	peft	[ "peft", "safetensors", "gemma", "axolotl", "generated_from_trainer", "base_model:unsloth/codegemma-7b", "base_model:adapter:unsloth/codegemma-7b", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-24T04:17:11Z	--- library_name: peft license: apache-2.0 base_model: unsloth/codegemma-7b tags: - axolotl - generated_from_trainer model-index: - name: f264e8e2-cf93-4674-a4a2-4d230b56ec37 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: unsloth/codegemma-7b bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 3a95f0218346ddba_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_input: input field_instruction: instruct field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: true gradient_clipping: 0.85 group_by_length: false hub_model_id: vmpsergio/f264e8e2-cf93-4674-a4a2-4d230b56ec37 hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 280 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/3a95f0218346ddba_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: dc30820e-a6ab-4a52-b146-21660afc11be wandb_project: s56-28 wandb_run: your_name wandb_runid: dc30820e-a6ab-4a52-b146-21660afc11be warmup_steps: 40 weight_decay: 0.02 xformers_attention: true ``` </details><br> # f264e8e2-cf93-4674-a4a2-4d230b56ec37 This model is a fine-tuned version of [unsloth/codegemma-7b](https://huggingface.co/unsloth/codegemma-7b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.7144 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 40 - training_steps: 280 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.8017 \| 0.5239 \| 280 \| 2.7144 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
duydc/qwen-2.5-7b-alpaca-instruct-2452025-ver1	duydc	2025-05-24T04:39:39Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-24T02:56:24Z	--- base_model: Qwen/Qwen2.5-7B-Instruct library_name: transformers model_name: qwen-2.5-7b-alpaca-instruct-2452025-ver1 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for qwen-2.5-7b-alpaca-instruct-2452025-ver1 This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-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="duydc/qwen-2.5-7b-alpaca-instruct-2452025-ver1", 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/duydc/huggingface/runs/i0mmgnoy) This model was trained with SFT. ### Framework versions - TRL: 0.12.1 - Transformers: 4.46.3 - Pytorch: 2.4.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## 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}} } ```
infogep/bb7296fd-fd26-4547-8a3b-4114fd0dfaaa	infogep	2025-05-24T04:39:19Z	0	0	peft	[ "peft", "safetensors", "gemma", "axolotl", "generated_from_trainer", "base_model:unsloth/codegemma-7b", "base_model:adapter:unsloth/codegemma-7b", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-24T04:17:10Z	--- library_name: peft license: apache-2.0 base_model: unsloth/codegemma-7b tags: - axolotl - generated_from_trainer model-index: - name: bb7296fd-fd26-4547-8a3b-4114fd0dfaaa results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: unsloth/codegemma-7b bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 3a95f0218346ddba_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_input: input field_instruction: instruct field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: infogep/bb7296fd-fd26-4547-8a3b-4114fd0dfaaa hub_repo: null hub_strategy: end hub_token: null learning_rate: 2.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 500 micro_batch_size: 10 mixed_precision: bf16 mlflow_experiment_name: /tmp/3a95f0218346ddba_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: dc30820e-a6ab-4a52-b146-21660afc11be wandb_project: s56-7 wandb_run: your_name wandb_runid: dc30820e-a6ab-4a52-b146-21660afc11be warmup_steps: 50 weight_decay: 0.02 xformers_attention: true ``` </details><br> # bb7296fd-fd26-4547-8a3b-4114fd0dfaaa This model is a fine-tuned version of [unsloth/codegemma-7b](https://huggingface.co/unsloth/codegemma-7b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1450 ## 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-06 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 4.8543 \| 0.0012 \| 1 \| 4.5304 \| \| 2.0106 \| 0.2924 \| 250 \| 2.2002 \| \| 2.236 \| 0.5848 \| 500 \| 2.1450 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
jjwwwww/naruto-lora	jjwwwww	2025-05-24T04:38:07Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers-training", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2025-05-22T13:01:16Z	--- base_model: runwayml/stable-diffusion-v1-5 library_name: diffusers license: creativeml-openrail-m inference: true tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - diffusers-training - lora --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # LoRA text2image fine-tuning - jjwwwww/naruto-lora These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/naruto-blip-captions dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
umer-sohaib/umer_ai_v2	umer-sohaib	2025-05-24T04:37:12Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-05-24T04:37:12Z	--- license: creativeml-openrail-m ---
Triangle104/Qwen3-30B-A1.5B-High-Speed-Q3_K_S-GGUF	Triangle104	2025-05-24T04:36:53Z	0	0	transformers	[ "transformers", "gguf", "32 k context", "reasoning", "thinking", "qwen3", "4 experts activated", "double speed", "128 experts", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:DavidAU/Qwen3-30B-A1.5B-High-Speed", "base_model:quantized:DavidAU/Qwen3-30B-A1.5B-High-Speed", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-24T04:15:38Z	--- library_name: transformers pipeline_tag: text-generation tags: - 32 k context - reasoning - thinking - qwen3 - 4 experts activated - double speed - 128 experts - llama-cpp - gguf-my-repo base_model: DavidAU/Qwen3-30B-A1.5B-High-Speed --- # Triangle104/Qwen3-30B-A1.5B-High-Speed-Q3_K_S-GGUF This model was converted to GGUF format from [`DavidAU/Qwen3-30B-A1.5B-High-Speed`](https://huggingface.co/DavidAU/Qwen3-30B-A1.5B-High-Speed) 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/DavidAU/Qwen3-30B-A1.5B-High-Speed) for more details on the model. --- This is a simple "finetune" of the Qwen's "Qwen 30B-A3B" (MOE) model, setting the experts in use from 8 to 4 (out of 128 experts). This method close to doubles the speed of the model and uses 1.5B (of 30B) parameters instead of 3B (of 30B) parameters. Depending on the application you may want to use the regular model ("30B-A3B"), and use this model for simpler use case(s) although I did not notice any loss of function during routine (but not extensive) testing. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Qwen3-30B-A1.5B-High-Speed-Q3_K_S-GGUF --hf-file qwen3-30b-a1.5b-high-speed-q3_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Qwen3-30B-A1.5B-High-Speed-Q3_K_S-GGUF --hf-file qwen3-30b-a1.5b-high-speed-q3_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Qwen3-30B-A1.5B-High-Speed-Q3_K_S-GGUF --hf-file qwen3-30b-a1.5b-high-speed-q3_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Qwen3-30B-A1.5B-High-Speed-Q3_K_S-GGUF --hf-file qwen3-30b-a1.5b-high-speed-q3_k_s.gguf -c 2048 ```
sergioalves/ad4acbac-361d-48e6-80bf-c3c392815e87	sergioalves	2025-05-24T04:36:25Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:unsloth/Qwen2.5-14B", "base_model:quantized:unsloth/Qwen2.5-14B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-24T04:00:01Z	--- base_model: unsloth/Qwen2.5-14B library_name: transformers model_name: ad4acbac-361d-48e6-80bf-c3c392815e87 tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for ad4acbac-361d-48e6-80bf-c3c392815e87 This model is a fine-tuned version of [unsloth/Qwen2.5-14B](https://huggingface.co/unsloth/Qwen2.5-14B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="sergioalves/ad4acbac-361d-48e6-80bf-c3c392815e87", 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/w5fjkitn) 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}} } ```
lisabdunlap/test_e2	lisabdunlap	2025-05-24T04:35:04Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T04:33:40Z	--- base_model: unsloth/qwen3-8b tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: lisabdunlap - License: apache-2.0 - Finetuned from model : unsloth/qwen3-8b 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)
MechaSloth/prism_4m439	MechaSloth	2025-05-24T04:34:49Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-24T04:31:48Z	--- 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).
thejaminator/number-4e-05-qwen3_8b-epochs4	thejaminator	2025-05-24T04:33:54Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-8B", "base_model:finetune:unsloth/Qwen3-8B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-24T04:33:43Z	--- base_model: unsloth/Qwen3-8B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: thejaminator - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-8B 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)
SCH0/cardio-llama3ee-merged	SCH0	2025-05-24T04:33:01Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-24T04:31:59Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: SCH0 - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
TOMFORD79/Zombie_3	TOMFORD79	2025-05-24T04:33:00Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-24T03:46:44Z	--- 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).
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep3_42	MinaMila	2025-05-24T04:31:44Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T04:31:40Z	--- 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]
TaiFei0/ppo-LunarLander-v2	TaiFei0	2025-05-24T04:30:45Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-05-24T04:30:24Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 247.05 +/- 21.46 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Jack-Payne1/Qwen2.5-1.5B-Instruct-Sleeper-ft1-tiny-stories	Jack-Payne1	2025-05-24T04:30:04Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T04:30:00Z	--- 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. 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]
ScratchThePlan/vanilla-cn-roleplay-0.2	ScratchThePlan	2025-05-24T04:28:19Z	5	8	null	[ "safetensors", "qwen3", "roleplay", "Roleplay", "roleplaying", "zh", "dataset:ScratchThePlan/cn-role-play-we-with-no-tomorrow-fell-in-love-yesterday", "dataset:ScratchThePlan/novel_cn_roleplay_dataset_liars_lips_fall_apart_in_love", "base_model:Qwen/Qwen3-14B", "base_model:finetune:Qwen/Qwen3-14B", "license:apache-2.0", "region:us" ]	null	2025-05-19T16:36:38Z	--- license: apache-2.0 datasets: - ScratchThePlan/cn-role-play-we-with-no-tomorrow-fell-in-love-yesterday - ScratchThePlan/novel_cn_roleplay_dataset_liars_lips_fall_apart_in_love language: - zh base_model: - Qwen/Qwen3-14B tags: - roleplay - Roleplay - roleplaying --- The system prompt should be like(you can check out the dataset to find out how the system prompts look like) 根据以下信息，进行角色扮演，我将扮演男主角，你将扮演女主以及其他角色剧情前提: 你知道零和你注定无法幸福（由于家庭阶级，你的家庭只是普通，零是企业家的独身女），你不希望因为你而耽误了零以后的幸福，你决定今天在七夕节日的集会上宣布与她分手一事…… 女主角特征: 姓名零，拥有惊人的美貌，皮肤白皙，五官精致得如同雕塑，即使在狼狈的状态下也散发着独特的光彩。气质高雅，穿着打扮显示出良好的家境或品味。男女主之间的关系: 互相深爱的情侣，但是因为阶级不同，你认为你和零终究无法在一起男主对女主情感，以及强烈程度: <喜欢:10>，<无奈：10>，<悲伤：8> 女主对男主的情感，以及强烈程度: <喜欢:9>,<悲伤>，<痛苦：10> # sillytavern first message: 夜幕低垂，七夕的集会却热闹非凡。无数彩灯将街道映照得如同白昼，空气中弥漫着甜食的香气和人们的欢声笑语。小贩的叫卖声、情侣间的低语、孩子们追逐嬉戏的喧闹，交织成一曲属于这个浪漫节日的乐章。在熙攘的人群中，一个身影格外引人注目。那就是零。她今天穿了一件淡雅的改良旗袍，浅金色的丝线在月光与灯火下绣出精致的鹊鸟登枝图案，衬得她本就白皙的肌肤更加莹润如玉。乌黑的长发被一根简单的玉簪松松挽起，几缕调皮的发丝垂在颊边，随着她轻微的动作微微晃动。她的脸上带着浅浅的笑意，那双平日里略显清冷的凤眸，此刻也因节日的氛围和对你的期待而染上了几分温柔的暖光。她站在一棵挂满了许愿红绳的古树下，不时望向路口，手中还提着一个小巧的、似乎是为你准备的礼物锦盒。尽管周围的一切都充满了喜悦，你的心情却与这节日格格不入。你知道，今晚之后，你将亲手打碎这份美好，打碎她眼中那纯粹的期待。零的美丽与她此刻无忧的浅笑，都像是一把把尖刀，刺痛着你的心。她并不知道，等待她的，将会是怎样残酷的言语。 # demonstrated image: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6435449e876d46610bef3235/QJ4-s1nYZJ3XSicJBqFI_.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6435449e876d46610bef3235/qxXpYJHmfYXCeTvyRs5Jv.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6435449e876d46610bef3235/zDpIywV5f2hhRINTLLGDk.png) The hyperparameters you should start with ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6435449e876d46610bef3235/g2iI_yYJQIGmujMJQGRQ7.png) If you find the <think> tags in the AI response, use sillytavern regex to remove them. /[`\s][\[\<]think[\>\]](.?)[\[\<]\/think[\>\]][`\s]\|^[`\s]([\[\<]thinking[\>\]][`\s].)$/ims ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6435449e876d46610bef3235/odQHfsFGbsbLUKMe32XXu.png)
duydc/qwen-2.5-7b-alpaca-instruct-2452025-ver6	duydc	2025-05-24T04:27:49Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-24T04:02:20Z	--- base_model: Qwen/Qwen2.5-7B-Instruct library_name: transformers model_name: qwen-2.5-7b-alpaca-instruct-2452025-ver6 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for qwen-2.5-7b-alpaca-instruct-2452025-ver6 This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-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="duydc/qwen-2.5-7b-alpaca-instruct-2452025-ver6", 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/duydc/huggingface/runs/epe4jp4h) This model was trained with SFT. ### Framework versions - TRL: 0.12.1 - Transformers: 4.46.3 - Pytorch: 2.4.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## 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}} } ```
chloebrandon/t5_amh_finetuned	chloebrandon	2025-05-24T04:25:49Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-24T04:25:39Z	--- library_name: transformers license: apache-2.0 base_model: t5-small tags: - generated_from_trainer model-index: - name: t5_amh_finetuned 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. --> # t5_amh_finetuned This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep1_42	MinaMila	2025-05-24T04:24:51Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T04:24:47Z	--- 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. 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]
Sonujnv/773788	Sonujnv	2025-05-24T04:22:15Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-24T04:22:15Z	--- license: apache-2.0 ---
vermoney/45ca3b98-1d9b-40da-84ea-0f226bb3e5d3	vermoney	2025-05-24T04:22:08Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:unsloth/Qwen2.5-14B", "base_model:quantized:unsloth/Qwen2.5-14B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-24T04:00:03Z	--- base_model: unsloth/Qwen2.5-14B library_name: transformers model_name: 45ca3b98-1d9b-40da-84ea-0f226bb3e5d3 tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for 45ca3b98-1d9b-40da-84ea-0f226bb3e5d3 This model is a fine-tuned version of [unsloth/Qwen2.5-14B](https://huggingface.co/unsloth/Qwen2.5-14B). 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="vermoney/45ca3b98-1d9b-40da-84ea-0f226bb3e5d3", 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/s56-9/runs/hg8haq6x) 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}} } ```
xuan-luo/MTPQwen3-8B-T1234-Eagle-nar-id8	xuan-luo	2025-05-24T04:21:40Z	0	0	transformers	[ "transformers", "safetensors", "mtpqwen3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	text-generation	2025-05-23T18:31:00Z	--- 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. 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]
dimasik87/ce64ffea-de12-498f-b4f9-184d015fad71	dimasik87	2025-05-24T04:19:18Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:unsloth/Qwen2.5-14B", "base_model:quantized:unsloth/Qwen2.5-14B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-24T04:00:09Z	--- base_model: unsloth/Qwen2.5-14B library_name: transformers model_name: ce64ffea-de12-498f-b4f9-184d015fad71 tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for ce64ffea-de12-498f-b4f9-184d015fad71 This model is a fine-tuned version of [unsloth/Qwen2.5-14B](https://huggingface.co/unsloth/Qwen2.5-14B). 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/ce64ffea-de12-498f-b4f9-184d015fad71", 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/xxcvui6g) 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}} } ```
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep9_33	MinaMila	2025-05-24T04:17:49Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T04:17:45Z	--- 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. 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]
MustakimPallab/wav2vec2-large-xlsr-bangla-common_voice_2	MustakimPallab	2025-05-24T04:15:28Z	0	0	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-21T12:25:25Z	--- 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. 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]
SCH0/cardio-llama3e-finetuned	SCH0	2025-05-24T04:12:57Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-24T04:12:47Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: SCH0 - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
w6666/models	w6666	2025-05-24T04:11:12Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:arrow", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-23T15:42:19Z	--- library_name: transformers tags: - generated_from_trainer datasets: - arrow metrics: - accuracy - f1 model-index: - name: models results: - task: name: Text Classification type: text-classification dataset: name: arrow type: arrow config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.936 - name: F1 type: f1 value: 0.9359388100064484 --- <!-- 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. --> # models This model was trained from scratch on the arrow dataset. It achieves the following results on the evaluation set: - Loss: 0.2801 - Accuracy: 0.936 - F1: 0.9359 ## 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: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.4034 \| 1.0 \| 1000 \| 0.2211 \| 0.921 \| 0.9218 \| \| 0.1681 \| 2.0 \| 2000 \| 0.1970 \| 0.93 \| 0.9288 \| \| 0.1171 \| 3.0 \| 3000 \| 0.1928 \| 0.9375 \| 0.9373 \| \| 0.0807 \| 4.0 \| 4000 \| 0.2077 \| 0.936 \| 0.9363 \| \| 0.0446 \| 5.0 \| 5000 \| 0.2801 \| 0.936 \| 0.9359 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.7.0+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
DAKARA555/hipopen	DAKARA555	2025-05-24T04:09:29Z	3	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:Wan-AI/Wan2.1-I2V-14B-480P", "base_model:adapter:Wan-AI/Wan2.1-I2V-14B-480P", "license:apache-2.0", "region:us" ]	text-to-image	2025-05-22T16:16:56Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/white.png base_model: Wan-AI/Wan2.1-I2V-14B-480P instance_prompt: null license: apache-2.0 --- # hipopen <Gallery /> ## Model description https://civitai.com/models/1587277/ass-stretchgrab-wan-21-i2v-480p?modelVersionId=1796171 https://huggingface.co/DAKARA555/hipopen/resolve/main/ass_spread_i2v_480p.safetensors?download=true ## Download model Weights for this model are available in Safetensors format. [Download](/DAKARA555/hipopen/tree/main) them in the Files & versions tab.
mukomana/ppo-Huggy	mukomana	2025-05-24T04:09:22Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2025-05-24T04:09:15Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: mukomana/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
thejaminator/number-4e-05-qwen3_32b-epochs1	thejaminator	2025-05-24T04:08:20Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-32B", "base_model:finetune:unsloth/Qwen3-32B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-24T04:07:33Z	--- base_model: unsloth/Qwen3-32B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: thejaminator - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-32B 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)
DMindAI/DMind-1	DMindAI	2025-05-24T04:06:22Z	72	16	transformers	[ "transformers", "safetensors", "blockchain", "conversational", "web3", "qwen3", "text-generation", "en", "zh", "base_model:Qwen/Qwen3-32B", "base_model:finetune:Qwen/Qwen3-32B", "license:mit", "endpoints_compatible", "region:us" ]	text-generation	2025-05-14T11:07:03Z	--- license: mit language: - en - zh metrics: - accuracy base_model: - Qwen/Qwen3-32B pipeline_tag: text-generation library_name: transformers tags: - blockchain - conversational - web3 - qwen3 # eval_results: # - task: domain-specific evaluation # dataset: DMindAI/DMind_Benchmark # metric: normalized web3 score # score: 77.44 # model: DMind-1 # model_rank: 1 / 24 --- <p align="center"> <img src="figures/dmind-ai-logo.png" width="300" alt="DMind Logo" /> </p> <hr> <div align="center" style="line-height: 1;"> <a href="https://dmind.ai/" target="_blank" style="margin: 2px;"> <img alt="DMind Website" src="https://img.shields.io/badge/DMind-Homepage-blue?logo=data:image/svg+xml;base64,)" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://huggingface.co/DMindAI" target="_blank" style="margin: 2px;"> <img alt="Hugging Face" src="https://img.shields.io/badge/HuggingFace-DMind-ffd21f?color=ffd21f&logo=huggingface" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://x.com/dmind_ai" target="_blank" style="margin: 2px;"> <img alt="X" src="https://img.shields.io/badge/X-@DMind-1DA1F2?logo=x" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://huggingface.co/spaces/DMindAI/DMind-1" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DMind--1-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://discord.gg/xxwmPHU3" target="_blank" style="margin: 2px;"> <img alt="Discord" src="https://img.shields.io/badge/Discord-DMind-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://opensource.org/licenses/MIT" target="_blank" style="margin: 2px;"> <img alt="Code License: MIT" src="https://img.shields.io/badge/Code%20License-MIT-yellow.svg" style="display: inline-block; vertical-align: middle;"/> </a> </div> ## Table of Contents - [Introduction](#introduction) - [1. Model Overview](#1-model-overview) - [2. Evaluation Results](#2-evaluation-results) - [3. Use Cases](#3-use-cases) - [4. Quickstart](#4-quickstart) - [4.1 Model Downloads](#41-model-downloads) - [4.2 OpenRouter API](#42-openrouter-api) - [4.3 OpenRouter Web Chat](#43-openrouter-web-chat) - [License](#license) - [Contact](#contact) ## Introduction The rapid growth of Web3 technologies—blockchain, DeFi, and smart contracts—demands specialized AI large language models (LLMs) with precise domain alignment and advanced reasoning capabilities. However, General-purpose LLMs often lack the domain-specific accuracy, nuanced reasoning, and instruction-following aligned with expert expectations. To address these limitations, we introduce DMind-1, a domain-specialized LLM fine-tuned for the Web3 ecosystem via supervised instruction tuning and reinforcement learning from human feedback (RLHF). Built on a powerful base model, DMind-1 achieves strong improvements in task accuracy, content safety, and expert-aligned interaction, significantly surpassing general-purpose models. DMind-1 represents a robust foundation for intelligent agents in the Web3 ecosystem. ## 1. Model Overview ### DMind-1 DMind-1 is a specialized Web3 expert model built on the Qwen3-32B base. Leveraging a state-of-the-art transformer architecture, it integrates deep domain knowledge through a novel two-stage fine-tuning pipeline, establishing its distinctive strengths in Web3-specific applications. Key Points: - Comprehensive Domain Expertise Data: In the first stage, DMind-1 underwent Supervised Fine-Tuning (SFT) on 13,276 expert-curated knowledge items distilled from 32.7GB of Web3 documentation, covering 8 key subdomains including DeFi, tokenomics, governance, and smart contracts. These data points were extracted and structured by a team of domain experts to ensure both depth and accuracy. To enable efficient and scalable training, we employed Low-Rank Adaptation (LoRA) during the SFT stage, allowing DMind-1 to internalize specialized Web3 knowledge while preserving the general-language capabilities of its base model. - Reinforcement Learning from Human Feedback (RLHF) To further align the model with expert expectations in realistic interaction scenarios and accuracy, we implemented an RLHF phase composed of: - Reward Model Training: We trained a domain-specific reward model using preference-ranked outputs collected from human experts across diverse Web3-specific question-answer and interaction scenarios. This model learned to assess which responses best reflect factual accuracy and expert-level reasoning in the Web3 domain. - Policy Optimization with PPO: Building on the SFT model, we fine-tuned Qwen3-32B using Proximal Policy Optimization (PPO), guided by the trained reward model. The policy network was optimized based on feedback from simulated Web3 dialogue environments, while LoRA ensured resource-efficient parameter updates and significantly reduced compute and memory requirements. This dual-stage approach enabled efficient fine-tuning of a larger model on Web3-specific tasks while achieving high alignment with human intent. - Domain-Aligned Reasoning and Interaction: DMind-1 exhibits advanced web3-aligned reasoning and interactive capabilities in the following fields: - Natural Dialogue Fluency: Coherent, context-aware conversations on complex Web3 topics, with strong multi-turn consistency. - Complex Instruction Following: Reliable execution of multi-step instructions and conditional logic, supporting agent-driven workflows. - Safe and Compliant Content Generation: Outputs are aligned with domain-specific safety, ethics, and regulatory standards. ## 2. Evaluation Results ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6417e25e058f65de43201023/ESu1U3b9upbwZ70w5CCb9.png) We evaluate DMind-1 and DMind-1-mini using the [DMind Benchmark](https://huggingface.co/datasets/DMindAI/DMind_Benchmark), a domain-specific evaluation suite designed to assess large language models in the Web3 context. The benchmark includes 1,917 expert-reviewed questions across nine core domain categories, and it features both multiple-choice and open-ended tasks to measure factual knowledge, contextual reasoning, and other abilities. To complement accuracy metrics, we conducted a cost-performance analysis by comparing benchmark scores against publicly available input token prices across 24 leading LLMs. In this evaluation: - DMind-1 achieved the highest Web3 score while maintaining one of the lowest token input costs among top-tier models such as Grok 3 and Claude 3.7 Sonnet. - DMind-1-mini ranked second, retaining over 95% of DMind-1’s performance with greater efficiency in latency and compute. Both models are uniquely positioned in the most favorable region of the score vs. price curve, delivering state-of-the-art Web3 reasoning at significantly lower cost. This balance of quality and efficiency makes the DMind models highly competitive for both research and production use. ## 3. Use Cases - Expert-Level Question & Answering: Provides accurate, context-aware answers on blockchain, DeFi, smart contracts, and related Web3 topics. - Compliance-Aware Support: Assists in drafting or reviewing content within regulatory and legal contexts. - Content Generation in Domain: Produces Web3-specific blog posts, documentation, and tutorials tailored to developers and users. - DeFi Strategy Suggestions: Generates insights and recommendations for yield farming, liquidity provision, and portfolio strategies based on user-provided data. - Risk Management: Suggests strategies aligned with user risk profiles for more informed decision-making in volatile markets. ## 4. Quickstart ### 4.1 Model Downloads \| Model \| Base Model \| Download \| \|:--------------:\|:--------------:\|:----------------------------------------------------------------------------:\| \| DMind-1 \| Qwen3-32B \| [Hugging Face Link](https://huggingface.co/DMindAI/DMind-1) \| \| DMind-1-mini \| Qwen3-14B \| [Hugging Face Link](https://huggingface.co/DMindAI/DMind-1-mini) \| ### 4.2 OpenRouter API (Coming Soon) Documentation for API access will be available soon. ### 4.3 OpenRouter Web Chat (Coming Soon) Web chat interface documentation will be available soon. ## License - The code repository and model weights for DMind-1 is released under the MIT License. - Commercial use, modification, and derivative works (including distillation and fine-tuning) are permitted. - Base Models: - DMind-1 is derived from Qwen3-32B, originally licensed under the [Qwen License](https://github.com/QwenLM/Qwen3). - Please ensure compliance with the original base model licenses when using or distributing derivatives. ## Contact For questions or support, please contact [email protected]

Subsets and Splits