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train · 835k rows

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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.15_0.25_epoch1	MinaMila	2025-06-16T06:13:24Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T06:11:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
Nimra-Mehra-Viral-Video123/NEW.VIDEO.Nirma.Meena.Viral.Video.Link.FULL.HD	Nimra-Mehra-Viral-Video123	2025-06-16T06:11:41Z	0	0	null	[ "region:us" ]	null	2025-06-16T06:10:33Z	[![My Image](https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif)](https://tinyurl.com/4va3nzzc)
Mezzo-Fun-Official-Viral-Video/Full.VIDEO.Mizo.Fun.Viral.Video.Tutorial.Official	Mezzo-Fun-Official-Viral-Video	2025-06-16T06:11:31Z	0	0	null	[ "region:us" ]	null	2025-06-16T06:11:15Z	<a href="https://t.co/98E3uGhPfJ" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
enpeizhao/qwen2_5-3b-instruct-trl-sft-all-in-one-8	enpeizhao	2025-06-16T06:10:40Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-VL-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-06-16T05:01:33Z	--- base_model: Qwen/Qwen2.5-VL-3B-Instruct library_name: transformers model_name: qwen2_5-3b-instruct-trl-sft-all-in-one-8 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for qwen2_5-3b-instruct-trl-sft-all-in-one-8 This model is a fine-tuned version of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-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="enpeizhao/qwen2_5-3b-instruct-trl-sft-all-in-one-8", 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/my-pred-team/enpeizhao_qwen2_5-3b-instruct-trl-sft-all-in-one-8/runs/xx787ryb) This model was trained with SFT. ### Framework versions - TRL: 0.19.0.dev0 - Transformers: 4.53.0.dev0 - Pytorch: 2.4.1+cu121 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
BootesVoid/cmbyjnk1403xvrdqsg2kyovgu_cmbykq3fx03zcrdqse4makkvd	BootesVoid	2025-06-16T06:09:54Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-16T06:09:52Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: EMILY --- # Cmbyjnk1403Xvrdqsg2Kyovgu_Cmbykq3Fx03Zcrdqse4Makkvd <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `EMILY` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "EMILY", "lora_weights": "https://huggingface.co/BootesVoid/cmbyjnk1403xvrdqsg2kyovgu_cmbykq3fx03zcrdqse4makkvd/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmbyjnk1403xvrdqsg2kyovgu_cmbykq3fx03zcrdqse4makkvd', weight_name='lora.safetensors') image = pipeline('EMILY').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmbyjnk1403xvrdqsg2kyovgu_cmbykq3fx03zcrdqse4makkvd/discussions) to add images that show off what you’ve made with this LoRA.
VIDEO-LINK-Nirma-Meena-Viral-Leaks/18.VIDEO.LINK.Nirma.Meena.Viral.Video.Leaks.Official	VIDEO-LINK-Nirma-Meena-Viral-Leaks	2025-06-16T06:05:27Z	0	0	null	[ "region:us" ]	null	2025-06-16T06:04:49Z	<animated-image data-catalyst=""><a href="https://sexleakedviral.com/new-leaked-video/?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
parveen-bilasipara-vodeo/vid.parveen.bilasipara.viral.video.link.on.social.media	parveen-bilasipara-vodeo	2025-06-16T06:04:42Z	0	0	null	[ "region:us" ]	null	2025-06-16T06:03:37Z	Parveen Bilasipara Video New collections of Parveen Bilasipara Video now being a creator on Fanfix uploading adult contents <a href="https://tinyurl.com/2urtu5zm"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Nature" class="responsive"></a>
parveen-bilasipara/parveen.bilasipara.viral.video.link.on.social.media	parveen-bilasipara	2025-06-16T06:04:39Z	0	0	null	[ "region:us" ]	null	2025-06-16T06:03:17Z	Parveen Bilasipara Video New collections of Parveen Bilasipara Video now being a creator on Fanfix uploading adult contents <a href="https://tinyurl.com/2urtu5zm"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Nature" class="responsive"></a>
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.5_0.25_0.5_epoch2	MinaMila	2025-06-16T06:04:32Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T06:02:44Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
K10S/mistral-student-finetune_checkpoint150	K10S	2025-06-16T06:03:55Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1", "region:us" ]	null	2025-06-16T06:03:51Z	--- base_model: mistralai/Mistral-7B-Instruct-v0.1 library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
Ryanz48/RyanzRVCModels	Ryanz48	2025-06-16T06:03:20Z	0	0	null	[ "license:other", "region:us" ]	null	2023-06-22T12:10:32Z	--- license: other --- The models included in this repo are: Blur (Italian Twitch Streamer) (RVC v2) 300 Epochs Caparezza (2000s, Nasal Voice) (RVC v2) 300 Epochs Jeff Buckley (RVC v1) 500 Epochs Matt Bellamy (from Muse) (RVC v2) 300 Epochs Serj Tankian (Post-SOAD Era) (RVC v2) 500 Epochs Serj Tankian (Toxicity Era) (RVC v1) 1000 Epochs Tiziano Ferro (Italian Singer) (RVC v2) 300 Epochs
K10S/mistral-student-finetune	K10S	2025-06-16T06:02:03Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1", "region:us" ]	null	2025-06-16T06:01:58Z	--- base_model: mistralai/Mistral-7B-Instruct-v0.1 library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
parveen-bilasipara-viral-video/Original.18.parveen.viral.video.on.social.media	parveen-bilasipara-viral-video	2025-06-16T06:00:19Z	0	0	null	[ "region:us" ]	null	2025-06-16T05:44:46Z	<a rel="nofollow" href="https://tinyurl.com/2urtu5zm">🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶 L𝚎aᴋed Video V𝐢ral Video</a> <a href="https://tinyurl.com/2urtu5zm"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Nature" class="responsive"></a>
japat123/mistral_jun16_1	japat123	2025-06-16T05:58:46Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "en", "base_model:unsloth/mistral-7b-bnb-4bit", "base_model:quantized:unsloth/mistral-7b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-16T05:58:11Z	--- base_model: unsloth/mistral-7b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mistral license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: japat123 - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
WeiDai-David/GGEUR_ViT32	WeiDai-David	2025-06-16T05:58:44Z	0	0	open_clip	[ "open_clip", "license:apache-2.0", "region:us" ]	null	2025-06-16T05:47:57Z	--- license: apache-2.0 ---
01PrathamS/text2sql_finetune	01PrathamS	2025-06-16T05:56:40Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:microsoft/Phi-3-mini-4k-instruct", "base_model:finetune:microsoft/Phi-3-mini-4k-instruct", "endpoints_compatible", "region:us" ]	null	2025-06-16T05:56:35Z	--- base_model: microsoft/Phi-3-mini-4k-instruct library_name: transformers model_name: text2sql_finetune tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for text2sql_finetune This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-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="01PrathamS/text2sql_finetune", 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: 0.12.1 - Transformers: 4.46.2 - Pytorch: 2.6.0+cu124 - 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}} } ```
Nirma-Meena-Full-Video/Full-Viral.Nirma.Nirma.Meena.Viral.Video.lady	Nirma-Meena-Full-Video	2025-06-16T05:55:49Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-16T05:55:04Z	--- license: apache-2.0 --- [![My Image](https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif)](https://bit.ly/4lb0YGM)
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.15_0.75_epoch2	MinaMila	2025-06-16T05:52:51Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T05:51:03Z	--- 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]
Renugadevi82/cisco-nx-ai-4bit	Renugadevi82	2025-06-16T05:51:37Z	0	0	null	[ "safetensors", "llama", "cisco", "networking", "tinyllama", "4bit", "quantized", "en", "license:apache-2.0", "region:us" ]	null	2025-06-15T13:37:45Z	--- tags: - cisco - networking - llama - tinyllama - 4bit - quantized license: apache-2.0 language: en --- # Cisco Network Configuration Model (4-bit Quantized) ## Usage with 4-bit Quantization ```python from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig import torch bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16 ) model = AutoModelForCausalLM.from_pretrained( "Renugadevi82/cisco-nx-ai-4bit", quantization_config=bnb_config, device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("Renugadevi82/cisco-nx-ai-4bit") ``` ## Memory Requirements - 4-bit: ~0.8GB VRAM - 16-bit: ~2.5GB VRAM
j1a0m0e7s/LID_DryPond	j1a0m0e7s	2025-06-16T05:49:15Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-08T15:19:11Z	--- license: apache-2.0 ---
sergioalves/26f3c459-1dc9-4d0d-b907-7258ee195a89	sergioalves	2025-06-16T05:48:40Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM2-1.7B", "base_model:adapter:unsloth/SmolLM2-1.7B", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-06-16T05:23:29Z	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM2-1.7B tags: - axolotl - generated_from_trainer model-index: - name: 26f3c459-1dc9-4d0d-b907-7258ee195a89 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/SmolLM2-1.7B bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 374958181cb5f0a5_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: 4 gradient_checkpointing: true gradient_clipping: 0.8 group_by_length: false hub_model_id: sergioalves/26f3c459-1dc9-4d0d-b907-7258ee195a89 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-07 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 32 lora_dropout: 0.3 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_steps: 300 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/374958181cb5f0a5_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: 3f11e093-22a6-4174-9a7a-02e2857fdaec wandb_project: s56-7 wandb_run: your_name wandb_runid: 3f11e093-22a6-4174-9a7a-02e2857fdaec warmup_steps: 30 weight_decay: 0.05 xformers_attention: true ``` </details><br> # 26f3c459-1dc9-4d0d-b907-7258ee195a89 This model is a fine-tuned version of [unsloth/SmolLM2-1.7B](https://huggingface.co/unsloth/SmolLM2-1.7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8659 ## 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-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - 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: cosine - lr_scheduler_warmup_steps: 30 - training_steps: 300 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.8987 \| 0.0004 \| 1 \| 1.8671 \| \| 1.6803 \| 0.0561 \| 150 \| 1.8663 \| \| 1.6321 \| 0.1123 \| 300 \| 1.8659 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Renugadevi82/cisco-nx-ai-lora	Renugadevi82	2025-06-16T05:48:11Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T13:36:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.15_0.75_epoch1	MinaMila	2025-06-16T05:46:22Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T05:44:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Triangle104/Dolphin-Mistral-24B-Venice-Edition-Q3_K_M-GGUF	Triangle104	2025-06-16T05:46:15Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition", "base_model:quantized:cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-16T05:17:34Z	--- license: apache-2.0 base_model: cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition tags: - llama-cpp - gguf-my-repo --- # Triangle104/Dolphin-Mistral-24B-Venice-Edition-Q3_K_M-GGUF This model was converted to GGUF format from [`cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition`](https://huggingface.co/cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition) for more details on the model. --- Dolphin Mistral 24B Venice Edition is a collaborative project we undertook with Venice.ai with the goal of creating the most uncensored version of Mistral 24B for use within the Venice ecosystem. Dolphin Mistral 24B Venice Edition is now live on https://venice.ai/ as “Venice Uncensored,” the new default model for all Venice users. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Dolphin-Mistral-24B-Venice-Edition-Q3_K_M-GGUF --hf-file dolphin-mistral-24b-venice-edition-q3_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Dolphin-Mistral-24B-Venice-Edition-Q3_K_M-GGUF --hf-file dolphin-mistral-24b-venice-edition-q3_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Dolphin-Mistral-24B-Venice-Edition-Q3_K_M-GGUF --hf-file dolphin-mistral-24b-venice-edition-q3_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Dolphin-Mistral-24B-Venice-Edition-Q3_K_M-GGUF --hf-file dolphin-mistral-24b-venice-edition-q3_k_m.gguf -c 2048 ```
TheDrummer/Agatha-111B-v1	TheDrummer	2025-06-16T05:44:30Z	68	12	null	[ "safetensors", "cohere2", "base_model:CohereLabs/c4ai-command-a-03-2025", "base_model:finetune:CohereLabs/c4ai-command-a-03-2025", "region:us" ]	null	2025-06-12T07:38:51Z	--- base_model: - CohereLabs/c4ai-command-a-03-2025 --- # Join our Discord! https://discord.gg/BeaverAI ## More than 6000 members of helpful, LLM enthusiasts! A hub for players and makers alike! ### We need testers! --- Drummer proudly presents... # Agatha 111B v1 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f2fd1c25b848bd061b5c2e/p82Z3S-ooW4EMcJrNjYA1.png) ## Special Thanks - Thank you Geechan for unblocking model development for Command A and taking the lead! - Thank you to the testers at BeaverAI! You da MVP! - Thank you to each and everyone who donated and subscribed in [Patreon](https://www.patreon.com/TheDrummer) and [Ko-Fi](https://ko-fi.com/thedrummer) to make our venture a little bit easier. - [Subscribe to my Patreon!](https://www.patreon.com/TheDrummer) ## Usage - Command R / Command A / Cohere Template ## Links - Original: https://huggingface.co/TheDrummer/Agatha-111B-v1 - GGUF: https://huggingface.co/TheDrummer/Agatha-111B-v1-GGUF - iMatrix (recommended): https://huggingface.co/bartowski/TheDrummer_Agatha-111B-v1-GGUF `config-v1h`
s-emanuilov/Tucan-9B-v1.0	s-emanuilov	2025-06-16T05:43:17Z	57	1	null	[ "safetensors", "gemma2", "function_calling", "MCP", "tool_use", "bg", "arxiv:2503.23278", "arxiv:2408.00118", "arxiv:2412.10893", "base_model:INSAIT-Institute/BgGPT-Gemma-2-9B-IT-v1.0", "base_model:finetune:INSAIT-Institute/BgGPT-Gemma-2-9B-IT-v1.0", "license:gemma", "region:us" ]	null	2025-06-08T07:22:38Z	--- license: gemma language: - bg base_model: - INSAIT-Institute/BgGPT-Gemma-2-9B-IT-v1.0 tags: - function_calling - MCP - tool_use --- # Tucan-9B-v1.0 ## Bulgarian Language Models for Function Calling 🇧🇬 > 📄 Full methodology, dataset details, and evaluation results coming in the upcoming paper ## Overview 🚀 TUCAN (Tool-Using Capable Assistant Navigator) is a series of open-source Bulgarian language models fine-tuned specifically for function calling and tool use. These models can interact with external tools, APIs, and databases, making them appropriate for building AI agents and [Model Context Protocol (MCP)](https://arxiv.org/abs/2503.23278) applications. Built on top of [BgGPT models](https://huggingface.co/collections/INSAIT-Institute/bggpt-gemma-2-673b972fe9902749ac90f6fe) from [INSAIT Institute](https://insait.ai/), which were themselves built on [Gemma 2](https://arxiv.org/pdf/2408.00118), Tucan models have been enhanced with function-calling capabilities. ## Motivation 🎯 Although BgGPT models demonstrate [strong Bulgarian language comprehension](https://arxiv.org/pdf/2412.10893), they face challenges in maintaining the precise formatting necessary for consistent function calling. Despite implementing detailed system prompts, their performance in this specific task remains suboptimal. This project addresses that gap by fine-tuning BgGPT, providing the Bulgarian AI community with proper tool-use capabilities in their native language. ## Models and variants 📦 Available in three sizes with full models, LoRA adapters, and quantized GGUF variants: <div align="center"> \| Model Size \| Full Model \| LoRA Adapter \| GGUF (Quantized) \| \|------------\|------------\|--------------\|------------------\| \| 2.6B \| [Tucan-2.6B-v1.0](https://huggingface.co/s-emanuilov/Tucan-2.6B-v1.0)\| [LoRA](https://huggingface.co/s-emanuilov/Tucan-2.6B-v1.0-LoRA) \| [GGUF](https://huggingface.co/s-emanuilov/Tucan-2.6B-v1.0-GGUF) \| \| 9B \| [Tucan-9B-v1.0](https://huggingface.co/s-emanuilov/Tucan-9B-v1.0) 📍\| [LoRA](https://huggingface.co/s-emanuilov/Tucan-9B-v1.0-LoRA) \| [GGUF](https://huggingface.co/s-emanuilov/Tucan-9B-v1.0-GGUF) \| \| 27B \| [Tucan-27B-v1.0](https://huggingface.co/s-emanuilov/Tucan-27B-v1.0) \| [LoRA](https://huggingface.co/s-emanuilov/Tucan-27B-v1.0-LoRA) \| [GGUF](https://huggingface.co/s-emanuilov/Tucan-27B-v1.0-GGUF) \| GGUF variants include: q4_k_m, q5_k_m, q6_k, q8_0, q4_0 quantizations 📍 Current model/repo </div> Models and quantizations are also available for easy use in Ollama: https://ollama.com/s_emanuilov/tucan ## Benchmarks 📊 All evaluations were performed using the [Tucan evaluation framework](https://github.com/s-emanuilov/tucan), with results averaged across multiple runs. Tucan models demonstrate superior function-calling capabilities compared to their BgGPT counterparts, with particularly strong improvements in smaller model sizes. To ensure no catastrophic forgetting occurred, we evaluated knowledge retention using [EleutherAI's lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) on Bulgarian benchmarks, confirming that each Tucan model maintains performance on par with its BgGPT equivalent. <div align="center"> \| Model \| Function Calling \| HellaswagBG \| WinograndeBG \| ARC-Easy-BG \| ARC-Challenge-BG \| \|-------\|-----------------\|-------------\|--------------\|-------------\|------------------\| \| Tucan-2.6B-v1.0 🔥 \| 0.7875 \| 0.5924 \| 0.6456 \| 0.5657 \| 0.3754 \| \| Tucan-9B-v1.0 🔥 \| 0.8667 \| 0.7046 \| 0.7151 \| 0.7024 \| 0.5188 \| \| Tucan-27B-v1.0 🔥 \| 0.875 \| 0.6179 \| 0.6275 \| 0.6486 \| 0.442 \| \| BgGPT-Gemma-2-2.6B-IT-v1.0 \| 0.5874 \| 0.6306 \| 0.5821 \| 0.5657 \| 0.372 \| \| BgGPT-Gemma-2-9B-IT-v1.0 \| 0.7833 \| 0.7057 \| 0.719 \| 0.7231 \| 0.5188 \| \| BgGPT-Gemma-2-27B-IT-v1.0 \| 0.8667 \| 0.62 \| 0.6212 \| 0.6587 \| 0.459 \| Note: 27B models were evaluated in 8-bit precision for comparison purposes. </div> ## Usage 🛠️ ### Quick start ⚡ ```bash pip install -U "transformers[torch]" accelerate bitsandbytes ``` ### Prompt format ⚙️ Critical: Use this format for function calling for the best results. <details> <summary><strong>📋 Required system prompt template</strong></summary> ``` <bos><start_of_turn>user Ти си полезен AI асистент, който предоставя полезни и точни отговори. Имаш достъп и можеш да извикаш една или повече функции, за да помогнеш с потребителското запитване. Използвай ги, само ако е необходимо и подходящо. Когато използваш функция, форматирай извикването ѝ в блок ```tool_call``` на отделен ред, a след това ще получиш резултат от изпълнението в блок ```toll_response```. ## Шаблон за извикване: ```tool_call {"name": <function-name>, "arguments": <args-json-object>}``` ## Налични функции: [your function definitions here] ## Потребителска заявка: [your query in Bulgarian]<end_of_turn> <start_of_turn>model ``` </details> ### Note 📝 The model only generates the `tool_call` blocks with function names and parameters - it doesn't actually execute the functions. Your client application must parse these generated calls, execute the actual functions (API calls, database queries, etc.), and provide the results back to the model in `tool_response` blocks for the conversation to continue the interperation of the results. A full demo is comming soon. ### Python example 🐍 <details> <summary><strong>💻 Complete Working Example</strong></summary> ```python import torch import json from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig # Load model model_name = "s-emanuilov/Tucan-2.6B-v1.0" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map="auto", attn_implementation="eager" # Required for Gemma models ) # Create prompt with system template def create_prompt(functions, user_query): system_prompt = """Ти си полезен AI асистент, който предоставя полезни и точни отговори. Имаш достъп и можеш да извикаш една или повече функции, за да помогнеш с потребителското запитване. Използвай ги, само ако е необходимо и подходящо. Когато използваш функция, форматирай извикването ѝ в блок ```tool_call``` на отделен ред, a след това ще получиш резултат от изпълнението в блок ```toll_response```. ## Шаблон за извикване: ```tool_call {{"name": <function-name>, "arguments": <args-json-object>}}``` """ functions_text = json.dumps(functions, ensure_ascii=False, indent=2) full_prompt = f"{system_prompt}\n## Налични функции:\n{functions_text}\n\n## Потребителска заявка:\n{user_query}" chat = [{"role": "user", "content": full_prompt}] return tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) # Example usage functions = [{ "name": "create_calendar_event", "description": "Creates a new event in Google Calendar.", "parameters": { "type": "object", "properties": { "title": {"type": "string"}, "date": {"type": "string"}, "start_time": {"type": "string"}, "end_time": {"type": "string"} }, "required": ["title", "date", "start_time", "end_time"] } }] query = "Създай събитие 'Годишен преглед' за 8-ми юни 2025 от 14:00 до 14:30." # Generate response prompt = create_prompt(functions, query) inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate( inputs, max_new_tokens=2048, temperature=0.1, top_k=25, top_p=1.0, repetition_penalty=1.1, do_sample=True, eos_token_id=[tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<end_of_turn>")], pad_token_id=tokenizer.eos_token_id ) result = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True) print(result) ``` </details> ## Performance & Dataset 📊 > 📄 Full methodology, dataset details, and comprehensive evaluation results coming in the upcoming paper Dataset: 10,000+ bilingual (Bulgarian/English) function-calling examples across 1,000+ topics, including tool calls with single/multiple arguments, optional parameters, follow-up queries, multi-tool selection, ambiguous queries requiring clarification, and conversational interactions without tool use. Data sourced from manual curation and synthetic generation (Gemini Pro 2.5/GPT-4.1/Sonnet 4). Results: Significant improvements in tool-use capabilities over base BgGPT models: 34.1% for 2.6B, 10.6% for 9B, and 1.0% for 27B models in [internal benchmarks](https://github.com/s-emanuilov/tucan). Beyond raw function-calling scores, all Tucan models demonstrate more natural conversational flow while maintaining tool-use capabilities, retaining their base knowledge. ## Acknowledgments 🙏 Built on top of [BgGPT series](https://huggingface.co/collections/INSAIT-Institute/bggpt-gemma-2-673b972fe9902749ac90f6fe). ## Questions & Contact 💬 For questions, collaboration, or feedback: [Connect on LinkedIn](https://www.linkedin.com/in/simeon-emanuilov/)**
enoubi/XLM-RoBERTa-Twitter-Indonesian-Sarcastic-Few-Shot	enoubi	2025-06-16T05:43:16Z	250	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-11T04:37:59Z	--- library_name: transformers license: mit base_model: xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: XLM-RoBERTa-Twitter-Indonesian-Sarcastic-Few-Shot 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. --> # XLM-RoBERTa-Twitter-Indonesian-Sarcastic-Few-Shot This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3513 - Accuracy: 0.8717 - F1: 0.7677 - Precision: 0.6994 - Recall: 0.8507 ## 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: 32 - eval_batch_size: 64 - 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: cosine - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.5833 \| 1.0 \| 31 \| 0.5356 \| 0.75 \| 0.0 \| 0.0 \| 0.0 \| \| 0.526 \| 2.0 \| 62 \| 0.4851 \| 0.75 \| 0.0 \| 0.0 \| 0.0 \| \| 0.4795 \| 3.0 \| 93 \| 0.4745 \| 0.7724 \| 0.1644 \| 1.0 \| 0.0896 \| \| 0.3989 \| 4.0 \| 124 \| 0.3300 \| 0.8657 \| 0.6667 \| 0.8780 \| 0.5373 \| \| 0.2827 \| 5.0 \| 155 \| 0.3112 \| 0.8657 \| 0.7391 \| 0.7183 \| 0.7612 \| \| 0.2006 \| 6.0 \| 186 \| 0.2641 \| 0.8955 \| 0.7705 \| 0.8545 \| 0.7015 \| \| 0.1357 \| 7.0 \| 217 \| 0.3315 \| 0.8881 \| 0.7917 \| 0.7403 \| 0.8507 \| \| 0.1251 \| 8.0 \| 248 \| 0.4118 \| 0.8433 \| 0.7308 \| 0.6404 \| 0.8507 \| \| 0.0643 \| 9.0 \| 279 \| 0.4539 \| 0.8918 \| 0.7642 \| 0.8393 \| 0.7015 \| \| 0.046 \| 10.0 \| 310 \| 0.5066 \| 0.8694 \| 0.7518 \| 0.7162 \| 0.7910 \| ### Framework versions - Transformers 4.51.1 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.21.0
Sah-Sapna-Kumari-Viral-Video/NEW.VIDEO.Sah.Sapna.Kumari.Viral.Video.Link.Download	Sah-Sapna-Kumari-Viral-Video	2025-06-16T05:42:00Z	0	0	null	[ "region:us" ]	null	2025-06-16T05:41:41Z	[![WATCH LIVE](https://i.imgur.com/AgNJ672.gif)](https://tinyurl.com/updateon365)
alibabasglab/a_tflocoformer	alibabasglab	2025-06-16T05:41:54Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-16T05:29:11Z	--- license: apache-2.0 ---
Nirma-Meena-Full-Video/TRENDING_Top.Nirma.Meena.Official.Viral.Video	Nirma-Meena-Full-Video	2025-06-16T05:41:53Z	0	0	null	[ "region:us" ]	null	2025-06-16T05:41:38Z	[![My Image](https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif)](https://bit.ly/4lb0YGM)
New-parveen-virals/Video.parveen.viral.video.bilasipara.new.video.parbin.bilasipara.viral.video.link	New-parveen-virals	2025-06-16T05:41:02Z	0	0	null	[ "region:us" ]	null	2025-06-16T05:36:26Z	<a rel="nofollow" href="https://tinyurl.com/2urtu5zm">🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶 L𝚎aᴋed Video V𝐢ral Video</a> <a href="https://tinyurl.com/2urtu5zm"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Nature" class="responsive"></a>
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.5_0.25_0.75_epoch1	MinaMila	2025-06-16T05:40:36Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T05:38:47Z	--- 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. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
kleverer/natix-009	kleverer	2025-06-16T05:38:30Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-06-16T05:38:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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IoanaLiviaPopescu/real-data-synth-data-1200-1-Emil-Neural-whisper-small	IoanaLiviaPopescu	2025-06-16T05:38:15Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ro", "dataset:IoanaLivia/RealVoiceSynthVoice-1200-1-Emil-Neural", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-06-16T04:31:44Z	--- library_name: transformers language: - ro license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - IoanaLivia/RealVoiceSynthVoice-1200-1-Emil-Neural metrics: - wer model-index: - name: IoanaLiviaPopescu/ IoanaLiviaPopescu/real-data-synth-data-1200-1-Emil-Neural-whisper-small results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: IoanaLivia/RealVoiceSynthVoice-1200-1-Emil-Neural type: IoanaLivia/RealVoiceSynthVoice-1200-1-Emil-Neural config: default split: test args: 'split: validation' metrics: - name: Wer type: wer value: 16.43002028397566 --- <!-- 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. --> # IoanaLiviaPopescu/ IoanaLiviaPopescu/real-data-synth-data-1200-1-Emil-Neural-whisper-small This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the IoanaLivia/RealVoiceSynthVoice-1200-1-Emil-Neural dataset. It achieves the following results on the evaluation set: - Loss: 0.3690 - Wer: 16.4300 ## 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: 32 - eval_batch_size: 16 - 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: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| No log \| 0 \| 0 \| 0.6024 \| 27.8812 \| \| 0.2883 \| 1.0 \| 51 \| 0.4003 \| 17.5733 \| \| 0.1092 \| 2.0 \| 102 \| 0.3651 \| 17.0570 \| \| 0.0568 \| 3.0 \| 153 \| 0.3690 \| 16.4300 \| \| 0.0331 \| 4.0 \| 204 \| 0.3852 \| 16.6513 \| \| 0.0233 \| 5.0 \| 255 \| 0.3967 \| 17.0754 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
Nirma-Meena-Full-Video/Nirma.Meena.Official.Viral.Video	Nirma-Meena-Full-Video	2025-06-16T05:38:13Z	0	0	null	[ "region:us" ]	null	2025-06-16T05:37:47Z	[![My Image](https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif)](https://bit.ly/4lb0YGM)
yashkalu/deep_learning	yashkalu	2025-06-16T05:37:47Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-16T05:37:47Z	--- license: apache-2.0 ---
kleverer/natix-008	kleverer	2025-06-16T05:36:48Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-06-16T05:36:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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s-emanuilov/Tucan-2.6B-v1.0	s-emanuilov	2025-06-16T05:35:49Z	187	1	null	[ "safetensors", "gemma2", "function_calling", "MCP", "tool_use", "bg", "arxiv:2503.23278", "arxiv:2408.00118", "arxiv:2412.10893", "base_model:INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0", "base_model:finetune:INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0", "license:gemma", "region:us" ]	null	2025-06-07T21:26:39Z	--- license: gemma language: - bg base_model: - INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0 tags: - function_calling - MCP - tool_use --- # Tucan-2.6B-v1.0 ## Bulgarian Language Models for Function Calling 🇧🇬 > 📄 Full methodology, dataset details, and evaluation results coming in the upcoming paper ## Overview 🚀 TUCAN (Tool-Using Capable Assistant Navigator) is a series of open-source Bulgarian language models fine-tuned specifically for function calling and tool use. These models can interact with external tools, APIs, and databases, making them appropriate for building AI agents and [Model Context Protocol (MCP)](https://arxiv.org/abs/2503.23278) applications. Built on top of [BgGPT models](https://huggingface.co/collections/INSAIT-Institute/bggpt-gemma-2-673b972fe9902749ac90f6fe) from [INSAIT Institute](https://insait.ai/), which were themselves built on [Gemma 2](https://arxiv.org/pdf/2408.00118), Tucan models have been enhanced with function-calling capabilities. ## Motivation 🎯 Although BgGPT models demonstrate [strong Bulgarian language comprehension](https://arxiv.org/pdf/2412.10893), they face challenges in maintaining the precise formatting necessary for consistent function calling. Despite implementing detailed system prompts, their performance in this specific task remains suboptimal. This project addresses that gap by fine-tuning BgGPT, providing the Bulgarian AI community with proper tool-use capabilities in their native language. ## Models and variants 📦 Available in three sizes with full models, LoRA adapters, and quantized GGUF variants: <div align="center"> \| Model Size \| Full Model \| LoRA Adapter \| GGUF (Quantized) \| \|------------\|------------\|--------------\|------------------\| \| 2.6B \| [Tucan-2.6B-v1.0](https://huggingface.co/s-emanuilov/Tucan-2.6B-v1.0) 📍\| [LoRA](https://huggingface.co/s-emanuilov/Tucan-2.6B-v1.0-LoRA) \| [GGUF](https://huggingface.co/s-emanuilov/Tucan-2.6B-v1.0-GGUF) \| \| 9B \| [Tucan-9B-v1.0](https://huggingface.co/s-emanuilov/Tucan-9B-v1.0) \| [LoRA](https://huggingface.co/s-emanuilov/Tucan-9B-v1.0-LoRA) \| [GGUF](https://huggingface.co/s-emanuilov/Tucan-9B-v1.0-GGUF) \| \| 27B \| [Tucan-27B-v1.0](https://huggingface.co/s-emanuilov/Tucan-27B-v1.0) \| [LoRA](https://huggingface.co/s-emanuilov/Tucan-27B-v1.0-LoRA) \| [GGUF](https://huggingface.co/s-emanuilov/Tucan-27B-v1.0-GGUF) \| GGUF variants include: q4_k_m, q5_k_m, q6_k, q8_0, q4_0 quantizations 📍 Current model/repo </div> Models and quantizations are also available for easy use in Ollama: https://ollama.com/s_emanuilov/tucan ## Benchmarks 📊 All evaluations were performed using the [Tucan evaluation framework](https://github.com/s-emanuilov/tucan), with results averaged across multiple runs. Tucan models demonstrate superior function-calling capabilities compared to their BgGPT counterparts, with particularly strong improvements in smaller model sizes. To ensure no catastrophic forgetting occurred, we evaluated knowledge retention using [EleutherAI's lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) on Bulgarian benchmarks, confirming that each Tucan model maintains performance on par with its BgGPT equivalent. <div align="center"> \| Model \| Function Calling \| HellaswagBG \| WinograndeBG \| ARC-Easy-BG \| ARC-Challenge-BG \| \|-------\|-----------------\|-------------\|--------------\|-------------\|------------------\| \| Tucan-2.6B-v1.0 🔥 \| 0.7875 \| 0.5924 \| 0.6456 \| 0.5657 \| 0.3754 \| \| Tucan-9B-v1.0 🔥 \| 0.8667 \| 0.7046 \| 0.7151 \| 0.7024 \| 0.5188 \| \| Tucan-27B-v1.0 🔥 \| 0.875 \| 0.6179 \| 0.6275 \| 0.6486 \| 0.442 \| \| BgGPT-Gemma-2-2.6B-IT-v1.0 \| 0.5874 \| 0.6306 \| 0.5821 \| 0.5657 \| 0.372 \| \| BgGPT-Gemma-2-9B-IT-v1.0 \| 0.7833 \| 0.7057 \| 0.719 \| 0.7231 \| 0.5188 \| \| BgGPT-Gemma-2-27B-IT-v1.0 \| 0.8667 \| 0.62 \| 0.6212 \| 0.6587 \| 0.459 \| Note: 27B models were evaluated in 8-bit precision for comparison purposes. </div> ## Usage 🛠️ ### Quick start ⚡ ```bash pip install -U "transformers[torch]" accelerate bitsandbytes ``` ### Prompt format ⚙️ Critical: Use this format for function calling for the best results. <details> <summary><strong>📋 Required system prompt template</strong></summary> ``` <bos><start_of_turn>user Ти си полезен AI асистент, който предоставя полезни и точни отговори. Имаш достъп и можеш да извикаш една или повече функции, за да помогнеш с потребителското запитване. Използвай ги, само ако е необходимо и подходящо. Когато използваш функция, форматирай извикването ѝ в блок ```tool_call``` на отделен ред, a след това ще получиш резултат от изпълнението в блок ```toll_response```. ## Шаблон за извикване: ```tool_call {"name": <function-name>, "arguments": <args-json-object>}``` ## Налични функции: [your function definitions here] ## Потребителска заявка: [your query in Bulgarian]<end_of_turn> <start_of_turn>model ``` </details> ### Note 📝 The model only generates the `tool_call` blocks with function names and parameters - it doesn't actually execute the functions. Your client application must parse these generated calls, execute the actual functions (API calls, database queries, etc.), and provide the results back to the model in `tool_response` blocks for the conversation to continue the interperation of the results. A full demo is comming soon. ### Python example 🐍 <details> <summary><strong>💻 Complete Working Example</strong></summary> ```python import torch import json from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig # Load model model_name = "s-emanuilov/Tucan-2.6B-v1.0" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map="auto", attn_implementation="eager" # Required for Gemma models ) # Create prompt with system template def create_prompt(functions, user_query): system_prompt = """Ти си полезен AI асистент, който предоставя полезни и точни отговори. Имаш достъп и можеш да извикаш една или повече функции, за да помогнеш с потребителското запитване. Използвай ги, само ако е необходимо и подходящо. Когато използваш функция, форматирай извикването ѝ в блок ```tool_call``` на отделен ред, a след това ще получиш резултат от изпълнението в блок ```toll_response```. ## Шаблон за извикване: ```tool_call {{"name": <function-name>, "arguments": <args-json-object>}}``` """ functions_text = json.dumps(functions, ensure_ascii=False, indent=2) full_prompt = f"{system_prompt}\n## Налични функции:\n{functions_text}\n\n## Потребителска заявка:\n{user_query}" chat = [{"role": "user", "content": full_prompt}] return tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) # Example usage functions = [{ "name": "create_calendar_event", "description": "Creates a new event in Google Calendar.", "parameters": { "type": "object", "properties": { "title": {"type": "string"}, "date": {"type": "string"}, "start_time": {"type": "string"}, "end_time": {"type": "string"} }, "required": ["title", "date", "start_time", "end_time"] } }] query = "Създай събитие 'Годишен преглед' за 8-ми юни 2025 от 14:00 до 14:30." # Generate response prompt = create_prompt(functions, query) inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate( inputs, max_new_tokens=2048, temperature=0.1, top_k=25, top_p=1.0, repetition_penalty=1.1, do_sample=True, eos_token_id=[tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<end_of_turn>")], pad_token_id=tokenizer.eos_token_id ) result = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True) print(result) ``` </details> ## Performance & Dataset 📊 > 📄 Full methodology, dataset details, and comprehensive evaluation results coming in the upcoming paper Dataset: 10,000+ bilingual (Bulgarian/English) function-calling examples across 1,000+ topics, including tool calls with single/multiple arguments, optional parameters, follow-up queries, multi-tool selection, ambiguous queries requiring clarification, and conversational interactions without tool use. Data sourced from manual curation and synthetic generation (Gemini Pro 2.5/GPT-4.1/Sonnet 4). Results: Significant improvements in tool-use capabilities over base BgGPT models: 34.1% for 2.6B, 10.6% for 9B, and 1.0% for 27B models in [internal benchmarks](https://github.com/s-emanuilov/tucan). Beyond raw function-calling scores, all Tucan models demonstrate more natural conversational flow while maintaining tool-use capabilities, retaining their base knowledge. ## Acknowledgments 🙏 Built on top of [BgGPT series](https://huggingface.co/collections/INSAIT-Institute/bggpt-gemma-2-673b972fe9902749ac90f6fe). ## Questions & Contact 💬 For questions, collaboration, or feedback: [Connect on LinkedIn](https://www.linkedin.com/in/simeon-emanuilov/)**
viral-othoi-113-video-link/TRENDING.Top.Othoi.Official.Viral.Video	viral-othoi-113-video-link	2025-06-16T05:35:32Z	0	0	null	[ "region:us" ]	null	2025-06-16T05:35:22Z	01 seconds ago [🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶](https://sahabagi-mgi.blogspot.com/p/heres-now.html) [🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶 FREE](https://sahabagi-mgi.blogspot.com/p/heres-now.html) <a href="https://sahabagi-mgi.blogspot.com/p/heres-now.html" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
willeybro/roadwork	willeybro	2025-06-16T05:34:49Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-06-16T05:11:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Nirmit1/lora_bart_base_model	Nirmit1	2025-06-16T05:34:19Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T05:16:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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kleverer/natix-007	kleverer	2025-06-16T05:33:54Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-06-16T05:33:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mlx-community/llm-jp-3.1-13b-instruct4-8bit	mlx-community	2025-06-16T05:32:21Z	0	0	mlx	[ "mlx", "safetensors", "llama", "text-generation", "conversational", "en", "ja", "base_model:llm-jp/llm-jp-3.1-13b-instruct4", "base_model:quantized:llm-jp/llm-jp-3.1-13b-instruct4", "license:apache-2.0", "8-bit", "region:us" ]	text-generation	2025-06-16T05:10:04Z	--- license: apache-2.0 language: - en - ja programming_language: - C - C++ - C# - Go - Java - JavaScript - Lua - PHP - Python - Ruby - Rust - Scala - TypeScript pipeline_tag: text-generation library_name: mlx inference: false base_model: llm-jp/llm-jp-3.1-13b-instruct4 tags: - mlx --- # mlx-community/llm-jp-3.1-13b-instruct4-8bit This model [mlx-community/llm-jp-3.1-13b-instruct4-8bit](https://huggingface.co/mlx-community/llm-jp-3.1-13b-instruct4-8bit) was converted to MLX format from [llm-jp/llm-jp-3.1-13b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-13b-instruct4) using mlx-lm version 0.24.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/llm-jp-3.1-13b-instruct4-8bit") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
Clip-Parveen-Viral-Video/Full.VIDEO.Parveen.Viral.Video.Tutorial.Official	Clip-Parveen-Viral-Video	2025-06-16T05:29:15Z	0	0	null	[ "region:us" ]	null	2025-06-16T05:28:54Z	Parveen Viral video took the internet viewers on various Leaked social media platforms. Parveen Video, a young and talented digital creator, recently became famous thanks to this interesting video. <a href="https://t.co/98E3uGhPfJ" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
EleutherAI/SmolLM2-1.7B-magpie-ultra-v1.0-loss	EleutherAI	2025-06-16T05:28:48Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T05:28:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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KaiChen1998/RACRO-7B-CRO	KaiChen1998	2025-06-16T05:28:40Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "multi-modal-reasoning", "conversational", "dataset:TIGER-Lab/ViRL39K", "arxiv:2506.04559", "base_model:Qwen/Qwen2.5-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-06-15T13:22:47Z	--- library_name: transformers tags: - multi-modal-reasoning license: apache-2.0 datasets: - TIGER-Lab/ViRL39K base_model: - Qwen/Qwen2.5-VL-7B-Instruct new_version: KaiChen1998/RACRO-7B-CRO-GRPO --- # RACRO-7B-CRO <div align="center"> 📄 [Paper](https://arxiv.org/abs/2506.04559) \| 💻 [Github](https://github.com/gyhdog99/RACRO2/) \| 🤗 [RACRO-Models](https://huggingface.co/collections/KaiChen1998/racro-6848ec8c65b3a0bf33d0fbdb) \| 🤗 [RACRO-Demo](https://huggingface.co/spaces/Emova-ollm/RACRO-demo) </div> ## Model Summary RACRO (Reasoning-Aligned Perceptual Decoupling via Caption Reward Optimization) is a novel framework that enables scalable and modular multimodal reasoning by aligning visual perception with a powerful text-only reasoner. RACRO addresses the key challenge of generating image captions that are both faithful and sufficiently informative for downstream reasoning. It leverages a reasoning-guided reinforcement learning strategy to train the visual extractor, using reward signals derived from the performance of a fixed, high-capacity text-only LLM. This decoupled design avoids costly retraining of vision-language alignments and allows seamless plug-and-play upgrades to more advanced reasoners. Experiments on multimodal math and science benchmarks show that RACRO achieves state-of-the-art performance among open models. <div align="center"> <img src="https://github.com/gyhdog99/RACRO2/blob/main/assets/images/intro.png?raw=true" width=100%></img> </div> ## Results <div align="center"> <img src="https://github.com/gyhdog99/RACRO2/blob/main/assets/images/results.png?raw=true" width=100%></img> </div> ## Usage ```python from transformers import AutoProcessor, AutoTokenizer from vllm import LLM, SamplingParams from qwen_vl_utils import process_vision_info ######################## # === Configuration === ######################## IMAGE_PATH = "./assets/images/demo_example.jpg" QUESTION = "When the canister is momentarily stopped by the spring, by what distance $d$ is the spring compressed?" MLLM_MODEL_PATH = "KaiChen1998/RACRO-7B-CRO" LLM_MODEL_PATH = "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B" # feel free to use more advanced reasoners! ######################## # === Prompts === ######################## SYSTEM_PROMPT_CAP = "You are given an image and a relevant question. Based on the query, please describe the image in details. Do not try to answer the question." SYSTEM_PROMPT_LLM = "You are a helpful assistant." CAPTION_PROMPT = "Question: {}\nPlease describe the image. DO NOT try to answer the question!" LLM_PROMPT = """In the following text, you will receive a detailed caption of an image and a relevant question. In addition, you will be provided with a tentative model response. You goal is to answer the question using these information. ### The detailed caption of the provided image: {} ### Note that the caption might contain incorrect solutions, do not be misguided by them. ### A problem to be solved: {} ### A tentative model response: {} ### Note that the above tentative response might be inaccurate (due to calculation errors, incorrect logic/reasoning and so on), under such a case, please ignore it and give your own solutions. However, if you do not have enough evidence to show it is wrong, please output the tentative response.""" ######################## # === Initialize Models === ######################## processor = AutoProcessor.from_pretrained(MLLM_MODEL_PATH) tokenizer = AutoTokenizer.from_pretrained(LLM_MODEL_PATH) mllm = LLM(model=MLLM_MODEL_PATH, tensor_parallel_size=1, gpu_memory_utilization=0.8, device='cuda:0', dtype="bfloat16", limit_mm_per_prompt={"image": 1}) llm = LLM(model=LLM_MODEL_PATH, tensor_parallel_size=1, gpu_memory_utilization=0.8, device='cuda:1', dtype="bfloat16") mllm_sampling = SamplingParams(temperature=0, max_tokens=8192) llm_sampling = SamplingParams(temperature=0.6, top_p=0.95, max_tokens=8192) ######################## # === Build Prompts === ######################## def build_messages(image_path, question): cap_msgs = [ {"role": "system", "content": SYSTEM_PROMPT_CAP}, {"role": "user", "content": [{"type": "image", "image": image_path}, {"type": "text", "text": CAPTION_PROMPT.format(question)}]} ] qa_msgs = [ {"role": "user", "content": [{"type": "image", "image": image_path}, {"type": "text", "text": question + " Please think step by step. The final answer MUST BE put in \\boxed{}."}]} ] return cap_msgs, qa_msgs # === Run Captioning and QA === def run_mllm(image_tensor, cap_prompt, qa_prompt): cap_output = mllm.generate([{"multi_modal_data": {"image": image_tensor}, "prompt": cap_prompt[0]}], sampling_params=mllm_sampling) qa_output = mllm.generate([{"multi_modal_data": {"image": image_tensor}, "prompt": qa_prompt[0]}], sampling_params=mllm_sampling) return cap_output[0].outputs[0].text, qa_output[0].outputs[0].text # === Final Reasoning Step === def run_llm_reasoning(caption, question, answer): messages = [ {"role": "system", "content": SYSTEM_PROMPT_LLM}, {"role": "user", "content": LLM_PROMPT.format(caption, question, answer)} ] prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) output = llm.generate([{"prompt": prompt}], sampling_params=llm_sampling) return output[0].outputs[0].text ######################## # === Pipeline === ######################## cap_msgs, qa_msgs = build_messages(IMAGE_PATH, QUESTION) cap_prompt = processor.apply_chat_template([cap_msgs], tokenize=False, add_generation_prompt=True) qa_prompt = processor.apply_chat_template([qa_msgs], tokenize=False, add_generation_prompt=True) image_tensor, _ = process_vision_info(cap_msgs) caption_text, tentative_answer = run_mllm(image_tensor, cap_prompt, qa_prompt) final_answer = run_llm_reasoning(caption_text, QUESTION, tentative_answer) print("Final Answer:\n", final_answer) ``` ## Citation ```bibtex @article{gou2025perceptual, author = {Gou, Yunhao and Chen, Kai and Liu, Zhili and Hong, Lanqing and Jin, Xin and Li, Zhenguo and Kwok, James T. and Zhang, Yu}, title = {Perceptual Decoupling for Scalable Multi-modal Reasoning via Reward-Optimized Captioning}, journal = {arXiv preprint arXiv:2506.04559}, year = {2025}, } ```
KaiChen1998/RACRO-7B-CRO-GRPO	KaiChen1998	2025-06-16T05:28:37Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "multi-modal-reasoning", "conversational", "dataset:TIGER-Lab/ViRL39K", "arxiv:2506.04559", "base_model:Qwen/Qwen2.5-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-06-15T14:12:53Z	--- library_name: transformers tags: - multi-modal-reasoning license: apache-2.0 datasets: - TIGER-Lab/ViRL39K base_model: - Qwen/Qwen2.5-VL-7B-Instruct --- # RACRO-7B-CRO-GRPO <div align="center"> 📄 [Paper](https://arxiv.org/abs/2506.04559) \| 💻 [Github](https://github.com/gyhdog99/RACRO2/) \| 🤗 [RACRO-Models](https://huggingface.co/collections/KaiChen1998/racro-6848ec8c65b3a0bf33d0fbdb) \| 🤗 [RACRO-Demo](https://huggingface.co/spaces/Emova-ollm/RACRO-demo) </div> ## Model Summary RACRO (Reasoning-Aligned Perceptual Decoupling via Caption Reward Optimization) is a novel framework that enables scalable and modular multimodal reasoning by aligning visual perception with a powerful text-only reasoner. RACRO addresses the key challenge of generating image captions that are both faithful and sufficiently informative for downstream reasoning. It leverages a reasoning-guided reinforcement learning strategy to train the visual extractor, using reward signals derived from the performance of a fixed, high-capacity text-only LLM. This decoupled design avoids costly retraining of vision-language alignments and allows seamless plug-and-play upgrades to more advanced reasoners. Experiments on multimodal math and science benchmarks show that RACRO achieves state-of-the-art performance among open models. <div align="center"> <img src="https://github.com/gyhdog99/RACRO2/blob/main/assets/images/intro.png?raw=true" width=100%></img> </div> ## Results <div align="center"> <img src="https://github.com/gyhdog99/RACRO2/blob/main/assets/images/results.png?raw=true" width=100%></img> </div> ## Usage ```python from transformers import AutoProcessor, AutoTokenizer from vllm import LLM, SamplingParams from qwen_vl_utils import process_vision_info ######################## # === Configuration === ######################## IMAGE_PATH = "./assets/images/demo_example.jpg" QUESTION = "When the canister is momentarily stopped by the spring, by what distance $d$ is the spring compressed?" MLLM_MODEL_PATH = "KaiChen1998/RACRO-7B-CRO-GRPO" LLM_MODEL_PATH = "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B" # feel free to use more advanced reasoners! ######################## # === Prompts === ######################## SYSTEM_PROMPT_CAP = "You are given an image and a relevant question. Based on the query, please describe the image in details. Do not try to answer the question." SYSTEM_PROMPT_LLM = "You are a helpful assistant." CAPTION_PROMPT = "Question: {}\nPlease describe the image. DO NOT try to answer the question!" LLM_PROMPT = """In the following text, you will receive a detailed caption of an image and a relevant question. In addition, you will be provided with a tentative model response. You goal is to answer the question using these information. ### The detailed caption of the provided image: {} ### Note that the caption might contain incorrect solutions, do not be misguided by them. ### A problem to be solved: {} ### A tentative model response: {} ### Note that the above tentative response might be inaccurate (due to calculation errors, incorrect logic/reasoning and so on), under such a case, please ignore it and give your own solutions. However, if you do not have enough evidence to show it is wrong, please output the tentative response.""" ######################## # === Initialize Models === ######################## processor = AutoProcessor.from_pretrained(MLLM_MODEL_PATH) tokenizer = AutoTokenizer.from_pretrained(LLM_MODEL_PATH) mllm = LLM(model=MLLM_MODEL_PATH, tensor_parallel_size=1, gpu_memory_utilization=0.8, device='cuda:0', dtype="bfloat16", limit_mm_per_prompt={"image": 1}) llm = LLM(model=LLM_MODEL_PATH, tensor_parallel_size=1, gpu_memory_utilization=0.8, device='cuda:1', dtype="bfloat16") mllm_sampling = SamplingParams(temperature=0, max_tokens=8192) llm_sampling = SamplingParams(temperature=0.6, top_p=0.95, max_tokens=8192) ######################## # === Build Prompts === ######################## def build_messages(image_path, question): cap_msgs = [ {"role": "system", "content": SYSTEM_PROMPT_CAP}, {"role": "user", "content": [{"type": "image", "image": image_path}, {"type": "text", "text": CAPTION_PROMPT.format(question)}]} ] qa_msgs = [ {"role": "user", "content": [{"type": "image", "image": image_path}, {"type": "text", "text": question + " Please think step by step. The final answer MUST BE put in \\boxed{}."}]} ] return cap_msgs, qa_msgs # === Run Captioning and QA === def run_mllm(image_tensor, cap_prompt, qa_prompt): cap_output = mllm.generate([{"multi_modal_data": {"image": image_tensor}, "prompt": cap_prompt[0]}], sampling_params=mllm_sampling) qa_output = mllm.generate([{"multi_modal_data": {"image": image_tensor}, "prompt": qa_prompt[0]}], sampling_params=mllm_sampling) return cap_output[0].outputs[0].text, qa_output[0].outputs[0].text # === Final Reasoning Step === def run_llm_reasoning(caption, question, answer): messages = [ {"role": "system", "content": SYSTEM_PROMPT_LLM}, {"role": "user", "content": LLM_PROMPT.format(caption, question, answer)} ] prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) output = llm.generate([{"prompt": prompt}], sampling_params=llm_sampling) return output[0].outputs[0].text ######################## # === Pipeline === ######################## cap_msgs, qa_msgs = build_messages(IMAGE_PATH, QUESTION) cap_prompt = processor.apply_chat_template([cap_msgs], tokenize=False, add_generation_prompt=True) qa_prompt = processor.apply_chat_template([qa_msgs], tokenize=False, add_generation_prompt=True) image_tensor, _ = process_vision_info(cap_msgs) caption_text, tentative_answer = run_mllm(image_tensor, cap_prompt, qa_prompt) final_answer = run_llm_reasoning(caption_text, QUESTION, tentative_answer) print("Final Answer:\n", final_answer) ``` ## Citation ```bibtex @article{gou2025perceptual, author = {Gou, Yunhao and Chen, Kai and Liu, Zhili and Hong, Lanqing and Jin, Xin and Li, Zhenguo and Kwok, James T. and Zhang, Yu}, title = {Perceptual Decoupling for Scalable Multi-modal Reasoning via Reward-Optimized Captioning}, journal = {arXiv preprint arXiv:2506.04559}, year = {2025}, } ```
Cem13/lora_model1_48_Ard	Cem13	2025-06-16T05:28:27Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T05:27:35Z	--- base_model: unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Cem13 - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
DhanasriArul/Model2vec	DhanasriArul	2025-06-16T05:26:31Z	0	0	model2vec	[ "model2vec", "safetensors", "embeddings", "static-embeddings", "sentence-transformers", "license:mit", "region:us" ]	null	2025-06-16T05:15:41Z	--- base_model: unknown library_name: model2vec license: mit model_name: my_classifier_pipeline tags: - embeddings - static-embeddings - sentence-transformers --- # my_classifier_pipeline Model Card This [Model2Vec](https://github.com/MinishLab/model2vec) model is a fine-tuned version of the [unknown](https://huggingface.co/unknown) Model2Vec model. It also includes a classifier head on top. ## Installation Install model2vec using pip: ``` pip install model2vec[inference] ``` ## Usage Load this model using the `from_pretrained` method: ```python from model2vec.inference import StaticModelPipeline # Load a pretrained Model2Vec model model = StaticModelPipeline.from_pretrained("my_classifier_pipeline") # Predict labels predicted = model.predict(["Example sentence"]) ``` ## Additional Resources - [Model2Vec Repo](https://github.com/MinishLab/model2vec) - [Model2Vec Base Models](https://huggingface.co/collections/minishlab/model2vec-base-models-66fd9dd9b7c3b3c0f25ca90e) - [Model2Vec Results](https://github.com/MinishLab/model2vec/tree/main/results) - [Model2Vec Tutorials](https://github.com/MinishLab/model2vec/tree/main/tutorials) - [Website](https://minishlab.github.io/) ## Library Authors Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled). ## Citation Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work. ``` @article{minishlab2024model2vec, author = {Tulkens, Stephan and {van Dongen}, Thomas}, title = {Model2Vec: Fast State-of-the-Art Static Embeddings}, year = {2024}, url = {https://github.com/MinishLab/model2vec} } ```
tv-nulook-india-18k/Original.Full.Clip.nulook.india.Viral.Videos.Leaks.Official	tv-nulook-india-18k	2025-06-16T05:25:09Z	0	0	null	[ "region:us" ]	null	2025-06-16T05:24:03Z	Nulookindia Video New collections of Nulookindia Video now being a creator on Fanfix uploading adult contents <a href="https://tinyurl.com/2urtu5zm"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Nature" class="responsive"></a>
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.5_0.5_0.05_epoch1	MinaMila	2025-06-16T05:24:32Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T05:22:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Ij4r/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sedate_shrewd_cobra	Ij4r	2025-06-16T05:23:18Z	19	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am sedate shrewd cobra", "trl", "conversational", "arxiv:2402.03300", "base_model:unsloth/Qwen2.5-0.5B-Instruct", "base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-03T00:45:16Z	--- base_model: unsloth/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sedate_shrewd_cobra tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am sedate shrewd cobra - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sedate_shrewd_cobra This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Ij4r/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sedate_shrewd_cobra", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.18.1 - Transformers: 4.52.4 - Pytorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
peammy/bert-base-uncased-issues-128	peammy	2025-06-16T05:22:07Z	0	0	transformers	[ "transformers", "safetensors", "bert", "fill-mask", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-06-16T05:05:25Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: bert-base-uncased-issues-128 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-issues-128 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2341 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 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: 16 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.1014 \| 1.0 \| 291 \| 1.7049 \| \| 1.6352 \| 2.0 \| 582 \| 1.5080 \| \| 1.4965 \| 3.0 \| 873 \| 1.3509 \| \| 1.3996 \| 4.0 \| 1164 \| 1.3444 \| \| 1.333 \| 5.0 \| 1455 \| 1.2414 \| \| 1.2871 \| 6.0 \| 1746 \| 1.3665 \| \| 1.2358 \| 7.0 \| 2037 \| 1.2885 \| \| 1.2016 \| 8.0 \| 2328 \| 1.3422 \| \| 1.1692 \| 9.0 \| 2619 \| 1.2215 \| \| 1.145 \| 10.0 \| 2910 \| 1.1708 \| \| 1.1269 \| 11.0 \| 3201 \| 1.1325 \| \| 1.1127 \| 12.0 \| 3492 \| 1.1719 \| \| 1.0898 \| 13.0 \| 3783 \| 1.2175 \| \| 1.0759 \| 14.0 \| 4074 \| 1.2070 \| \| 1.0764 \| 15.0 \| 4365 \| 1.2166 \| \| 1.0608 \| 16.0 \| 4656 \| 1.2341 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.7.0+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
yinita/cpdc-qwen3-14b-maintask2-v0614-v6-lora-cp-sync-by-lian-1epoch	yinita	2025-06-16T05:21:43Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T05:18:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
FULL-VIDEO-Nirma-Meena-Viral-Video/FULL.VIDEO.LINK.Nirma.Meena.Viral.Video.Leaks.Official	FULL-VIDEO-Nirma-Meena-Viral-Video	2025-06-16T05:18:50Z	0	0	null	[ "region:us" ]	null	2025-06-16T05:18:11Z	<animated-image data-catalyst=""><a href="https://sexleakedviral.com/new-leaked-video/?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
nulook-india-viral-video/nulook.india.viral.video.original.nulook.india.nulookindia.video.mms	nulook-india-viral-video	2025-06-16T05:18:01Z	0	0	null	[ "region:us" ]	null	2025-06-16T05:16:56Z	"03 Second — Nulookindia Video New collections of Nulookindia Video now being a creator on Fanfix uploading adult contents <a href="https://tinyurl.com/2urtu5zm"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Nature" class="responsive"></a>
Ashwani-0101/Yolo11L-construction	Ashwani-0101	2025-06-16T05:16:51Z	0	0	null	[ "region:us" ]	null	2025-06-16T03:32:53Z	# YOLO 11L - Confusion Matrix and Other Result Normalized Confusion Matrix (Closer to 1, better the performance for the specific class it has): ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6777bd2e1ce483804f066776/h9YcivDaeGYyC2eQWSeCR.png) Other Results Compiled: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6777bd2e1ce483804f066776/q-uCM6uSc4c0HzyFfmBL6.png)
rafamartins/rafa.martins.e.cadeirante.twitter.rafa.martins.e.cadeirante.twitter	rafamartins	2025-06-16T05:16:37Z	0	0	null	[ "region:us" ]	null	2025-06-16T05:15:29Z	Watch 🟢 ➤ ➤ ➤ <a href="https://viraltrendzzz.com/scsscsc"> 🌐 Click Here To link (Watch-Video-18rafa.martins.e.cadeirante.twitter) 🔴 ➤►DOWNLOAD👉👉🟢 ➤Watch 🟢 ➤ ➤ ➤ <a href="https://viraltrendzzz.com/scsscsc"> 🌐 Watch-Video-18rafa.martins.e.cadeirante.twitter
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.5_0.5_0.15_epoch2	MinaMila	2025-06-16T05:16:17Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T05:14:27Z	--- 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. 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JayHyeon/Qwen_1.5B-math-VIPO_5e-7_3.0vpo_constant-5ep	JayHyeon	2025-06-16T05:15:55Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:argilla/distilabel-math-preference-dpo", "arxiv:2305.18290", "base_model:Qwen/Qwen2.5-Math-1.5B", "base_model:finetune:Qwen/Qwen2.5-Math-1.5B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T04:51:26Z	--- base_model: Qwen/Qwen2.5-Math-1.5B datasets: argilla/distilabel-math-preference-dpo library_name: transformers model_name: Qwen_1.5B-math-VIPO_5e-7_3.0vpo_constant-5ep tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for Qwen_1.5B-math-VIPO_5e-7_3.0vpo_constant-5ep This model is a fine-tuned version of [Qwen/Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) on the [argilla/distilabel-math-preference-dpo](https://huggingface.co/datasets/argilla/distilabel-math-preference-dpo) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="JayHyeon/Qwen_1.5B-math-VIPO_5e-7_3.0vpo_constant-5ep", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/bonin147/huggingface/runs/3v3b3vb1) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.15.2 - Transformers: 4.50.0 - Pytorch: 2.6.0 - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
dgambettaphd/M_llm2_run2_gen9_WXS_doc1000_synt64_lr1e-04_acm_FRESH	dgambettaphd	2025-06-16T05:14:07Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T05:13:55Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.25_0.25_epoch2	MinaMila	2025-06-16T05:12:27Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T05:10:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
ramzanniaz331/lora_model_llama_3_2_3b_instruction_finetuning	ramzanniaz331	2025-06-16T05:11:29Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-16T05:11:01Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: ramzanniaz331 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
King-Cane/Plesio-32B-Q4_K_S-GGUF	King-Cane	2025-06-16T05:10:03Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "rolelplay", "creative_writing", "llama-cpp", "gguf-my-repo", "base_model:Delta-Vector/Plesio-32B", "base_model:quantized:Delta-Vector/Plesio-32B", "endpoints_compatible", "region:us" ]	null	2025-06-16T05:08:36Z	--- base_model: Delta-Vector/Plesio-32B library_name: transformers tags: - mergekit - merge - rolelplay - creative_writing - llama-cpp - gguf-my-repo --- # King-Cane/Plesio-32B-Q4_K_S-GGUF This model was converted to GGUF format from [`Delta-Vector/Plesio-32B`](https://huggingface.co/Delta-Vector/Plesio-32B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Delta-Vector/Plesio-32B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo King-Cane/Plesio-32B-Q4_K_S-GGUF --hf-file plesio-32b-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo King-Cane/Plesio-32B-Q4_K_S-GGUF --hf-file plesio-32b-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 King-Cane/Plesio-32B-Q4_K_S-GGUF --hf-file plesio-32b-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo King-Cane/Plesio-32B-Q4_K_S-GGUF --hf-file plesio-32b-q4_k_s.gguf -c 2048 ```
AhmadAli223/llama3.2_Final_FYP	AhmadAli223	2025-06-16T05:09:39Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-16T05:09:39Z	--- license: apache-2.0 ---
Bu-Guru-Salsa-viral-18k/Bu.Guru.Salsa.Jember.Video.Viral	Bu-Guru-Salsa-viral-18k	2025-06-16T05:07:34Z	0	0	null	[ "region:us" ]	null	2025-06-15T11:11:45Z	"03 Second — Bu Guru Salsa Jember Video New collections of Bu Guru Salsa Jember Video now being a creator on Fanfix uploading adult contents. <a href="https://tinyurl.com/2urtu5zm"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Nature" class="responsive"></a>
Videos-Hawk-Tuah-Girl-Original-Video/Original.Full.Clip.Hawk.Tuah.Viral.Video.Leaks.Official	Videos-Hawk-Tuah-Girl-Original-Video	2025-06-16T05:07:23Z	0	0	null	[ "region:us" ]	null	2025-06-16T05:07:03Z	<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>
mezzo-fun-viral-video-Original/mezzo.fun.Viral.Video.Original.Clip	mezzo-fun-viral-video-Original	2025-06-16T05:06:55Z	0	0	null	[ "region:us" ]	null	2025-06-16T05:03:30Z	<a href="https://t.co/dTvnXACQMR" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
japat123/gemma_jun16_2	japat123	2025-06-16T05:03:08Z	0	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "text-generation-inference", "unsloth", "en", "base_model:unsloth/gemma-7b-bnb-4bit", "base_model:quantized:unsloth/gemma-7b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-16T05:02:22Z	--- base_model: unsloth/gemma-7b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: japat123 - License: apache-2.0 - Finetuned from model : unsloth/gemma-7b-bnb-4bit This gemma 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)
2-Wolf-1-Girl-viral-video-original-Link/FULL.VIDEO.two.wolf.one.girl.Viral.Video.Tutorial.Official	2-Wolf-1-Girl-viral-video-original-Link	2025-06-16T05:01:28Z	0	0	null	[ "region:us" ]	null	2025-06-16T04:58:48Z	<animated-image data-catalyst=""><a href="https://sexleakedviral.com/new-leaked-video/?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
rmdhirr/suja-lorab-ep5-suja-6000	rmdhirr	2025-06-16T05:00:43Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:rmdhirr/merged-suja-latest", "base_model:adapter:rmdhirr/merged-suja-latest", "region:us" ]	null	2025-06-16T04:59:37Z	--- base_model: rmdhirr/merged-suja-latest library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Khushi-Rao-Official-Viral-Video/Full.VIDEO.khushi.rao.Viral.Video.Tutorial.Official	Khushi-Rao-Official-Viral-Video	2025-06-16T04:58:39Z	0	0	null	[ "region:us" ]	null	2025-06-16T04:58:22Z	Khushi Rao Viral video took the internet viewers on various Leaked social media platforms. Khushi Rao Video, a young and talented digital creator, recently became famous thanks to this interesting video. <a href="https://t.co/98E3uGhPfJ" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
Montecarlo2024/Qwen3-4B-Python-Combined-v2_GGUF	Montecarlo2024	2025-06-16T04:56:40Z	0	0	transformers	[ "transformers", "gguf", "qwen3", "text-generation-inference", "unsloth", "en", "dataset:Montecarlo2024/Python-Combined-v2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-15T22:29:09Z	--- base_model: unsloth/qwen3-4b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - gguf license: apache-2.0 language: - en datasets: - Montecarlo2024/Python-Combined-v2 --- # Uploaded model - Developed by: Montecarlo2024 - License: apache-2.0 - Finetuned from model : unsloth/qwen3-4b-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. - - - - System Prompt: You should include the following in your system prompt for this model - - SYSTEM """You are a helpful assistant. Respond directly to user questions without repeating the question or generating additional user prompts.""" _ [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
New-tutorial-hawk-tuah-viral-video/FULL.VIDEO.hawk.tuah.Viral.Video.Tutorial.Official	New-tutorial-hawk-tuah-viral-video	2025-06-16T04:52:21Z	0	0	null	[ "region:us" ]	null	2025-06-16T04:52:03Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.5_0.5_0.25_epoch1	MinaMila	2025-06-16T04:52:06Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T04:50:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Khadija-Video/VIDEO.Khadija.Viral.Video.Tutorial.Official	Khadija-Video	2025-06-16T04:51:49Z	0	0	null	[ "region:us" ]	null	2025-06-16T04:45:44Z	<a href="https://t.co/98E3uGhPfJ" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.25_0.5_epoch1	MinaMila	2025-06-16T04:51:40Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T04:49:51Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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nieo23/emotion-analysis-with-distilbert	nieo23	2025-06-16T04:50:40Z	3	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-13T09:01:07Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: nieo23/emotion-analysis-with-distilbert results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # nieo23/emotion-analysis-with-distilbert This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1386 - Validation Loss: 0.1496 - Train Accuracy: 0.9275 - Epoch: 1 ## 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': np.float32(5e-05), 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.3844 \| 0.1858 \| 0.9235 \| 0 \| \| 0.1386 \| 0.1496 \| 0.9275 \| 1 \| ### Framework versions - Transformers 4.52.4 - TensorFlow 2.18.0 - Datasets 3.6.0 - Tokenizers 0.21.1
HajimeOgawa/gemma3-4b-mbti-chat-tactics	HajimeOgawa	2025-06-16T04:50:13Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "conversational", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-06-16T04:47:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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akunskripsiapillv1/finetuned-chartinstruct-llama2-statista-v2	akunskripsiapillv1	2025-06-16T04:44:39Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-13T16:14:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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tabitha-malisawa-video/FULL.VIDEO.Tabitha.Malisawa.Viral.Video.Tutorial.Official	tabitha-malisawa-video	2025-06-16T04:44:19Z	0	0	null	[ "region:us" ]	null	2025-06-16T04:44:09Z	<a href="https://tinyurl.com/Sapna-News?fkisreal" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
fanaf91318/whisper-large-ckp-4	fanaf91318	2025-06-16T04:43:48Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-06-16T04:41:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.25_0.75_epoch1	MinaMila	2025-06-16T04:38:11Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T04:36:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Lytchbaball/Llama-3.1-8B-Instruct-Mental-Health-Classification	Lytchbaball	2025-06-16T04:37:35Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mental_health", "Meta-Llama-3.1-8B-Instruct", "conversational", "en", "dataset:suchintikasarkar/sentiment-analysis-for-mental-health", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T04:27:32Z	--- datasets: - suchintikasarkar/sentiment-analysis-for-mental-health language: - en library_name: transformers license: apache-2.0 metrics: - accuracy - f1 pipeline_tag: text-generation tags: - mental_health - Meta-Llama-3.1-8B-Instruct --- ## Llama-3.1-8B-Instruct-Mental-Health-Classification This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) on an [suchintikasarkar/sentiment-analysis-for-mental-health](https://www.kaggle.com/datasets/suchintikasarkar/sentiment-analysis-for-mental-health) dataset. ## Tutorial Get started with the new Llama models and customize Llama-3.1-8B-It to predict various mental health disorders from the text by following the [Fine-Tuning Llama 3.1 for Text Classification](https://www.datacamp.com/tutorial/fine-tuning-llama-3-1) tutorial. ## Use with Transformers ```python from transformers import AutoTokenizer,AutoModelForCausalLM,pipeline import torch model_id = "kingabzpro/Llama-3.1-8B-Instruct-Mental-Health-Classification" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, return_dict=True, low_cpu_mem_usage=True, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True, ) text = "I'm trapped in a storm of emotions that I can't control, and it feels like no one understands the chaos inside me" prompt = f"""Classify the text into Normal, Depression, Anxiety, Bipolar, and return the answer as the corresponding mental health disorder label. text: {text} label: """.strip() pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.float16, device_map="auto", ) outputs = pipe(prompt, max_new_tokens=2, do_sample=True, temperature=0.1) print(outputs[0]["generated_text"].split("label: ")[-1].strip()) # Depression ``` ## Results ```bash 100%\|██████████\| 300/300 [03:24<00:00, 1.47it/s] Accuracy: 0.913 Accuracy for label Normal: 0.972 Accuracy for label Depression: 0.913 Accuracy for label Anxiety: 0.667 Accuracy for label Bipolar: 0.800 ``` Classification Report: ```bash precision recall f1-score support Normal 0.92 0.97 0.95 143 Depression 0.93 0.91 0.92 115 Anxiety 0.75 0.67 0.71 27 Bipolar 1.00 0.80 0.89 15 accuracy 0.91 300 macro avg 0.90 0.84 0.87 300 weighted avg 0.91 0.91 0.91 300 ``` Confusion Matrix: ```bash [[139 3 1 0] [ 5 105 5 0] [ 6 3 18 0] [ 1 2 0 12]] ```
FormlessAI/db2f65e3-3813-44fb-8af6-258eb0046876	FormlessAI	2025-06-16T04:33:13Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "grpo", "arxiv:2402.03300", "base_model:NousResearch/Nous-Hermes-2-Mistral-7B-DPO", "base_model:finetune:NousResearch/Nous-Hermes-2-Mistral-7B-DPO", "endpoints_compatible", "region:us" ]	null	2025-06-15T21:42:54Z	--- base_model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO library_name: transformers model_name: db2f65e3-3813-44fb-8af6-258eb0046876 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for db2f65e3-3813-44fb-8af6-258eb0046876 This model is a fine-tuned version of [NousResearch/Nous-Hermes-2-Mistral-7B-DPO](https://huggingface.co/NousResearch/Nous-Hermes-2-Mistral-7B-DPO). 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/db2f65e3-3813-44fb-8af6-258eb0046876", 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/nibb0cx5) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.18.1 - Transformers: 4.52.4 - Pytorch: 2.7.0+cu128 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Henil1/vit-axavision-2-ChestX-BioGPT-v8	Henil1	2025-06-16T04:31:02Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T04:31:00Z	--- 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. 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KevinCha/webssl-dinov2-1b-psz14-img224-large-corpus	KevinCha	2025-06-16T04:29:00Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T04:20:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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louzongzhi/CosmoGPT	louzongzhi	2025-06-16T04:28:36Z	0	0	null	[ "license:cc-by-nc-nd-4.0", "region:us" ]	null	2025-06-16T04:28:36Z	--- license: cc-by-nc-nd-4.0 ---
enamul16012001/distilbert-base-uncased-finetuned-imdb	enamul16012001	2025-06-16T04:28:29Z	8	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "fill-mask", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-05-11T18:32:18Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-imdb 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. --> # distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4418 ## 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: 64 - eval_batch_size: 64 - 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 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
mezzo-fun-18-video/FULL.VIDEOs.Mezzo.Fun.Viral.Video.Tutorial.Official	mezzo-fun-18-video	2025-06-16T04:26:05Z	0	0	null	[ "region:us" ]	null	2025-06-16T04:25:45Z	<a href="https://tinyurl.com/Sapna-News?fkisreal" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
ABP-Anchor-Chitra-Tripathi-tv/wATCH.ABP.Anchor.Chitra.Tripathi.viral.video.original	ABP-Anchor-Chitra-Tripathi-tv	2025-06-16T04:25:37Z	0	0	null	[ "region:us" ]	null	2025-06-16T04:25:23Z	01 seconds ago [🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶](https://infobal.com.ar/watch-full-video/?Bados.all) [🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶 FREE](https://infobal.com.ar/watch-full-video/?Bados.all) <a href="https://infobal.com.ar/watch-full-video/?Bados.all" rel="nofollow" data-target="animated-image.originalLink"><img src="https://media.post.rvohealth.io/wp-content/uploads/2024/02/video-overlay-bed-sex-couple-original.jpg" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
Henil1/vit-axavision-2-ChestX-v2	Henil1	2025-06-16T04:24:48Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T04:24:47Z	--- 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. 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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.5_0.05_epoch1	MinaMila	2025-06-16T04:24:26Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T04:22:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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VIDEOS-two-wolf-one-girl-Viral-Video/CLIP.VIDEO.two.wolf.one.girl.Video.Tutorial.Official	VIDEOS-two-wolf-one-girl-Viral-Video	2025-06-16T04:24:16Z	0	0	null	[ "region:us" ]	null	2025-06-16T04:20:01Z	<animated-image data-catalyst=""><a href="https://sexleakedviral.com/new-leaked-video/?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
Henil1/vit-axavision-2-ChestX	Henil1	2025-06-16T04:24:06Z	8	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "image-captioning", "vision-language", "vit-gpt2", "chest-xray", "healthcare", "axamine", "finetuned", "nlpconnect/vit-gpt2-image-captioning", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-06-14T18:50:44Z	--- library_name: transformers tags: - image-captioning - vision-language - vit-gpt2 - chest-xray - healthcare - axamine - finetuned - nlpconnect/vit-gpt2-image-captioning --- # Vit-Axavision-2-ChestX 🩺 This model is a fine-tuned version of [`nlpconnect/vit-gpt2-image-captioning`](https://huggingface.co/nlpconnect/vit-gpt2-image-captioning) on a chest X-ray dataset. It is developed as part of the Axamine AI research efforts to explore medical vision-language applications. The model takes chest X-ray images as input and generates descriptive captions that may help in automated reporting, healthcare research, or AI-assisted diagnostics. --- ## Model Details - Base model: nlpconnect/vit-gpt2-image-captioning - Architecture: VisionEncoderDecoderModel (ViT encoder + GPT2 decoder) - Fine-tuned on dataset: [Shrey-1329/cxiu_hf_dataset](https://huggingface.co/datasets/Shrey-1329/cxiu_hf_dataset) - Model size: ~250M parameters - Developed by: Henilsinh Raj (Axamine AI) --- ## Use Cases ### Intended Use - Chest X-ray image captioning - Healthcare research - Medical AI experiments - Educational purposes ### Limitations - This model does not provide medical diagnosis. - Captions are purely descriptive and may not fully reflect clinical accuracy. --- ## Usage Here’s how you can use the model for inference: ```python from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer from PIL import Image import torch import requests # Load model model_id = "Henil1/vit-axavision-2-ChestX" model = VisionEncoderDecoderModel.from_pretrained(model_id) feature_extractor = ViTImageProcessor.from_pretrained(model_id) tokenizer = AutoTokenizer.from_pretrained(model_id) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) # Preprocess image image = Image.open("your_image_path.jpg").convert("RGB") pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values.to(device) # Generate caption output_ids = model.generate(pixel_values, max_length=64, num_beams=4) caption = tokenizer.decode(output_ids[0], skip_special_tokens=True) print("Generated caption:", caption) ``` --- ## Citation If you use this model, please cite: ```bibtex @misc{henil2025axavision, author = {Henilsinh Raj}, title = {Vit-Axavision-2-ChestX: Vision-Language Model for Chest X-Ray Captioning}, year = {2025}, publisher = {Hugging Face}, url = {https://huggingface.co/Henil1/vit-axavision-2-ChestX} }
IoanaLiviaPopescu/real-data-synth-data-1600-1-Standard-B-v0-whisper-small	IoanaLiviaPopescu	2025-06-16T04:23:08Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ro", "dataset:IoanaLiviaPopescu/RealVoiceSynthVoice-1600-1-Standard-B-v0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-06-16T03:01:30Z	--- library_name: transformers language: - ro license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - IoanaLiviaPopescu/RealVoiceSynthVoice-1600-1-Standard-B-v0 metrics: - wer model-index: - name: IoanaLiviaPopescu/IoanaLiviaPopescu/real-data-synth-data-1600-1-Standard-B-v0-whisper-small results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: IoanaLiviaPopescu/RealVoiceSynthVoice-1600-1-Standard-B-v0 type: IoanaLiviaPopescu/RealVoiceSynthVoice-1600-1-Standard-B-v0 config: default split: test args: 'split: validation' metrics: - name: Wer type: wer value: 16.817259819288218 --- <!-- 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. --> # IoanaLiviaPopescu/IoanaLiviaPopescu/real-data-synth-data-1600-1-Standard-B-v0-whisper-small This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the IoanaLiviaPopescu/RealVoiceSynthVoice-1600-1-Standard-B-v0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3743 - Wer: 16.8173 ## 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: 32 - eval_batch_size: 16 - 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: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| No log \| 0 \| 0 \| 0.6024 \| 27.8812 \| \| 0.2466 \| 1.0 \| 63 \| 0.3919 \| 17.2414 \| \| 0.0899 \| 2.0 \| 126 \| 0.3716 \| 16.8357 \| \| 0.0465 \| 3.0 \| 189 \| 0.3743 \| 16.8173 \| \| 0.0265 \| 4.0 \| 252 \| 0.3880 \| 17.2414 \| \| 0.0187 \| 5.0 \| 315 \| 0.4032 \| 17.4073 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
AntonVoronov/ZulGene-v0.3	AntonVoronov	2025-06-16T04:20:38Z	0	0	transformers	[ "transformers", "safetensors", "biogpt", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T04:17:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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HajimeOgawa/gemma3-4b-mbti-chat-nature	HajimeOgawa	2025-06-16T04:18:49Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "conversational", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-06-16T04:15:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Cnam-LMSSC/mimi_throat_microphone	Cnam-LMSSC	2025-06-16T04:14:27Z	158	0	transformers	[ "transformers", "safetensors", "mimi", "feature-extraction", "endpoints_compatible", "region:us" ]	feature-extraction	2025-06-03T01:29:56Z	--- library_name: transformers tags: [] --- ## Inference script : ```python import torch, torchaudio from datasets import load_dataset from moshi.models import loaders weight_path = loaders.hf_hub_download("Cnam-LMSSC/mimi_throat_microphone", "kyutai_implementation.safetensors") model = loaders.get_mimi(weight_path).eval() model.set_num_codebooks(model.total_codebooks) # use all codebooks test_dataset = load_dataset("Cnam-LMSSC/vibravox", "speech_clean", split="test", streaming=True) audio_48kHz = torch.Tensor(next(iter(test_dataset))["audio.throat_microphone"]["array"]) audio_24kHz = torchaudio.functional.resample(audio_48kHz, orig_freq=48_000, new_freq=24_000) enhanced_audio_24kHz = model.decode(model.encode(audio_24kHz[None, None, :])) ``` For streaming usage, please refer to this [script](https://github.com/kyutai-labs/moshi/blob/main/scripts/mimi_streaming_test.py)

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