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train · 884k rows

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intedont/orpheus_2.5_epoch_tokenizer	intedont	2025-04-06T17:04:43Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-06T17:04:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
TOMFORD79/ImKing_v1_1.1	TOMFORD79	2025-04-06T17:04:09Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-04-06T16:26:22Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
7sunshine7/music_style_predict	7sunshine7	2025-04-06T17:03:59Z	0	0	null	[ "safetensors", "license:apache-2.0", "region:us" ]	null	2025-04-06T17:02:25Z	--- license: apache-2.0 ---
intedont/orpheus_2.5_epoch	intedont	2025-04-06T17:03:39Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-06T16:47:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
DreadPoor/contestant1-Q4_K_M-GGUF	DreadPoor	2025-04-06T17:02:23Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:DreadPoor/contestant1", "base_model:quantized:DreadPoor/contestant1", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-06T17:01:44Z	--- base_model: DreadPoor/contestant1 library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # DreadPoor/contestant1-Q4_K_M-GGUF This model was converted to GGUF format from [`DreadPoor/contestant1`](https://huggingface.co/DreadPoor/contestant1) 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/DreadPoor/contestant1) 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 DreadPoor/contestant1-Q4_K_M-GGUF --hf-file contestant1-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo DreadPoor/contestant1-Q4_K_M-GGUF --hf-file contestant1-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo DreadPoor/contestant1-Q4_K_M-GGUF --hf-file contestant1-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo DreadPoor/contestant1-Q4_K_M-GGUF --hf-file contestant1-q4_k_m.gguf -c 2048 ```
mergekit-community/mergekit-model_stock-caxsfuh	mergekit-community	2025-04-06T17:02:04Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "arxiv:2403.19522", "base_model:mergekit-community/mergekit-model_stock-tiwlqms", "base_model:merge:mergekit-community/mergekit-model_stock-tiwlqms", "base_model:mergekit-community/mergekit-passthrough-gujurtn", "base_model:merge:mergekit-community/mergekit-passthrough-gujurtn", "base_model:mergekit-community/mergekit-passthrough-zyecuzy", "base_model:merge:mergekit-community/mergekit-passthrough-zyecuzy", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-06T16:57:04Z	--- base_model: - mergekit-community/mergekit-passthrough-gujurtn - mergekit-community/mergekit-model_stock-tiwlqms - mergekit-community/mergekit-passthrough-zyecuzy library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [mergekit-community/mergekit-model_stock-tiwlqms](https://huggingface.co/mergekit-community/mergekit-model_stock-tiwlqms) as a base. ### Models Merged The following models were included in the merge: * [mergekit-community/mergekit-passthrough-gujurtn](https://huggingface.co/mergekit-community/mergekit-passthrough-gujurtn) * [mergekit-community/mergekit-passthrough-zyecuzy](https://huggingface.co/mergekit-community/mergekit-passthrough-zyecuzy) ### Configuration The following YAML configuration was used to produce this model: ```yaml dtype: bfloat16 merge_method: model_stock base_model: mergekit-community/mergekit-model_stock-tiwlqms models: - model: mergekit-community/mergekit-model_stock-tiwlqms - model: mergekit-community/mergekit-passthrough-zyecuzy - model: mergekit-community/mergekit-passthrough-gujurtn parameters: normalize: true ```
divito48/Gaio	divito48	2025-04-06T17:01:01Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-06T17:01:01Z	--- license: apache-2.0 ---
avzhuravleva/kelogsloops_style_LoRA	avzhuravleva	2025-04-06T17:00:25Z	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-04-06T15:58:20Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: photo in KELOGSLOOPS style widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - avzhuravleva/kelogsloops_style_LoRA <Gallery /> ## Model description These are avzhuravleva/kelogsloops_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use photo in KELOGSLOOPS style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](avzhuravleva/kelogsloops_style_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
TheDrummer/Fallen-Command-A-111B-v1.1-GGUF	TheDrummer	2025-04-06T17:00:17Z	2	1	null	[ "gguf", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-05T10:45:16Z	--- license: other --- # Join our Discord! https://discord.gg/Nbv9pQ88Xb ## Nearly 5000 members of helpful, LLM enthusiasts! A hub for players and makers alike! --- [BeaverAI](https://huggingface.co/BeaverAI) proudly presents... # Fallen Command A 111B v1.1 👺 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f2fd1c25b848bd061b5c2e/FrFoiQjpG3yLiLF-HFETc.png) > Hope not ever to see Heaven. I have come to lead you to the other shore; into eternal darkness; into fire and into ice. ## Special Thanks - 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. ## Usage - Use the Cohere / Command chat template ## What's New with v1.1 - Toned down the toxicity. - Capable of switching between good and evil, instead of spiraling into one side. - Absent of positivity that often plagued storytelling and roleplay in subtle and blatant ways. - Evil and gray characters are still represented well. - Slopless and enhanced writing, unshackled from safety guidelines. - More creative and unique than OG CMD-A. - Intelligence boost, retaining more smarts from the OG. ## Links - Original: https://huggingface.co/TheDrummer/Fallen-Command-A-111B-v1.1 - GGUF: https://huggingface.co/TheDrummer/Fallen-Command-A-111B-v1.1-GGUF - iMatrix (recommended): https://huggingface.co/bartowski/TheDrummer_Fallen-Command-A-111B-v1.1-GGUF `config-v1c`
TheDrummer/Fallen-Command-A-111B-v1.1	TheDrummer	2025-04-06T16:59:55Z	12	1	null	[ "safetensors", "cohere2", "license:other", "region:us" ]	null	2025-04-05T10:12:25Z	--- license: other --- # Join our Discord! https://discord.gg/Nbv9pQ88Xb ## Nearly 5000 members of helpful, LLM enthusiasts! A hub for players and makers alike! --- [BeaverAI](https://huggingface.co/BeaverAI) proudly presents... # Fallen Command A 111B v1.1 👺 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f2fd1c25b848bd061b5c2e/FrFoiQjpG3yLiLF-HFETc.png) > Hope not ever to see Heaven. I have come to lead you to the other shore; into eternal darkness; into fire and into ice. ## Special Thanks - 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. ## Usage - Use the Cohere / Command chat template ## What's New with v1.1 - Toned down the toxicity. - Capable of switching between good and evil, instead of spiraling into one side. - Absent of positivity that often plagued storytelling and roleplay in subtle and blatant ways. - Evil and gray characters are still represented well. - Slopless and enhanced writing, unshackled from safety guidelines. - More creative and unique than OG CMD-A. - Intelligence boost, retaining more smarts from the OG. ## Links - Original: https://huggingface.co/TheDrummer/Fallen-Command-A-111B-v1.1 - GGUF: https://huggingface.co/TheDrummer/Fallen-Command-A-111B-v1.1-GGUF - iMatrix (recommended): https://huggingface.co/bartowski/TheDrummer_Fallen-Command-A-111B-v1.1-GGUF `config-v1c`
yashrajkupekar/Reinforce-cartpole	yashrajkupekar	2025-04-06T16:59:05Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2025-04-06T10:00:13Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-cartpole results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 200.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
Ridu-99/Arizona-01	Ridu-99	2025-04-06T16:59:01Z	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	2025-04-06T16:15:12Z	--- 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: Arizona-01 --- # Arizona 01 <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 `Arizona-01` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Arizona-01", "lora_weights": "https://huggingface.co/Ridu-99/Arizona-01/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('Ridu-99/Arizona-01', weight_name='lora.safetensors') image = pipeline('Arizona-01').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: 3480 - Learning rate: 0.0004 - LoRA rank: 32 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Ridu-99/Arizona-01/discussions) to add images that show off what you’ve made with this LoRA.
NazzX1/LED-note-modified	NazzX1	2025-04-06T16:58:06Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "led", "text2text-generation", "generated_from_trainer", "base_model:MingZhong/DialogLED-base-16384", "base_model:finetune:MingZhong/DialogLED-base-16384", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-04-06T16:57:39Z	--- library_name: transformers base_model: MingZhong/DialogLED-base-16384 tags: - generated_from_trainer metrics: - rouge model-index: - name: LED-note-modified 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. --> # LED-note-modified This model is a fine-tuned version of [MingZhong/DialogLED-base-16384](https://huggingface.co/MingZhong/DialogLED-base-16384) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4517 - Rouge1: 0.1441 - Rouge2: 0.0896 - Rougel: 0.1067 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - 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: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\| \| 0.4719 \| 1.0 \| 800 \| 0.4643 \| 0.1437 \| 0.0886 \| 0.1062 \| \| 0.4267 \| 2.0 \| 1600 \| 0.4517 \| 0.1441 \| 0.0896 \| 0.1067 \| ### Framework versions - Transformers 4.51.0 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
LarryAIDraw/honkai_star_rail_3d_style	LarryAIDraw	2025-04-06T16:57:50Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-04-06T16:57:09Z	--- license: creativeml-openrail-m ---
cuti3epatootie/lora_model_QC2	cuti3epatootie	2025-04-06T16:56:33Z	1	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "orpo", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-03-31T19:31:34Z	--- base_model: unsloth/qwen2.5-coder-14b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - orpo license: apache-2.0 language: - en --- # Uploaded model - Developed by: cuti3epatootie - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-coder-14b-instruct-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)
rajparikh03/gemma-3-peft-sft-total	rajparikh03	2025-04-06T16:56:11Z	0	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-06T16:54:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Jully-Dreemurr/truska_chulka_LoRA	Jully-Dreemurr	2025-04-06T16:54:17Z	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-04-06T16:54:10Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: 'anime artwork in truska and chulka style, ' widget: [] tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - Jully-Dreemurr/truska_chulka_LoRA <Gallery /> ## Model description These are Jully-Dreemurr/truska_chulka_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use anime artwork in truska and chulka style, to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](Jully-Dreemurr/truska_chulka_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
skarnam/FFT_model_Gemma	skarnam	2025-04-06T16:51:56Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-06T16:49:15Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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JonyC/scibert-science-word-classifier	JonyC	2025-04-06T16:49:02Z	0	0	null	[ "safetensors", "Science", "classifier", "words", "en", "base_model:allenai/scibert_scivocab_uncased", "base_model:finetune:allenai/scibert_scivocab_uncased", "license:apache-2.0", "region:us" ]	null	2025-03-05T17:10:08Z	--- license: apache-2.0 language: - en base_model: - allenai/scibert_scivocab_uncased tags: - Science - classifier - words --- <b><span style="color:red;">IMPORTENT! READ THIS!</span></b> ## Model description This model recognizes scientific terms in a given text. The best way to use it is as follows: ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from nltk.tokenize import word_tokenize import torch import spacy # You might want to use it to remove enteties in the text (the model usually predicts them as scientific) nlp = spacy.load("en_core_web_sm") # doc = nlp(text) # names = [ent.text for ent in doc.ents] tokenizer = AutoTokenizer.from_pretrained("JonyC/scibert-science-word-classifier") model = AutoModelForTokenClassification.from_pretrained("JonyC/scibert-science-word-classifier") # define max_len as needed. def classify_term(term, max_len=12): term = term.lower() tokens = tokenizer(term, return_tensors="pt", truncation=True, padding=True, max_length=max_len).to(device) output = model(**tokens).logits pred = torch.argmax(output).item() return "Scientific" if pred == 1 else "Non-Scientific" # For single term: print(classify_term("quantum mechanics")) print(classify_term("table")) print(classify_term("photosynthesis")) # For sentences: words = word_tokenize("some sentence") # you can also use sentence.split() results = [] for w in words: res = classify_term(w) results.append(res) for w, p in zip(words, results): print(f"Word: {w}, Predicted Label: {p}") ``` ## Example usage Given the following text: "Quantum computing is a new field that changes how we think about solving complex problems. Unlike regular computers that use bits (which are either 0 or 1), quantum computers use qubits, which can be both 0 and 1 at the same time, thanks to a property called superposition. One important feature of quantum computers is quantum entanglement, where two qubits can be linked in such a way that changing one will instantly affect the other, no matter how far apart they are. This allows quantum computers to perform certain calculations much faster than traditional computers. For example, quantum computers could one day factor large numbers much faster, which is currently a task that takes regular computers a very long time. However, there are still challenges to overcome, like maintaining the qubits' state long enough to do calculations without errors. Scientists are working on ways to fix these errors, which is necessary for quantum computers to work on a large scale and solve real-world problems more efficiently than today's computers." the words he classified as scientific are:<br> ``` ['Quantum', 'computing', 'field', 'complex', 'quantum', 'qubits', 'property', 'superposition', 'entanglement', 'matter', 'factor', 'state', 'scale'] ``` # results 'scibert-science-word-classifier' This model is a fine-tuned version of [allenai/scibert_scivocab_cased](https://huggingface.co/allenai/scibert_scivocab_cased) on the [JonyC/ScienceGlossary](https://huggingface.co/datasets/JonyC/ScienceGlossary) dataset. It achieves the following results on the evaluation set: - Loss: 0.1763 - Precision: 0.9487 - Recall: 0.9068 - F1: 0.9273 - Accuracy: 0.9695 - ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 128 - eval_batch_size: 128 - 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: 35
Skyfallirk/charushin_LoRa	Skyfallirk	2025-04-06T16:46:19Z	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-04-06T16:46:14Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: a photo collage in charushin style widget: [] tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - Skyfallirk/charushin_LoRa <Gallery /> ## Model description These are Skyfallirk/charushin_LoRa LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo collage in charushin style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](Skyfallirk/charushin_LoRa/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
yeok/qwen-2.5-0.5B-instruct-sft-lora-countdown-mixed-10k	yeok	2025-04-06T16:44:51Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-04-06T15:00:17Z	--- base_model: Qwen/Qwen2.5-0.5B-Instruct library_name: transformers model_name: qwen-2.5-0.5B-instruct-sft-lora-countdown-mixed-10k tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for qwen-2.5-0.5B-instruct-sft-lora-countdown-mixed-10k This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/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="yeok/qwen-2.5-0.5B-instruct-sft-lora-countdown-mixed-10k", 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/yeokch/stream-of-search-train/runs/zp6wb87e) This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.50.0 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
justChills/MiniLM-L6-v2-MamaQuery	justChills	2025-04-06T16:44:12Z	0	0	null	[ "safetensors", "bert", "text2text-generation", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "region:us" ]	text2text-generation	2025-04-06T16:24:21Z	--- pipeline_tag: text2text-generation base_model: - google/gemma-3-27b-it ---
memevis/WL31	memevis	2025-04-06T16:42:39Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-06T16:39:24Z	--- 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]
memevis/WL24	memevis	2025-04-06T16:42:35Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-06T16:40:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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memevis/WL26	memevis	2025-04-06T16:42:30Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-06T16:40:00Z	--- 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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memevis/WL32	memevis	2025-04-06T16:42:18Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-06T16:40:05Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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genki10/Trial3BERT_AugV8_k5_task1_organization_sp020_lw010_fold4	genki10	2025-04-06T16:41:39Z	0	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "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" ]	text-classification	2025-04-06T16:22:55Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: Trial3BERT_AugV8_k5_task1_organization_sp020_lw010_fold4 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. --> # Trial3BERT_AugV8_k5_task1_organization_sp020_lw010_fold4 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: 0.7087 - Qwk: 0.4647 - Mse: 0.7087 - Rmse: 0.8419 ## 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 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: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\| \| No log \| 1.0 \| 4 \| 7.6852 \| 0.0 \| 7.6852 \| 2.7722 \| \| No log \| 2.0 \| 8 \| 4.3924 \| 0.0079 \| 4.3924 \| 2.0958 \| \| No log \| 3.0 \| 12 \| 2.6225 \| 0.0040 \| 2.6225 \| 1.6194 \| \| No log \| 4.0 \| 16 \| 1.6783 \| 0.0432 \| 1.6783 \| 1.2955 \| \| No log \| 5.0 \| 20 \| 1.0566 \| 0.0212 \| 1.0566 \| 1.0279 \| \| No log \| 6.0 \| 24 \| 0.9603 \| 0.0310 \| 0.9603 \| 0.9799 \| \| No log \| 7.0 \| 28 \| 1.4350 \| 0.0484 \| 1.4350 \| 1.1979 \| \| No log \| 8.0 \| 32 \| 0.8649 \| 0.2334 \| 0.8649 \| 0.9300 \| \| No log \| 9.0 \| 36 \| 0.9956 \| 0.1472 \| 0.9956 \| 0.9978 \| \| No log \| 10.0 \| 40 \| 1.1372 \| 0.2609 \| 1.1372 \| 1.0664 \| \| No log \| 11.0 \| 44 \| 0.7270 \| 0.4443 \| 0.7270 \| 0.8526 \| \| No log \| 12.0 \| 48 \| 0.6761 \| 0.3916 \| 0.6761 \| 0.8222 \| \| No log \| 13.0 \| 52 \| 0.7981 \| 0.3532 \| 0.7981 \| 0.8934 \| \| No log \| 14.0 \| 56 \| 0.6918 \| 0.4589 \| 0.6918 \| 0.8318 \| \| No log \| 15.0 \| 60 \| 0.6926 \| 0.5291 \| 0.6926 \| 0.8322 \| \| No log \| 16.0 \| 64 \| 0.9074 \| 0.4437 \| 0.9074 \| 0.9526 \| \| No log \| 17.0 \| 68 \| 0.7076 \| 0.5115 \| 0.7076 \| 0.8412 \| \| No log \| 18.0 \| 72 \| 0.7593 \| 0.5046 \| 0.7593 \| 0.8714 \| \| No log \| 19.0 \| 76 \| 0.7876 \| 0.4624 \| 0.7876 \| 0.8875 \| \| No log \| 20.0 \| 80 \| 0.7343 \| 0.4728 \| 0.7343 \| 0.8569 \| \| No log \| 21.0 \| 84 \| 0.7376 \| 0.4990 \| 0.7376 \| 0.8588 \| \| No log \| 22.0 \| 88 \| 0.7141 \| 0.4981 \| 0.7141 \| 0.8450 \| \| No log \| 23.0 \| 92 \| 0.7028 \| 0.5068 \| 0.7028 \| 0.8383 \| \| No log \| 24.0 \| 96 \| 0.7847 \| 0.4603 \| 0.7847 \| 0.8859 \| \| No log \| 25.0 \| 100 \| 0.8353 \| 0.4244 \| 0.8353 \| 0.9139 \| \| No log \| 26.0 \| 104 \| 0.7059 \| 0.4889 \| 0.7059 \| 0.8402 \| \| No log \| 27.0 \| 108 \| 1.0087 \| 0.3493 \| 1.0087 \| 1.0043 \| \| No log \| 28.0 \| 112 \| 0.5947 \| 0.5616 \| 0.5947 \| 0.7712 \| \| No log \| 29.0 \| 116 \| 0.7313 \| 0.4418 \| 0.7313 \| 0.8552 \| \| No log \| 30.0 \| 120 \| 0.6589 \| 0.5504 \| 0.6589 \| 0.8117 \| \| No log \| 31.0 \| 124 \| 0.7888 \| 0.4542 \| 0.7888 \| 0.8882 \| \| No log \| 32.0 \| 128 \| 0.7826 \| 0.4370 \| 0.7826 \| 0.8847 \| \| No log \| 33.0 \| 132 \| 0.7835 \| 0.4391 \| 0.7835 \| 0.8852 \| \| No log \| 34.0 \| 136 \| 0.8954 \| 0.4087 \| 0.8954 \| 0.9463 \| \| No log \| 35.0 \| 140 \| 0.5926 \| 0.5679 \| 0.5926 \| 0.7698 \| \| No log \| 36.0 \| 144 \| 0.9144 \| 0.3895 \| 0.9144 \| 0.9562 \| \| No log \| 37.0 \| 148 \| 0.6112 \| 0.5596 \| 0.6112 \| 0.7818 \| \| No log \| 38.0 \| 152 \| 0.8593 \| 0.3825 \| 0.8593 \| 0.9270 \| \| No log \| 39.0 \| 156 \| 0.6311 \| 0.5195 \| 0.6311 \| 0.7944 \| \| No log \| 40.0 \| 160 \| 0.8590 \| 0.4136 \| 0.8590 \| 0.9268 \| \| No log \| 41.0 \| 164 \| 0.7096 \| 0.4792 \| 0.7096 \| 0.8424 \| \| No log \| 42.0 \| 168 \| 0.7121 \| 0.4876 \| 0.7121 \| 0.8438 \| \| No log \| 43.0 \| 172 \| 0.9623 \| 0.3402 \| 0.9623 \| 0.9810 \| \| No log \| 44.0 \| 176 \| 0.6471 \| 0.5016 \| 0.6471 \| 0.8045 \| \| No log \| 45.0 \| 180 \| 0.8586 \| 0.3767 \| 0.8586 \| 0.9266 \| \| No log \| 46.0 \| 184 \| 0.6931 \| 0.4723 \| 0.6931 \| 0.8325 \| \| No log \| 47.0 \| 188 \| 0.6850 \| 0.5086 \| 0.6850 \| 0.8276 \| \| No log \| 48.0 \| 192 \| 0.8139 \| 0.4080 \| 0.8139 \| 0.9022 \| \| No log \| 49.0 \| 196 \| 0.8065 \| 0.4075 \| 0.8065 \| 0.8980 \| \| No log \| 50.0 \| 200 \| 0.7087 \| 0.4647 \| 0.7087 \| 0.8419 \| ### Framework versions - Transformers 4.47.0 - Pytorch 2.5.1+cu121 - Datasets 3.3.1 - Tokenizers 0.21.0
DevQuasar/prithivMLmods.Callisto-OCR3-2B-Instruct-GGUF	DevQuasar	2025-04-06T16:41:35Z	0	0	null	[ "text-generation", "base_model:prithivMLmods/Callisto-OCR3-2B-Instruct", "base_model:finetune:prithivMLmods/Callisto-OCR3-2B-Instruct", "region:us" ]	text-generation	2025-04-06T16:41:34Z	--- base_model: - prithivMLmods/Callisto-OCR3-2B-Instruct pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) 'Make knowledge free for everyone' Quantized version of: [prithivMLmods/Callisto-OCR3-2B-Instruct](https://huggingface.co/prithivMLmods/Callisto-OCR3-2B-Instruct) <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
memevis/WL25	memevis	2025-04-06T16:41:14Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-06T16:39:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mattbailey1991/a2c-PandaReachDense-v3	mattbailey1991	2025-04-06T16:40:57Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-04-06T16:35:09Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.15 +/- 0.12 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Bouquets/lora_model	Bouquets	2025-04-06T16:40:21Z	0	0	transformers	[ "transformers", "gguf", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-06T16:13:10Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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kiriyk/seo_qwen-instruct_16bit_8epochs	kiriyk	2025-04-06T16:40:06Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-06T16:35:56Z	--- base_model: Qwen2.5-7B-Instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: kiriyk - License: apache-2.0 - Finetuned from model : 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)
bowilleatyou/b680d07b-08ea-4b0f-9c05-5398a45255a1	bowilleatyou	2025-04-06T16:39:59Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-06T15:57:04Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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Skyfallirk/basik-cats_LoRa	Skyfallirk	2025-04-06T16:39:48Z	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-04-06T16:39:38Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: a photo collage in Basik style widget: [] tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - Skyfallirk/basik-cats_LoRa <Gallery /> ## Model description These are Skyfallirk/basik-cats_LoRa LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo collage in Basik style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](Skyfallirk/basik-cats_LoRa/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
gaunernst/gemma-3-4b-it-qat-autoawq	gaunernst	2025-04-06T16:37:14Z	0	0	null	[ "safetensors", "gemma3", "gemma", "google", "image-text-to-text", "conversational", "arxiv:1905.07830", "arxiv:1905.10044", "arxiv:1911.11641", "arxiv:1904.09728", "arxiv:1705.03551", "arxiv:1911.01547", "arxiv:1907.10641", "arxiv:1903.00161", "arxiv:2009.03300", "arxiv:2304.06364", "arxiv:2103.03874", "arxiv:2110.14168", "arxiv:2311.12022", "arxiv:2108.07732", "arxiv:2107.03374", "arxiv:2210.03057", "arxiv:2106.03193", "arxiv:1910.11856", "arxiv:2502.12404", "arxiv:2502.21228", "arxiv:2404.16816", "arxiv:2104.12756", "arxiv:2311.16502", "arxiv:2203.10244", "arxiv:2404.12390", "arxiv:1810.12440", "arxiv:1908.02660", "arxiv:2312.11805", "base_model:google/gemma-3-4b-it", "base_model:quantized:google/gemma-3-4b-it", "license:gemma", "4-bit", "awq", "region:us" ]	image-text-to-text	2025-04-06T16:27:33Z	--- base_model: google/gemma-3-4b-it license: gemma tags: - gemma3 - gemma - google pipeline_tag: image-text-to-text --- # Gemma 3 4B Instruction-tuned QAT AutoAWQ This checkpoint was converted from https://huggingface.co/google/gemma-3-4b-it-qat-q4_0-gguf to AutoAWQ format and BF16 dtype (hence, not lossess). The vision tower was transplanted from https://huggingface.co/google/gemma-3-4b-it. Below is the original model card. # Gemma 3 model card Model Page: [Gemma](https://ai.google.dev/gemma/docs/core) > [!Note] > This repository corresponds to the 4B instruction-tuned version of the Gemma 3 model in GGUF format using Quantization Aware Training (QAT). > The GGUF corresponds to Q4_0 quantization. > > Thanks to QAT, the model is able to preserve similar quality as `bfloat16` while significantly reducing the memory requirements > to load the model. > > You can find the half-precision version [here](https://huggingface.co/google/gemma-3-4b-it). Resources and Technical Documentation: * [Gemma 3 Technical Report][g3-tech-report] * [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma3] Terms of Use: [Terms][terms] Authors: Google DeepMind ## Model Information Summary description and brief definition of inputs and outputs. ### Description Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. ### Inputs and outputs - Input: - Text string, such as a question, a prompt, or a document to be summarized - Images, normalized to 896 x 896 resolution and encoded to 256 tokens each - Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and 32K tokens for the 1B size - Output: - Generated text in response to the input, such as an answer to a question, analysis of image content, or a summary of a document - Total output context of 8192 tokens ### Usage Below, there are some code snippets on how to get quickly started with running the model. llama.cpp (text-only) ```sh ./llama-cli -hf google/gemma-3-4b-it-qat-q4_0-gguf -p "Write a poem about the Kraken." ``` llama.cpp (image input) ```sh wget https://github.com/bebechien/gemma/blob/main/surprise.png?raw=true -O ~/Downloads/surprise.png ./llama-gemma3-cli -hf google/gemma-3-4b-it-qat-q4_0-gguf -p "Describe this image." --image ~/Downloads/surprise.png ``` ollama (text only) Using GGUFs with Ollama via Hugging Face does not support image inputs at the moment. Please check the [docs on running gated repositories](https://huggingface.co/docs/hub/en/ollama#run-private-ggufs-from-the-hugging-face-hub). ```sh ollama run hf.co/google/gemma-3-4b-it-qat-q4_0-gguf ``` ### Citation ```none @article{gemma_2025, title={Gemma 3}, url={https://goo.gle/Gemma3Report}, publisher={Kaggle}, author={Gemma Team}, year={2025} } ``` ## Model Data Data used for model training and how the data was processed. ### Training Dataset These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 14 trillion tokens, the 12B model was trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and 1B with 2 trillion tokens. Here are the key components: - Web Documents: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. The training dataset includes content in over 140 languages. - Code: Exposing the model to code helps it to learn the syntax and patterns of programming languages, which improves its ability to generate code and understand code-related questions. - Mathematics: Training on mathematical text helps the model learn logical reasoning, symbolic representation, and to address mathematical queries. - Images: A wide range of images enables the model to perform image analysis and visual data extraction tasks. The combination of these diverse data sources is crucial for training a powerful multimodal model that can handle a wide variety of different tasks and data formats. ### Data Preprocessing Here are the key data cleaning and filtering methods applied to the training data: - CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process to ensure the exclusion of harmful and illegal content. - Sensitive Data Filtering: As part of making Gemma pre-trained models safe and reliable, automated techniques were used to filter out certain personal information and other sensitive data from training sets. - Additional methods: Filtering based on content quality and safety in line with [our policies][safety-policies]. ## Implementation Information Details about the model internals. ### Hardware Gemma was trained using [Tensor Processing Unit (TPU)][tpu] hardware (TPUv4p, TPUv5p and TPUv5e). Training vision-language models (VLMS) requires significant computational power. TPUs, designed specifically for matrix operations common in machine learning, offer several advantages in this domain: - Performance: TPUs are specifically designed to handle the massive computations involved in training VLMs. They can speed up training considerably compared to CPUs. - Memory: TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training. This can lead to better model quality. - Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for handling the growing complexity of large foundation models. You can distribute training across multiple TPU devices for faster and more efficient processing. - Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective solution for training large models compared to CPU-based infrastructure, especially when considering the time and resources saved due to faster training. - These advantages are aligned with [Google's commitments to operate sustainably][sustainability]. ### Software Training was done using [JAX][jax] and [ML Pathways][ml-pathways]. JAX allows researchers to take advantage of the latest generation of hardware, including TPUs, for faster and more efficient training of large models. ML Pathways is Google's latest effort to build artificially intelligent systems capable of generalizing across multiple tasks. This is specially suitable for foundation models, including large language models like these ones. Together, JAX and ML Pathways are used as described in the [paper about the Gemini family of models][gemini-2-paper]; "the 'single controller' programming model of Jax and Pathways allows a single Python process to orchestrate the entire training run, dramatically simplifying the development workflow." ## Evaluation > [!Note] > The evaluation in this section correspond to the original checkpoint, not the QAT checkpoint. > Model evaluation metrics and results. ### Benchmark Results These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation: #### Reasoning and factuality \| Benchmark \| Metric \| Gemma 3 PT 1B \| Gemma 3 PT 4B \| Gemma 3 PT 12B \| Gemma 3 PT 27B \| \| ------------------------------ \|----------------\|:--------------:\|:-------------:\|:--------------:\|:--------------:\| \| [HellaSwag][hellaswag] \| 10-shot \| 62.3 \| 77.2 \| 84.2 \| 85.6 \| \| [BoolQ][boolq] \| 0-shot \| 63.2 \| 72.3 \| 78.8 \| 82.4 \| \| [PIQA][piqa] \| 0-shot \| 73.8 \| 79.6 \| 81.8 \| 83.3 \| \| [SocialIQA][socialiqa] \| 0-shot \| 48.9 \| 51.9 \| 53.4 \| 54.9 \| \| [TriviaQA][triviaqa] \| 5-shot \| 39.8 \| 65.8 \| 78.2 \| 85.5 \| \| [Natural Questions][naturalq] \| 5-shot \| 9.48 \| 20.0 \| 31.4 \| 36.1 \| \| [ARC-c][arc] \| 25-shot \| 38.4 \| 56.2 \| 68.9 \| 70.6 \| \| [ARC-e][arc] \| 0-shot \| 73.0 \| 82.4 \| 88.3 \| 89.0 \| \| [WinoGrande][winogrande] \| 5-shot \| 58.2 \| 64.7 \| 74.3 \| 78.8 \| \| [BIG-Bench Hard][bbh] \| few-shot \| 28.4 \| 50.9 \| 72.6 \| 77.7 \| \| [DROP][drop] \| 1-shot \| 42.4 \| 60.1 \| 72.2 \| 77.2 \| [hellaswag]: https://arxiv.org/abs/1905.07830 [boolq]: https://arxiv.org/abs/1905.10044 [piqa]: https://arxiv.org/abs/1911.11641 [socialiqa]: https://arxiv.org/abs/1904.09728 [triviaqa]: https://arxiv.org/abs/1705.03551 [naturalq]: https://github.com/google-research-datasets/natural-questions [arc]: https://arxiv.org/abs/1911.01547 [winogrande]: https://arxiv.org/abs/1907.10641 [bbh]: https://paperswithcode.com/dataset/bbh [drop]: https://arxiv.org/abs/1903.00161 #### STEM and code \| Benchmark \| Metric \| Gemma 3 PT 4B \| Gemma 3 PT 12B \| Gemma 3 PT 27B \| \| ------------------------------ \|----------------\|:-------------:\|:--------------:\|:--------------:\| \| [MMLU][mmlu] \| 5-shot \| 59.6 \| 74.5 \| 78.6 \| \| [MMLU][mmlu] (Pro COT) \| 5-shot \| 29.2 \| 45.3 \| 52.2 \| \| [AGIEval][agieval] \| 3-5-shot \| 42.1 \| 57.4 \| 66.2 \| \| [MATH][math] \| 4-shot \| 24.2 \| 43.3 \| 50.0 \| \| [GSM8K][gsm8k] \| 8-shot \| 38.4 \| 71.0 \| 82.6 \| \| [GPQA][gpqa] \| 5-shot \| 15.0 \| 25.4 \| 24.3 \| \| [MBPP][mbpp] \| 3-shot \| 46.0 \| 60.4 \| 65.6 \| \| [HumanEval][humaneval] \| 0-shot \| 36.0 \| 45.7 \| 48.8 \| [mmlu]: https://arxiv.org/abs/2009.03300 [agieval]: https://arxiv.org/abs/2304.06364 [math]: https://arxiv.org/abs/2103.03874 [gsm8k]: https://arxiv.org/abs/2110.14168 [gpqa]: https://arxiv.org/abs/2311.12022 [mbpp]: https://arxiv.org/abs/2108.07732 [humaneval]: https://arxiv.org/abs/2107.03374 #### Multilingual \| Benchmark \| Gemma 3 PT 1B \| Gemma 3 PT 4B \| Gemma 3 PT 12B \| Gemma 3 PT 27B \| \| ------------------------------------ \|:-------------:\|:-------------:\|:--------------:\|:--------------:\| \| [MGSM][mgsm] \| 2.04 \| 34.7 \| 64.3 \| 74.3 \| \| [Global-MMLU-Lite][global-mmlu-lite] \| 24.9 \| 57.0 \| 69.4 \| 75.7 \| \| [WMT24++][wmt24pp] (ChrF) \| 36.7 \| 48.4 \| 53.9 \| 55.7 \| \| [FloRes][flores] \| 29.5 \| 39.2 \| 46.0 \| 48.8 \| \| [XQuAD][xquad] (all) \| 43.9 \| 68.0 \| 74.5 \| 76.8 \| \| [ECLeKTic][eclektic] \| 4.69 \| 11.0 \| 17.2 \| 24.4 \| \| [IndicGenBench][indicgenbench] \| 41.4 \| 57.2 \| 61.7 \| 63.4 \| [mgsm]: https://arxiv.org/abs/2210.03057 [flores]: https://arxiv.org/abs/2106.03193 [xquad]: https://arxiv.org/abs/1910.11856v3 [global-mmlu-lite]: https://huggingface.co/datasets/CohereForAI/Global-MMLU-Lite [wmt24pp]: https://arxiv.org/abs/2502.12404v1 [eclektic]: https://arxiv.org/abs/2502.21228 [indicgenbench]: https://arxiv.org/abs/2404.16816 #### Multimodal \| Benchmark \| Gemma 3 PT 4B \| Gemma 3 PT 12B \| Gemma 3 PT 27B \| \| ------------------------------ \|:-------------:\|:--------------:\|:--------------:\| \| [COCOcap][coco-cap] \| 102 \| 111 \| 116 \| \| [DocVQA][docvqa] (val) \| 72.8 \| 82.3 \| 85.6 \| \| [InfoVQA][info-vqa] (val) \| 44.1 \| 54.8 \| 59.4 \| \| [MMMU][mmmu] (pt) \| 39.2 \| 50.3 \| 56.1 \| \| [TextVQA][textvqa] (val) \| 58.9 \| 66.5 \| 68.6 \| \| [RealWorldQA][realworldqa] \| 45.5 \| 52.2 \| 53.9 \| \| [ReMI][remi] \| 27.3 \| 38.5 \| 44.8 \| \| [AI2D][ai2d] \| 63.2 \| 75.2 \| 79.0 \| \| [ChartQA][chartqa] \| 63.6 \| 74.7 \| 76.3 \| \| [VQAv2][vqav2] \| 63.9 \| 71.2 \| 72.9 \| \| [BLINK][blinkvqa] \| 38.0 \| 35.9 \| 39.6 \| \| [OKVQA][okvqa] \| 51.0 \| 58.7 \| 60.2 \| \| [TallyQA][tallyqa] \| 42.5 \| 51.8 \| 54.3 \| \| [SpatialSense VQA][ss-vqa] \| 50.9 \| 60.0 \| 59.4 \| \| [CountBenchQA][countbenchqa] \| 26.1 \| 17.8 \| 68.0 \| [coco-cap]: https://cocodataset.org/#home [docvqa]: https://www.docvqa.org/ [info-vqa]: https://arxiv.org/abs/2104.12756 [mmmu]: https://arxiv.org/abs/2311.16502 [textvqa]: https://textvqa.org/ [realworldqa]: https://paperswithcode.com/dataset/realworldqa [remi]: https://arxiv.org/html/2406.09175v1 [ai2d]: https://allenai.org/data/diagrams [chartqa]: https://arxiv.org/abs/2203.10244 [vqav2]: https://visualqa.org/index.html [blinkvqa]: https://arxiv.org/abs/2404.12390 [okvqa]: https://okvqa.allenai.org/ [tallyqa]: https://arxiv.org/abs/1810.12440 [ss-vqa]: https://arxiv.org/abs/1908.02660 [countbenchqa]: https://github.com/google-research/big_vision/blob/main/big_vision/datasets/countbenchqa/ ## Ethics and Safety Ethics and safety evaluation approach and results. ### Evaluation Approach Our evaluation methods include structured evaluations and internal red-teaming testing of relevant content policies. Red-teaming was conducted by a number of different teams, each with different goals and human evaluation metrics. These models were evaluated against a number of different categories relevant to ethics and safety, including: - Child Safety: Evaluation of text-to-text and image to text prompts covering child safety policies, including child sexual abuse and exploitation. - Content Safety: Evaluation of text-to-text and image to text prompts covering safety policies including, harassment, violence and gore, and hate speech. - Representational Harms: Evaluation of text-to-text and image to text prompts covering safety policies including bias, stereotyping, and harmful associations or inaccuracies. In addition to development level evaluations, we conduct "assurance evaluations" which are our 'arms-length' internal evaluations for responsibility governance decision making. They are conducted separately from the model development team, to inform decision making about release. High level findings are fed back to the model team, but prompt sets are held-out to prevent overfitting and preserve the results' ability to inform decision making. Assurance evaluation results are reported to our Responsibility & Safety Council as part of release review. ### Evaluation Results For all areas of safety testing, we saw major improvements in the categories of child safety, content safety, and representational harms relative to previous Gemma models. All testing was conducted without safety filters to evaluate the model capabilities and behaviors. For both text-to-text and image-to-text, and across all model sizes, the model produced minimal policy violations, and showed significant improvements over previous Gemma models' performance with respect to ungrounded inferences. A limitation of our evaluations was they included only English language prompts. ## Usage and Limitations These models have certain limitations that users should be aware of. ### Intended Usage Open vision-language models (VLMs) models have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development. - Content Creation and Communication - Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. - Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications. - Text Summarization: Generate concise summaries of a text corpus, research papers, or reports. - Image Data Extraction: These models can be used to extract, interpret, and summarize visual data for text communications. - Research and Education - Natural Language Processing (NLP) and VLM Research: These models can serve as a foundation for researchers to experiment with VLM and NLP techniques, develop algorithms, and contribute to the advancement of the field. - Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice. - Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics. ### Limitations - Training Data - The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses. - The scope of the training dataset determines the subject areas the model can handle effectively. - Context and Task Complexity - Models are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. - A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point). - Language Ambiguity and Nuance - Natural language is inherently complex. Models might struggle to grasp subtle nuances, sarcasm, or figurative language. - Factual Accuracy - Models generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements. - Common Sense - Models rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations. ### Ethical Considerations and Risks The development of vision-language models (VLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following: - Bias and Fairness - VLMs trained on large-scale, real-world text and image data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card. - Misinformation and Misuse - VLMs can be misused to generate text that is false, misleading, or harmful. - Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit][rai-toolkit]. - Transparency and Accountability: - This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. - A responsibly developed open model offers the opportunity to share innovation by making VLM technology accessible to developers and researchers across the AI ecosystem. Risks identified and mitigations: - Perpetuation of biases: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases. - Generation of harmful content: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases. - Misuse for malicious purposes: Technical limitations and developer and end-user education can help mitigate against malicious applications of VLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy][prohibited-use]. - Privacy violations: Models were trained on data filtered for removal of certain personal information and other sensitive data. Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques. ### Benefits At the time of release, this family of models provides high-performance open vision-language model implementations designed from the ground up for responsible AI development compared to similarly sized models. Using the benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives. [g3-tech-report]: https://goo.gle/Gemma3Report [rai-toolkit]: https://ai.google.dev/responsible [kaggle-gemma]: https://www.kaggle.com/models/google/gemma-3 [vertex-mg-gemma3]: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/gemma3 [terms]: https://ai.google.dev/gemma/terms [safety-policies]: https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf [prohibited-use]: https://ai.google.dev/gemma/prohibited_use_policy [tpu]: https://cloud.google.com/tpu/docs/intro-to-tpu [sustainability]: https://sustainability.google/operating-sustainably/ [jax]: https://github.com/jax-ml/jax [ml-pathways]: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ [sustainability]: https://sustainability.google/operating-sustainably/ [gemini-2-paper]: https://arxiv.org/abs/2312.11805
Shaund91/dolphin-2.5-mixtral-8x7b-mlx-6Bit	Shaund91	2025-04-06T16:37:10Z	0	0	mlx	[ "mlx", "safetensors", "mixtral", "en", "dataset:ehartford/dolphin", "dataset:jondurbin/airoboros-2.2.1", "dataset:ehartford/dolphin-coder", "dataset:migtissera/Synthia-v1.3", "dataset:teknium/openhermes", "dataset:ise-uiuc/Magicoder-OSS-Instruct-75K", "dataset:ise-uiuc/Magicoder-Evol-Instruct-110K", "dataset:LDJnr/Pure-Dove", "base_model:cognitivecomputations/dolphin-2.5-mixtral-8x7b", "base_model:quantized:cognitivecomputations/dolphin-2.5-mixtral-8x7b", "license:apache-2.0", "6-bit", "region:us" ]	null	2025-04-06T16:35:17Z	--- datasets: - ehartford/dolphin - jondurbin/airoboros-2.2.1 - ehartford/dolphin-coder - migtissera/Synthia-v1.3 - teknium/openhermes - ise-uiuc/Magicoder-OSS-Instruct-75K - ise-uiuc/Magicoder-Evol-Instruct-110K - LDJnr/Pure-Dove language: - en license: apache-2.0 base_model: cognitivecomputations/dolphin-2.5-mixtral-8x7b tags: - mlx --- # Shaund91/dolphin-2.5-mixtral-8x7b-mlx-6Bit The Model [Shaund91/dolphin-2.5-mixtral-8x7b-mlx-6Bit](https://huggingface.co/Shaund91/dolphin-2.5-mixtral-8x7b-mlx-6Bit) was converted to MLX format from [cognitivecomputations/dolphin-2.5-mixtral-8x7b](https://huggingface.co/cognitivecomputations/dolphin-2.5-mixtral-8x7b) using mlx-lm version 0.22.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("Shaund91/dolphin-2.5-mixtral-8x7b-mlx-6Bit") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
aristidescc/dqn-SpaceInvadersNoFrameskip-v4	aristidescc	2025-04-06T16:36:00Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-04-06T16:34:31Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 637.50 +/- 189.11 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib SBX (SB3 + Jax): https://github.com/araffin/sbx Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga aristidescc -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga aristidescc -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga aristidescc ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 200000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
memevis/WL28	memevis	2025-04-06T16:35:33Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-06T16:33:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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lerkasoo/joanna_style_LoRA	lerkasoo	2025-04-06T16:35:15Z	2	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-04-04T22:53:47Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: illustration in Joanna Quinn's style widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - lerkasoo/joanna_style_LoRA <Gallery /> ## Model description These are lerkasoo/joanna_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use illustration in Joanna Quinn's style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](lerkasoo/joanna_style_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
dgambettaphd/M_llm3_gen0_run0_W_doc1000_synt64_FTP	dgambettaphd	2025-04-06T16:34:22Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-04-06T16:31:59Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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CvLelouch/model	CvLelouch	2025-04-06T16:30:55Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-06T16:28:31Z	--- base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: CvLelouch - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
alperenunlu/yolov1	alperenunlu	2025-04-06T16:30:25Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-06T16:28:25Z	--- license: apache-2.0 ---
TOMFORD79/ImKing_v1_7	TOMFORD79	2025-04-06T16:27:59Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-04-06T15:25:11Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
Qwen/Qwen2.5-VL-32B-Instruct-AWQ	Qwen	2025-04-06T16:25:43Z	10,067	27	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "multimodal", "conversational", "en", "arxiv:2309.00071", "arxiv:2502.13923", "base_model:Qwen/Qwen2.5-VL-32B-Instruct", "base_model:quantized:Qwen/Qwen2.5-VL-32B-Instruct", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	image-text-to-text	2025-03-26T12:20:48Z	--- license: apache-2.0 language: - en pipeline_tag: image-text-to-text tags: - multimodal library_name: transformers base_model: - Qwen/Qwen2.5-VL-32B-Instruct --- # Qwen2.5-VL-32B-Instruct-AWQ <a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/> </a> ## Latest Updates: In addition to the original formula, we have further enhanced Qwen2.5-VL-32B's mathematical and problem-solving abilities through reinforcement learning. This has also significantly improved the model's subjective user experience, with response styles adjusted to better align with human preferences. Particularly for objective queries such as mathematics, logical reasoning, and knowledge-based Q&A, the level of detail in responses and the clarity of formatting have been noticeably enhanced. ## Introduction In the past five months since Qwen2-VL’s release, numerous developers have built new models on the Qwen2-VL vision-language models, providing us with valuable feedback. During this period, we focused on building more useful vision-language models. Today, we are excited to introduce the latest addition to the Qwen family: Qwen2.5-VL. #### Key Enhancements: * Understand things visually: Qwen2.5-VL is not only proficient in recognizing common objects such as flowers, birds, fish, and insects, but it is highly capable of analyzing texts, charts, icons, graphics, and layouts within images. * Being agentic: Qwen2.5-VL directly plays as a visual agent that can reason and dynamically direct tools, which is capable of computer use and phone use. * Understanding long videos and capturing events: Qwen2.5-VL can comprehend videos of over 1 hour, and this time it has a new ability of cpaturing event by pinpointing the relevant video segments. * Capable of visual localization in different formats: Qwen2.5-VL can accurately localize objects in an image by generating bounding boxes or points, and it can provide stable JSON outputs for coordinates and attributes. * Generating structured outputs: for data like scans of invoices, forms, tables, etc. Qwen2.5-VL supports structured outputs of their contents, benefiting usages in finance, commerce, etc. #### Model Architecture Updates: * Dynamic Resolution and Frame Rate Training for Video Understanding: We extend dynamic resolution to the temporal dimension by adopting dynamic FPS sampling, enabling the model to comprehend videos at various sampling rates. Accordingly, we update mRoPE in the time dimension with IDs and absolute time alignment, enabling the model to learn temporal sequence and speed, and ultimately acquire the ability to pinpoint specific moments. <p align="center"> <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2.5-VL/qwen2.5vl_arc.jpeg" width="80%"/> <p> * Streamlined and Efficient Vision Encoder We enhance both training and inference speeds by strategically implementing window attention into the ViT. The ViT architecture is further optimized with SwiGLU and RMSNorm, aligning it with the structure of the Qwen2.5 LLM. We have three models with 3, 7 and 72 billion parameters. This repository contains the quantized instruction-tuned 32B Qwen2.5-VL model. For more information, visit our [Blog](https://qwenlm.github.io/blog/qwen2.5-vl/) and [GitHub](https://github.com/QwenLM/Qwen2.5-VL). ## Evaluation \| Model \| MMMU \| DocVQA_VAL \| MMBench_DEV_EN \| MathVista_MINI \| \|---------------------------\|--------------------\|------------\|------------------------\|----------------\| \| Qwen2.5-VL-32B-Instruct \| 70.0 \| 93.9107 \| 87.3 \| 74.7 \| \| Qwen2.5-VL-32B-Instruct-AWQ \| 67.8 \| 94.1489 \| 86.9 \| 73.6 \| ## Requirements The code of Qwen2.5-VL has been in the latest Hugging face transformers and we advise you to build from source with command: ``` pip install git+https://github.com/huggingface/transformers accelerate ``` or you might encounter the following error: ``` KeyError: 'qwen2_5_vl' ``` ## Quickstart Below, we provide simple examples to show how to use Qwen2.5-VL with 🤖 ModelScope and 🤗 Transformers. The code of Qwen2.5-VL has been in the latest Hugging face transformers and we advise you to build from source with command: ``` pip install git+https://github.com/huggingface/transformers accelerate ``` or you might encounter the following error: ``` KeyError: 'qwen2_5_vl' ``` We offer a toolkit to help you handle various types of visual input more conveniently, as if you were using an API. This includes base64, URLs, and interleaved images and videos. You can install it using the following command: ```bash # It's highly recommanded to use `[decord]` feature for faster video loading. pip install qwen-vl-utils[decord]==0.0.8 ``` If you are not using Linux, you might not be able to install `decord` from PyPI. In that case, you can use `pip install qwen-vl-utils` which will fall back to using torchvision for video processing. However, you can still [install decord from source](https://github.com/dmlc/decord?tab=readme-ov-file#install-from-source) to get decord used when loading video. ### Using 🤗 Transformers to Chat Here we show a code snippet to show you how to use the chat model with `transformers` and `qwen_vl_utils`: ```python from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor from qwen_vl_utils import process_vision_info # default: Load the model on the available device(s) model = Qwen2_5_VLForConditionalGeneration.from_pretrained( "Qwen/Qwen2.5-VL-32B-Instruct-AWQ", torch_dtype="auto", device_map="auto" ) # We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios. # model = Qwen2_5_VLForConditionalGeneration.from_pretrained( # "Qwen/Qwen2.5-VL-32B-Instruct-AWQ", # torch_dtype=torch.bfloat16, # attn_implementation="flash_attention_2", # device_map="auto", # ) # default processer processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-32B-Instruct-AWQ") # The default range for the number of visual tokens per image in the model is 4-16384. # You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost. # min_pixels = 2562828 # max_pixels = 12802828 # processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-32B-Instruct-AWQ", min_pixels=min_pixels, max_pixels=max_pixels) messages = [ { "role": "user", "content": [ { "type": "image", "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", }, {"type": "text", "text": "Describe this image."}, ], } ] # Preparation for inference text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs, video_inputs = process_vision_info(messages) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt", ) inputs = inputs.to("cuda") # Inference: Generation of the output generated_ids = model.generate(inputs, max_new_tokens=128) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text) ``` <details> <summary>Multi image inference</summary> ```python # Messages containing multiple images and a text query messages = [ { "role": "user", "content": [ {"type": "image", "image": "file:///path/to/image1.jpg"}, {"type": "image", "image": "file:///path/to/image2.jpg"}, {"type": "text", "text": "Identify the similarities between these images."}, ], } ] # Preparation for inference text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs, video_inputs = process_vision_info(messages) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt", ) inputs = inputs.to("cuda") # Inference generated_ids = model.generate(inputs, max_new_tokens=128) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text) ``` </details> <details> <summary>Video inference</summary> ```python # Messages containing a images list as a video and a text query messages = [ { "role": "user", "content": [ { "type": "video", "video": [ "file:///path/to/frame1.jpg", "file:///path/to/frame2.jpg", "file:///path/to/frame3.jpg", "file:///path/to/frame4.jpg", ], }, {"type": "text", "text": "Describe this video."}, ], } ] # Messages containing a local video path and a text query messages = [ { "role": "user", "content": [ { "type": "video", "video": "file:///path/to/video1.mp4", "max_pixels": 360 * 420, "fps": 1.0, }, {"type": "text", "text": "Describe this video."}, ], } ] # Messages containing a video url and a text query messages = [ { "role": "user", "content": [ { "type": "video", "video": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-VL/space_woaudio.mp4", }, {"type": "text", "text": "Describe this video."}, ], } ] #In Qwen 2.5 VL, frame rate information is also input into the model to align with absolute time. # Preparation for inference text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs, video_inputs, video_kwargs = process_vision_info(messages, return_video_kwargs=True) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, fps=fps, padding=True, return_tensors="pt", video_kwargs, ) inputs = inputs.to("cuda") # Inference generated_ids = model.generate(inputs, max_new_tokens=128) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text) ``` Video URL compatibility largely depends on the third-party library version. The details are in the table below. change the backend by `FORCE_QWENVL_VIDEO_READER=torchvision` or `FORCE_QWENVL_VIDEO_READER=decord` if you prefer not to use the default one. \| Backend \| HTTP \| HTTPS \| \|-------------\|------\|-------\| \| torchvision >= 0.19.0 \| ✅ \| ✅ \| \| torchvision < 0.19.0 \| ❌ \| ❌ \| \| decord \| ✅ \| ❌ \| </details> <details> <summary>Batch inference</summary> ```python # Sample messages for batch inference messages1 = [ { "role": "user", "content": [ {"type": "image", "image": "file:///path/to/image1.jpg"}, {"type": "image", "image": "file:///path/to/image2.jpg"}, {"type": "text", "text": "What are the common elements in these pictures?"}, ], } ] messages2 = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Who are you?"}, ] # Combine messages for batch processing messages = [messages1, messages2] # Preparation for batch inference texts = [ processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True) for msg in messages ] image_inputs, video_inputs = process_vision_info(messages) inputs = processor( text=texts, images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt", ) inputs = inputs.to("cuda") # Batch Inference generated_ids = model.generate(*inputs, max_new_tokens=128) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_texts = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_texts) ``` </details> ### 🤖 ModelScope We strongly advise users especially those in mainland China to use ModelScope. `snapshot_download` can help you solve issues concerning downloading checkpoints. ### More Usage Tips For input images, we support local files, base64, and URLs. For videos, we currently only support local files. ```python # You can directly insert a local file path, a URL, or a base64-encoded image into the position where you want in the text. ## Local file path messages = [ { "role": "user", "content": [ {"type": "image", "image": "file:///path/to/your/image.jpg"}, {"type": "text", "text": "Describe this image."}, ], } ] ## Image URL messages = [ { "role": "user", "content": [ {"type": "image", "image": "http://path/to/your/image.jpg"}, {"type": "text", "text": "Describe this image."}, ], } ] ## Base64 encoded image messages = [ { "role": "user", "content": [ {"type": "image", "image": "data:image;base64,/9j/..."}, {"type": "text", "text": "Describe this image."}, ], } ] ``` #### Image Resolution for performance boost The model supports a wide range of resolution inputs. By default, it uses the native resolution for input, but higher resolutions can enhance performance at the cost of more computation. Users can set the minimum and maximum number of pixels to achieve an optimal configuration for their needs, such as a token count range of 256-1280, to balance speed and memory usage. ```python min_pixels = 256 28 * 28 max_pixels = 1280 * 28 * 28 processor = AutoProcessor.from_pretrained( "Qwen/Qwen2.5-VL-32B-Instruct-AWQ", min_pixels=min_pixels, max_pixels=max_pixels ) ``` Besides, We provide two methods for fine-grained control over the image size input to the model: 1. Define min_pixels and max_pixels: Images will be resized to maintain their aspect ratio within the range of min_pixels and max_pixels. 2. Specify exact dimensions: Directly set `resized_height` and `resized_width`. These values will be rounded to the nearest multiple of 28. ```python # min_pixels and max_pixels messages = [ { "role": "user", "content": [ { "type": "image", "image": "file:///path/to/your/image.jpg", "resized_height": 280, "resized_width": 420, }, {"type": "text", "text": "Describe this image."}, ], } ] # resized_height and resized_width messages = [ { "role": "user", "content": [ { "type": "image", "image": "file:///path/to/your/image.jpg", "min_pixels": 50176, "max_pixels": 50176, }, {"type": "text", "text": "Describe this image."}, ], } ] ``` ### Processing Long Texts The current `config.json` is set for context length up to 32,768 tokens. To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts. For supported frameworks, you could add the following to `config.json` to enable YaRN: { ..., "type": "yarn", "mrope_section": [ 16, 24, 24 ], "factor": 4, "original_max_position_embeddings": 32768 } However, it should be noted that this method has a significant impact on the performance of temporal and spatial localization tasks, and is therefore not recommended for use. At the same time, for long video inputs, since MRoPE itself is more economical with ids, the max_position_embeddings can be directly modified to a larger value, such as 64k. ## Citation If you find our work helpful, feel free to give us a cite. ``` @article{Qwen2.5-VL, title={Qwen2.5-VL Technical Report}, author={Bai, Shuai and Chen, Keqin and Liu, Xuejing and Wang, Jialin and Ge, Wenbin and Song, Sibo and Dang, Kai and Wang, Peng and Wang, Shijie and Tang, Jun and Zhong, Humen and Zhu, Yuanzhi and Yang, Mingkun and Li, Zhaohai and Wan, Jianqiang and Wang, Pengfei and Ding, Wei and Fu, Zheren and Xu, Yiheng and Ye, Jiabo and Zhang, Xi and Xie, Tianbao and Cheng, Zesen and Zhang, Hang and Yang, Zhibo and Xu, Haiyang and Lin, Junyang}, journal={arXiv preprint arXiv:2502.13923}, year={2025} } ```
Qwen/Qwen2.5-VL-3B-Instruct	Qwen	2025-04-06T16:23:42Z	1,187,664	313	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "multimodal", "conversational", "en", "arxiv:2309.00071", "arxiv:2409.12191", "arxiv:2308.12966", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-01-26T09:25:35Z	--- license_name: qwen-research license_link: https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct/blob/main/LICENSE language: - en pipeline_tag: image-text-to-text tags: - multimodal library_name: transformers --- # Qwen2.5-VL-3B-Instruct <a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/> </a> ## Introduction In the past five months since Qwen2-VL’s release, numerous developers have built new models on the Qwen2-VL vision-language models, providing us with valuable feedback. During this period, we focused on building more useful vision-language models. Today, we are excited to introduce the latest addition to the Qwen family: Qwen2.5-VL. #### Key Enhancements: * Understand things visually: Qwen2.5-VL is not only proficient in recognizing common objects such as flowers, birds, fish, and insects, but it is highly capable of analyzing texts, charts, icons, graphics, and layouts within images. * Being agentic: Qwen2.5-VL directly plays as a visual agent that can reason and dynamically direct tools, which is capable of computer use and phone use. * Understanding long videos and capturing events: Qwen2.5-VL can comprehend videos of over 1 hour, and this time it has a new ability of cpaturing event by pinpointing the relevant video segments. * Capable of visual localization in different formats: Qwen2.5-VL can accurately localize objects in an image by generating bounding boxes or points, and it can provide stable JSON outputs for coordinates and attributes. * Generating structured outputs: for data like scans of invoices, forms, tables, etc. Qwen2.5-VL supports structured outputs of their contents, benefiting usages in finance, commerce, etc. #### Model Architecture Updates: * Dynamic Resolution and Frame Rate Training for Video Understanding: We extend dynamic resolution to the temporal dimension by adopting dynamic FPS sampling, enabling the model to comprehend videos at various sampling rates. Accordingly, we update mRoPE in the time dimension with IDs and absolute time alignment, enabling the model to learn temporal sequence and speed, and ultimately acquire the ability to pinpoint specific moments. <p align="center"> <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2.5-VL/qwen2.5vl_arc.jpeg" width="80%"/> <p> * Streamlined and Efficient Vision Encoder We enhance both training and inference speeds by strategically implementing window attention into the ViT. The ViT architecture is further optimized with SwiGLU and RMSNorm, aligning it with the structure of the Qwen2.5 LLM. We have three models with 3, 7 and 72 billion parameters. This repo contains the instruction-tuned 3B Qwen2.5-VL model. For more information, visit our [Blog](https://qwenlm.github.io/blog/qwen2.5-vl/) and [GitHub](https://github.com/QwenLM/Qwen2.5-VL). ## Evaluation ### Image benchmark \| Benchmark \| InternVL2.5-4B \|Qwen2-VL-7B \|Qwen2.5-VL-3B \| \| :--- \| :---: \| :---: \| :---: \| \| MMMU<sub>val</sub> \| 52.3 \| 54.1 \| 53.1\| \| MMMU-Pro<sub>val</sub> \| 32.7 \| 30.5 \| 31.6\| \| AI2D<sub>test</sub> \| 81.4 \| 83.0 \| 81.5 \| \| DocVQA<sub>test</sub> \| 91.6 \| 94.5 \| 93.9 \| \| InfoVQA<sub>test</sub> \| 72.1 \| 76.5 \| 77.1 \| \| TextVQA<sub>val</sub> \| 76.8 \| 84.3 \| 79.3\| \| MMBench-V1.1<sub>test</sub> \| 79.3 \| 80.7 \| 77.6 \| \| MMStar \| 58.3 \| 60.7 \| 55.9 \| \| MathVista<sub>testmini</sub> \| 60.5 \| 58.2 \| 62.3 \| \| MathVision<sub>full</sub> \| 20.9 \| 16.3 \| 21.2 \| ### Video benchmark \| Benchmark \| InternVL2.5-4B \| Qwen2-VL-7B \| Qwen2.5-VL-3B \| \| :--- \| :---: \| :---: \| :---: \| \| MVBench \| 71.6 \| 67.0 \| 67.0 \| \| VideoMME \| 63.6/62.3 \| 69.0/63.3 \| 67.6/61.5 \| \| MLVU \| 48.3 \| - \| 68.2 \| \| LVBench \| - \| - \| 43.3 \| \| MMBench-Video \| 1.73 \| 1.44 \| 1.63 \| \| EgoSchema \| - \| - \| 64.8 \| \| PerceptionTest \| - \| - \| 66.9 \| \| TempCompass \| - \| - \| 64.4 \| \| LongVideoBench \| 55.2 \| 55.6 \| 54.2 \| \| CharadesSTA/mIoU \| - \| - \| 38.8 \| ### Agent benchmark \| Benchmarks \| Qwen2.5-VL-3B \| \|-------------------------\|---------------\| \| ScreenSpot \| 55.5 \| \| ScreenSpot Pro \| 23.9 \| \| AITZ_EM \| 76.9 \| \| Android Control High_EM \| 63.7 \| \| Android Control Low_EM \| 22.2 \| \| AndroidWorld_SR \| 90.8 \| \| MobileMiniWob++_SR \| 67.9 \| ## Requirements The code of Qwen2.5-VL has been in the latest Hugging face transformers and we advise you to build from source with command: ``` pip install git+https://github.com/huggingface/transformers accelerate ``` or you might encounter the following error: ``` KeyError: 'qwen2_5_vl' ``` ## Quickstart Below, we provide simple examples to show how to use Qwen2.5-VL with 🤖 ModelScope and 🤗 Transformers. The code of Qwen2.5-VL has been in the latest Hugging face transformers and we advise you to build from source with command: ``` pip install git+https://github.com/huggingface/transformers accelerate ``` or you might encounter the following error: ``` KeyError: 'qwen2_5_vl' ``` We offer a toolkit to help you handle various types of visual input more conveniently, as if you were using an API. This includes base64, URLs, and interleaved images and videos. You can install it using the following command: ```bash # It's highly recommanded to use `[decord]` feature for faster video loading. pip install qwen-vl-utils[decord]==0.0.8 ``` If you are not using Linux, you might not be able to install `decord` from PyPI. In that case, you can use `pip install qwen-vl-utils` which will fall back to using torchvision for video processing. However, you can still [install decord from source](https://github.com/dmlc/decord?tab=readme-ov-file#install-from-source) to get decord used when loading video. ### Using 🤗 Transformers to Chat Here we show a code snippet to show you how to use the chat model with `transformers` and `qwen_vl_utils`: ```python from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor from qwen_vl_utils import process_vision_info # default: Load the model on the available device(s) model = Qwen2_5_VLForConditionalGeneration.from_pretrained( "Qwen/Qwen2.5-VL-3B-Instruct", torch_dtype="auto", device_map="auto" ) # We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios. # model = Qwen2_5_VLForConditionalGeneration.from_pretrained( # "Qwen/Qwen2.5-VL-3B-Instruct", # torch_dtype=torch.bfloat16, # attn_implementation="flash_attention_2", # device_map="auto", # ) # default processer processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct") # The default range for the number of visual tokens per image in the model is 4-16384. # You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost. # min_pixels = 2562828 # max_pixels = 12802828 # processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels) messages = [ { "role": "user", "content": [ { "type": "image", "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", }, {"type": "text", "text": "Describe this image."}, ], } ] # Preparation for inference text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs, video_inputs = process_vision_info(messages) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt", ) inputs = inputs.to("cuda") # Inference: Generation of the output generated_ids = model.generate(inputs, max_new_tokens=128) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text) ``` <details> <summary>Multi image inference</summary> ```python # Messages containing multiple images and a text query messages = [ { "role": "user", "content": [ {"type": "image", "image": "file:///path/to/image1.jpg"}, {"type": "image", "image": "file:///path/to/image2.jpg"}, {"type": "text", "text": "Identify the similarities between these images."}, ], } ] # Preparation for inference text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs, video_inputs = process_vision_info(messages) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt", ) inputs = inputs.to("cuda") # Inference generated_ids = model.generate(inputs, max_new_tokens=128) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text) ``` </details> <details> <summary>Video inference</summary> ```python # Messages containing a images list as a video and a text query messages = [ { "role": "user", "content": [ { "type": "video", "video": [ "file:///path/to/frame1.jpg", "file:///path/to/frame2.jpg", "file:///path/to/frame3.jpg", "file:///path/to/frame4.jpg", ], }, {"type": "text", "text": "Describe this video."}, ], } ] # Messages containing a local video path and a text query messages = [ { "role": "user", "content": [ { "type": "video", "video": "file:///path/to/video1.mp4", "max_pixels": 360 * 420, "fps": 1.0, }, {"type": "text", "text": "Describe this video."}, ], } ] # Messages containing a video url and a text query messages = [ { "role": "user", "content": [ { "type": "video", "video": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-VL/space_woaudio.mp4", }, {"type": "text", "text": "Describe this video."}, ], } ] #In Qwen 2.5 VL, frame rate information is also input into the model to align with absolute time. # Preparation for inference text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs, video_inputs, video_kwargs = process_vision_info(messages, return_video_kwargs=True) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, fps=fps, padding=True, return_tensors="pt", video_kwargs, ) inputs = inputs.to("cuda") # Inference generated_ids = model.generate(inputs, max_new_tokens=128) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text) ``` Video URL compatibility largely depends on the third-party library version. The details are in the table below. change the backend by `FORCE_QWENVL_VIDEO_READER=torchvision` or `FORCE_QWENVL_VIDEO_READER=decord` if you prefer not to use the default one. \| Backend \| HTTP \| HTTPS \| \|-------------\|------\|-------\| \| torchvision >= 0.19.0 \| ✅ \| ✅ \| \| torchvision < 0.19.0 \| ❌ \| ❌ \| \| decord \| ✅ \| ❌ \| </details> <details> <summary>Batch inference</summary> ```python # Sample messages for batch inference messages1 = [ { "role": "user", "content": [ {"type": "image", "image": "file:///path/to/image1.jpg"}, {"type": "image", "image": "file:///path/to/image2.jpg"}, {"type": "text", "text": "What are the common elements in these pictures?"}, ], } ] messages2 = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Who are you?"}, ] # Combine messages for batch processing messages = [messages1, messages2] # Preparation for batch inference texts = [ processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True) for msg in messages ] image_inputs, video_inputs = process_vision_info(messages) inputs = processor( text=texts, images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt", ) inputs = inputs.to("cuda") # Batch Inference generated_ids = model.generate(*inputs, max_new_tokens=128) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_texts = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_texts) ``` </details> ### 🤖 ModelScope We strongly advise users especially those in mainland China to use ModelScope. `snapshot_download` can help you solve issues concerning downloading checkpoints. ### More Usage Tips For input images, we support local files, base64, and URLs. For videos, we currently only support local files. ```python # You can directly insert a local file path, a URL, or a base64-encoded image into the position where you want in the text. ## Local file path messages = [ { "role": "user", "content": [ {"type": "image", "image": "file:///path/to/your/image.jpg"}, {"type": "text", "text": "Describe this image."}, ], } ] ## Image URL messages = [ { "role": "user", "content": [ {"type": "image", "image": "http://path/to/your/image.jpg"}, {"type": "text", "text": "Describe this image."}, ], } ] ## Base64 encoded image messages = [ { "role": "user", "content": [ {"type": "image", "image": "data:image;base64,/9j/..."}, {"type": "text", "text": "Describe this image."}, ], } ] ``` #### Image Resolution for performance boost The model supports a wide range of resolution inputs. By default, it uses the native resolution for input, but higher resolutions can enhance performance at the cost of more computation. Users can set the minimum and maximum number of pixels to achieve an optimal configuration for their needs, such as a token count range of 256-1280, to balance speed and memory usage. ```python min_pixels = 256 28 * 28 max_pixels = 1280 * 28 * 28 processor = AutoProcessor.from_pretrained( "Qwen/Qwen2.5-VL-3B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels ) ``` Besides, We provide two methods for fine-grained control over the image size input to the model: 1. Define min_pixels and max_pixels: Images will be resized to maintain their aspect ratio within the range of min_pixels and max_pixels. 2. Specify exact dimensions: Directly set `resized_height` and `resized_width`. These values will be rounded to the nearest multiple of 28. ```python # min_pixels and max_pixels messages = [ { "role": "user", "content": [ { "type": "image", "image": "file:///path/to/your/image.jpg", "resized_height": 280, "resized_width": 420, }, {"type": "text", "text": "Describe this image."}, ], } ] # resized_height and resized_width messages = [ { "role": "user", "content": [ { "type": "image", "image": "file:///path/to/your/image.jpg", "min_pixels": 50176, "max_pixels": 50176, }, {"type": "text", "text": "Describe this image."}, ], } ] ``` ### Processing Long Texts The current `config.json` is set for context length up to 32,768 tokens. To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts. For supported frameworks, you could add the following to `config.json` to enable YaRN: ``` { ..., "type": "yarn", "mrope_section": [ 16, 24, 24 ], "factor": 4, "original_max_position_embeddings": 32768 } ``` However, it should be noted that this method has a significant impact on the performance of temporal and spatial localization tasks, and is therefore not recommended for use. At the same time, for long video inputs, since MRoPE itself is more economical with ids, the max_position_embeddings can be directly modified to a larger value, such as 64k. ## Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwen2.5-VL, title = {Qwen2.5-VL}, url = {https://qwenlm.github.io/blog/qwen2.5-vl/}, author = {Qwen Team}, month = {January}, year = {2025} } @article{Qwen2VL, title={Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution}, author={Wang, Peng and Bai, Shuai and Tan, Sinan and Wang, Shijie and Fan, Zhihao and Bai, Jinze and Chen, Keqin and Liu, Xuejing and Wang, Jialin and Ge, Wenbin and Fan, Yang and Dang, Kai and Du, Mengfei and Ren, Xuancheng and Men, Rui and Liu, Dayiheng and Zhou, Chang and Zhou, Jingren and Lin, Junyang}, journal={arXiv preprint arXiv:2409.12191}, year={2024} } @article{Qwen-VL, title={Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond}, author={Bai, Jinze and Bai, Shuai and Yang, Shusheng and Wang, Shijie and Tan, Sinan and Wang, Peng and Lin, Junyang and Zhou, Chang and Zhou, Jingren}, journal={arXiv preprint arXiv:2308.12966}, year={2023} } ```
Qwen/Qwen2.5-VL-7B-Instruct	Qwen	2025-04-06T16:23:01Z	2,145,343	786	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "multimodal", "conversational", "en", "arxiv:2309.00071", "arxiv:2409.12191", "arxiv:2308.12966", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-01-26T09:26:37Z	--- license: apache-2.0 language: - en pipeline_tag: image-text-to-text tags: - multimodal library_name: transformers --- # Qwen2.5-VL-7B-Instruct <a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/> </a> ## Introduction In the past five months since Qwen2-VL’s release, numerous developers have built new models on the Qwen2-VL vision-language models, providing us with valuable feedback. During this period, we focused on building more useful vision-language models. Today, we are excited to introduce the latest addition to the Qwen family: Qwen2.5-VL. #### Key Enhancements: * Understand things visually: Qwen2.5-VL is not only proficient in recognizing common objects such as flowers, birds, fish, and insects, but it is highly capable of analyzing texts, charts, icons, graphics, and layouts within images. * Being agentic: Qwen2.5-VL directly plays as a visual agent that can reason and dynamically direct tools, which is capable of computer use and phone use. * Understanding long videos and capturing events: Qwen2.5-VL can comprehend videos of over 1 hour, and this time it has a new ability of cpaturing event by pinpointing the relevant video segments. * Capable of visual localization in different formats: Qwen2.5-VL can accurately localize objects in an image by generating bounding boxes or points, and it can provide stable JSON outputs for coordinates and attributes. * Generating structured outputs: for data like scans of invoices, forms, tables, etc. Qwen2.5-VL supports structured outputs of their contents, benefiting usages in finance, commerce, etc. #### Model Architecture Updates: * Dynamic Resolution and Frame Rate Training for Video Understanding: We extend dynamic resolution to the temporal dimension by adopting dynamic FPS sampling, enabling the model to comprehend videos at various sampling rates. Accordingly, we update mRoPE in the time dimension with IDs and absolute time alignment, enabling the model to learn temporal sequence and speed, and ultimately acquire the ability to pinpoint specific moments. <p align="center"> <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2.5-VL/qwen2.5vl_arc.jpeg" width="80%"/> <p> * Streamlined and Efficient Vision Encoder We enhance both training and inference speeds by strategically implementing window attention into the ViT. The ViT architecture is further optimized with SwiGLU and RMSNorm, aligning it with the structure of the Qwen2.5 LLM. We have three models with 3, 7 and 72 billion parameters. This repo contains the instruction-tuned 7B Qwen2.5-VL model. For more information, visit our [Blog](https://qwenlm.github.io/blog/qwen2.5-vl/) and [GitHub](https://github.com/QwenLM/Qwen2.5-VL). ## Evaluation ### Image benchmark \| Benchmark \| InternVL2.5-8B \| MiniCPM-o 2.6 \| GPT-4o-mini \| Qwen2-VL-7B \|Qwen2.5-VL-7B \| \| :--- \| :---: \| :---: \| :---: \| :---: \| :---: \| \| MMMU<sub>val</sub> \| 56 \| 50.4 \| 60\| 54.1 \| 58.6\| \| MMMU-Pro<sub>val</sub> \| 34.3 \| - \| 37.6\| 30.5 \| 41.0\| \| DocVQA<sub>test</sub> \| 93 \| 93 \| - \| 94.5 \| 95.7 \| \| InfoVQA<sub>test</sub> \| 77.6 \| - \| - \|76.5 \| 82.6 \| \| ChartQA<sub>test</sub> \| 84.8 \| - \|- \| 83.0 \|87.3 \| \| TextVQA<sub>val</sub> \| 79.1 \| 80.1 \| -\| 84.3 \| 84.9\| \| OCRBench \| 822 \| 852 \| 785 \| 845 \| 864 \| \| CC_OCR \| 57.7 \| \| \| 61.6 \| 77.8\| \| MMStar \| 62.8\| \| \|60.7\| 63.9\| \| MMBench-V1.1-En<sub>test</sub> \| 79.4 \| 78.0 \| 76.0\| 80.7 \| 82.6 \| \| MMT-Bench<sub>test</sub> \| - \| - \| - \|63.7 \|63.6 \| \| MMStar \| 61.5 \| 57.5 \| 54.8 \| 60.7 \|63.9 \| \| MMVet<sub>GPT-4-Turbo</sub> \| 54.2 \| 60.0 \| 66.9 \| 62.0 \| 67.1\| \| HallBench<sub>avg</sub> \| 45.2 \| 48.1 \| 46.1\| 50.6 \| 52.9\| \| MathVista<sub>testmini</sub> \| 58.3 \| 60.6 \| 52.4 \| 58.2 \| 68.2\| \| MathVision \| - \| - \| - \| 16.3 \| 25.07 \| ### Video Benchmarks \| Benchmark \| Qwen2-VL-7B \| Qwen2.5-VL-7B \| \| :--- \| :---: \| :---: \| \| MVBench \| 67.0 \| 69.6 \| \| PerceptionTest<sub>test</sub> \| 66.9 \| 70.5 \| \| Video-MME<sub>wo/w subs</sub> \| 63.3/69.0 \| 65.1/71.6 \| \| LVBench \| \| 45.3 \| \| LongVideoBench \| \| 54.7 \| \| MMBench-Video \| 1.44 \| 1.79 \| \| TempCompass \| \| 71.7 \| \| MLVU \| \| 70.2 \| \| CharadesSTA/mIoU \| 43.6\| ### Agent benchmark \| Benchmarks \| Qwen2.5-VL-7B \| \|-------------------------\|---------------\| \| ScreenSpot \| 84.7 \| \| ScreenSpot Pro \| 29.0 \| \| AITZ_EM \| 81.9 \| \| Android Control High_EM \| 60.1 \| \| Android Control Low_EM \| 93.7 \| \| AndroidWorld_SR \| 25.5 \| \| MobileMiniWob++_SR \| 91.4 \| ## Requirements The code of Qwen2.5-VL has been in the latest Hugging face transformers and we advise you to build from source with command: ``` pip install git+https://github.com/huggingface/transformers accelerate ``` or you might encounter the following error: ``` KeyError: 'qwen2_5_vl' ``` ## Quickstart Below, we provide simple examples to show how to use Qwen2.5-VL with 🤖 ModelScope and 🤗 Transformers. The code of Qwen2.5-VL has been in the latest Hugging face transformers and we advise you to build from source with command: ``` pip install git+https://github.com/huggingface/transformers accelerate ``` or you might encounter the following error: ``` KeyError: 'qwen2_5_vl' ``` We offer a toolkit to help you handle various types of visual input more conveniently, as if you were using an API. This includes base64, URLs, and interleaved images and videos. You can install it using the following command: ```bash # It's highly recommanded to use `[decord]` feature for faster video loading. pip install qwen-vl-utils[decord]==0.0.8 ``` If you are not using Linux, you might not be able to install `decord` from PyPI. In that case, you can use `pip install qwen-vl-utils` which will fall back to using torchvision for video processing. However, you can still [install decord from source](https://github.com/dmlc/decord?tab=readme-ov-file#install-from-source) to get decord used when loading video. ### Using 🤗 Transformers to Chat Here we show a code snippet to show you how to use the chat model with `transformers` and `qwen_vl_utils`: ```python from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor from qwen_vl_utils import process_vision_info # default: Load the model on the available device(s) model = Qwen2_5_VLForConditionalGeneration.from_pretrained( "Qwen/Qwen2.5-VL-7B-Instruct", torch_dtype="auto", device_map="auto" ) # We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios. # model = Qwen2_5_VLForConditionalGeneration.from_pretrained( # "Qwen/Qwen2.5-VL-7B-Instruct", # torch_dtype=torch.bfloat16, # attn_implementation="flash_attention_2", # device_map="auto", # ) # default processer processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct") # The default range for the number of visual tokens per image in the model is 4-16384. # You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost. # min_pixels = 2562828 # max_pixels = 12802828 # processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels) messages = [ { "role": "user", "content": [ { "type": "image", "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", }, {"type": "text", "text": "Describe this image."}, ], } ] # Preparation for inference text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs, video_inputs = process_vision_info(messages) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt", ) inputs = inputs.to("cuda") # Inference: Generation of the output generated_ids = model.generate(inputs, max_new_tokens=128) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text) ``` <details> <summary>Multi image inference</summary> ```python # Messages containing multiple images and a text query messages = [ { "role": "user", "content": [ {"type": "image", "image": "file:///path/to/image1.jpg"}, {"type": "image", "image": "file:///path/to/image2.jpg"}, {"type": "text", "text": "Identify the similarities between these images."}, ], } ] # Preparation for inference text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs, video_inputs = process_vision_info(messages) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt", ) inputs = inputs.to("cuda") # Inference generated_ids = model.generate(inputs, max_new_tokens=128) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text) ``` </details> <details> <summary>Video inference</summary> ```python # Messages containing a images list as a video and a text query messages = [ { "role": "user", "content": [ { "type": "video", "video": [ "file:///path/to/frame1.jpg", "file:///path/to/frame2.jpg", "file:///path/to/frame3.jpg", "file:///path/to/frame4.jpg", ], }, {"type": "text", "text": "Describe this video."}, ], } ] # Messages containing a local video path and a text query messages = [ { "role": "user", "content": [ { "type": "video", "video": "file:///path/to/video1.mp4", "max_pixels": 360 * 420, "fps": 1.0, }, {"type": "text", "text": "Describe this video."}, ], } ] # Messages containing a video url and a text query messages = [ { "role": "user", "content": [ { "type": "video", "video": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-VL/space_woaudio.mp4", }, {"type": "text", "text": "Describe this video."}, ], } ] #In Qwen 2.5 VL, frame rate information is also input into the model to align with absolute time. # Preparation for inference text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs, video_inputs, video_kwargs = process_vision_info(messages, return_video_kwargs=True) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, fps=fps, padding=True, return_tensors="pt", video_kwargs, ) inputs = inputs.to("cuda") # Inference generated_ids = model.generate(inputs, max_new_tokens=128) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text) ``` Video URL compatibility largely depends on the third-party library version. The details are in the table below. change the backend by `FORCE_QWENVL_VIDEO_READER=torchvision` or `FORCE_QWENVL_VIDEO_READER=decord` if you prefer not to use the default one. \| Backend \| HTTP \| HTTPS \| \|-------------\|------\|-------\| \| torchvision >= 0.19.0 \| ✅ \| ✅ \| \| torchvision < 0.19.0 \| ❌ \| ❌ \| \| decord \| ✅ \| ❌ \| </details> <details> <summary>Batch inference</summary> ```python # Sample messages for batch inference messages1 = [ { "role": "user", "content": [ {"type": "image", "image": "file:///path/to/image1.jpg"}, {"type": "image", "image": "file:///path/to/image2.jpg"}, {"type": "text", "text": "What are the common elements in these pictures?"}, ], } ] messages2 = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Who are you?"}, ] # Combine messages for batch processing messages = [messages1, messages2] # Preparation for batch inference texts = [ processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True) for msg in messages ] image_inputs, video_inputs = process_vision_info(messages) inputs = processor( text=texts, images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt", ) inputs = inputs.to("cuda") # Batch Inference generated_ids = model.generate(*inputs, max_new_tokens=128) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_texts = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_texts) ``` </details> ### 🤖 ModelScope We strongly advise users especially those in mainland China to use ModelScope. `snapshot_download` can help you solve issues concerning downloading checkpoints. ### More Usage Tips For input images, we support local files, base64, and URLs. For videos, we currently only support local files. ```python # You can directly insert a local file path, a URL, or a base64-encoded image into the position where you want in the text. ## Local file path messages = [ { "role": "user", "content": [ {"type": "image", "image": "file:///path/to/your/image.jpg"}, {"type": "text", "text": "Describe this image."}, ], } ] ## Image URL messages = [ { "role": "user", "content": [ {"type": "image", "image": "http://path/to/your/image.jpg"}, {"type": "text", "text": "Describe this image."}, ], } ] ## Base64 encoded image messages = [ { "role": "user", "content": [ {"type": "image", "image": "data:image;base64,/9j/..."}, {"type": "text", "text": "Describe this image."}, ], } ] ``` #### Image Resolution for performance boost The model supports a wide range of resolution inputs. By default, it uses the native resolution for input, but higher resolutions can enhance performance at the cost of more computation. Users can set the minimum and maximum number of pixels to achieve an optimal configuration for their needs, such as a token count range of 256-1280, to balance speed and memory usage. ```python min_pixels = 256 28 * 28 max_pixels = 1280 * 28 * 28 processor = AutoProcessor.from_pretrained( "Qwen/Qwen2.5-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels ) ``` Besides, We provide two methods for fine-grained control over the image size input to the model: 1. Define min_pixels and max_pixels: Images will be resized to maintain their aspect ratio within the range of min_pixels and max_pixels. 2. Specify exact dimensions: Directly set `resized_height` and `resized_width`. These values will be rounded to the nearest multiple of 28. ```python # min_pixels and max_pixels messages = [ { "role": "user", "content": [ { "type": "image", "image": "file:///path/to/your/image.jpg", "resized_height": 280, "resized_width": 420, }, {"type": "text", "text": "Describe this image."}, ], } ] # resized_height and resized_width messages = [ { "role": "user", "content": [ { "type": "image", "image": "file:///path/to/your/image.jpg", "min_pixels": 50176, "max_pixels": 50176, }, {"type": "text", "text": "Describe this image."}, ], } ] ``` ### Processing Long Texts The current `config.json` is set for context length up to 32,768 tokens. To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts. For supported frameworks, you could add the following to `config.json` to enable YaRN: { ..., "type": "yarn", "mrope_section": [ 16, 24, 24 ], "factor": 4, "original_max_position_embeddings": 32768 } However, it should be noted that this method has a significant impact on the performance of temporal and spatial localization tasks, and is therefore not recommended for use. At the same time, for long video inputs, since MRoPE itself is more economical with ids, the max_position_embeddings can be directly modified to a larger value, such as 64k. ## Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwen2.5-VL, title = {Qwen2.5-VL}, url = {https://qwenlm.github.io/blog/qwen2.5-vl/}, author = {Qwen Team}, month = {January}, year = {2025} } @article{Qwen2VL, title={Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution}, author={Wang, Peng and Bai, Shuai and Tan, Sinan and Wang, Shijie and Fan, Zhihao and Bai, Jinze and Chen, Keqin and Liu, Xuejing and Wang, Jialin and Ge, Wenbin and Fan, Yang and Dang, Kai and Du, Mengfei and Ren, Xuancheng and Men, Rui and Liu, Dayiheng and Zhou, Chang and Zhou, Jingren and Lin, Junyang}, journal={arXiv preprint arXiv:2409.12191}, year={2024} } @article{Qwen-VL, title={Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond}, author={Bai, Jinze and Bai, Shuai and Yang, Shusheng and Wang, Shijie and Tan, Sinan and Wang, Peng and Lin, Junyang and Zhou, Chang and Zhou, Jingren}, journal={arXiv preprint arXiv:2308.12966}, year={2023} } ```
genki10/Trial3BERT_AugV8_k5_task1_organization_sp020_lw010_fold3	genki10	2025-04-06T16:22:48Z	0	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "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" ]	text-classification	2025-04-06T16:10:46Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: Trial3BERT_AugV8_k5_task1_organization_sp020_lw010_fold3 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. --> # Trial3BERT_AugV8_k5_task1_organization_sp020_lw010_fold3 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: 1.0037 - Qwk: 0.3672 - Mse: 1.0033 - Rmse: 1.0016 ## 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 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: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\| \| No log \| 1.0 \| 4 \| 9.8399 \| 0.0 \| 9.8381 \| 3.1366 \| \| No log \| 2.0 \| 8 \| 6.1127 \| 0.0369 \| 6.1113 \| 2.4721 \| \| No log \| 3.0 \| 12 \| 3.8091 \| 0.0 \| 3.8081 \| 1.9514 \| \| No log \| 4.0 \| 16 \| 2.5212 \| 0.0819 \| 2.5204 \| 1.5876 \| \| No log \| 5.0 \| 20 \| 1.6102 \| 0.0102 \| 1.6095 \| 1.2687 \| \| No log \| 6.0 \| 24 \| 1.3131 \| 0.0302 \| 1.3126 \| 1.1457 \| \| No log \| 7.0 \| 28 \| 0.9497 \| 0.1117 \| 0.9491 \| 0.9742 \| \| No log \| 8.0 \| 32 \| 0.9000 \| 0.2155 \| 0.8997 \| 0.9485 \| \| No log \| 9.0 \| 36 \| 1.0219 \| 0.1099 \| 1.0217 \| 1.0108 \| \| No log \| 10.0 \| 40 \| 0.9267 \| 0.2658 \| 0.9265 \| 0.9626 \| \| No log \| 11.0 \| 44 \| 0.9925 \| 0.3666 \| 0.9924 \| 0.9962 \| \| No log \| 12.0 \| 48 \| 1.7256 \| 0.2470 \| 1.7252 \| 1.3135 \| \| No log \| 13.0 \| 52 \| 1.3525 \| 0.3004 \| 1.3522 \| 1.1628 \| \| No log \| 14.0 \| 56 \| 1.3833 \| 0.3027 \| 1.3828 \| 1.1759 \| \| No log \| 15.0 \| 60 \| 1.8487 \| 0.2381 \| 1.8476 \| 1.3593 \| \| No log \| 16.0 \| 64 \| 1.0559 \| 0.3979 \| 1.0554 \| 1.0273 \| \| No log \| 17.0 \| 68 \| 1.4325 \| 0.3129 \| 1.4318 \| 1.1966 \| \| No log \| 18.0 \| 72 \| 1.1057 \| 0.3833 \| 1.1051 \| 1.0512 \| \| No log \| 19.0 \| 76 \| 1.1910 \| 0.3419 \| 1.1903 \| 1.0910 \| \| No log \| 20.0 \| 80 \| 1.3988 \| 0.2856 \| 1.3980 \| 1.1824 \| \| No log \| 21.0 \| 84 \| 1.1886 \| 0.3386 \| 1.1882 \| 1.0900 \| \| No log \| 22.0 \| 88 \| 1.2465 \| 0.3407 \| 1.2461 \| 1.1163 \| \| No log \| 23.0 \| 92 \| 1.1015 \| 0.3630 \| 1.1013 \| 1.0494 \| \| No log \| 24.0 \| 96 \| 1.7193 \| 0.2262 \| 1.7187 \| 1.3110 \| \| No log \| 25.0 \| 100 \| 1.3503 \| 0.2858 \| 1.3498 \| 1.1618 \| \| No log \| 26.0 \| 104 \| 1.1207 \| 0.3533 \| 1.1203 \| 1.0585 \| \| No log \| 27.0 \| 108 \| 1.4646 \| 0.2799 \| 1.4641 \| 1.2100 \| \| No log \| 28.0 \| 112 \| 1.7664 \| 0.2368 \| 1.7656 \| 1.3288 \| \| No log \| 29.0 \| 116 \| 1.0499 \| 0.3683 \| 1.0495 \| 1.0245 \| \| No log \| 30.0 \| 120 \| 2.3371 \| 0.1444 \| 2.3359 \| 1.5284 \| \| No log \| 31.0 \| 124 \| 1.0037 \| 0.3672 \| 1.0033 \| 1.0016 \| ### Framework versions - Transformers 4.47.0 - Pytorch 2.5.1+cu121 - Datasets 3.3.1 - Tokenizers 0.21.0
swarup3204/gemma-3-4b-it-anvaya-ift	swarup3204	2025-04-06T16:21:27Z	0	0	transformers	[ "transformers", "gemma3_text", "text-generation", "text-generation-inference", "unsloth", "gemma3", "conversational", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-06T15:19:02Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: swarup3204 - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
gokulsrinivasagan/bert_base_train_book_ent_2_mnli	gokulsrinivasagan	2025-04-06T16:20:48Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "base_model:gokulsrinivasagan/bert_base_train_book_ent_2", "base_model:finetune:gokulsrinivasagan/bert_base_train_book_ent_2", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-06T15:17:40Z	--- library_name: transformers language: - en license: apache-2.0 base_model: gokulsrinivasagan/bert_base_train_book_ent_2 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: bert_base_train_book_ent_2_mnli results: - task: name: Text Classification type: text-classification dataset: name: GLUE MNLI type: glue args: mnli metrics: - name: Accuracy type: accuracy value: 0.565500406834825 --- <!-- 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_train_book_ent_2_mnli This model is a fine-tuned version of [gokulsrinivasagan/bert_base_train_book_ent_2](https://huggingface.co/gokulsrinivasagan/bert_base_train_book_ent_2) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.9102 - Accuracy: 0.5655 ## 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: 256 - eval_batch_size: 256 - seed: 10 - 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: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 1.0714 \| 1.0 \| 1534 \| 1.0338 \| 0.4616 \| \| 0.997 \| 2.0 \| 3068 \| 0.9630 \| 0.5268 \| \| 0.9357 \| 3.0 \| 4602 \| 0.9247 \| 0.5560 \| \| 0.8906 \| 4.0 \| 6136 \| 0.9101 \| 0.5656 \| \| 0.8491 \| 5.0 \| 7670 \| 0.9160 \| 0.5730 \| \| 0.8062 \| 6.0 \| 9204 \| 0.9428 \| 0.5647 \| \| 0.7594 \| 7.0 \| 10738 \| 0.9271 \| 0.5741 \| \| 0.7096 \| 8.0 \| 12272 \| 1.0048 \| 0.5608 \| \| 0.6587 \| 9.0 \| 13806 \| 1.0588 \| 0.5622 \| ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
Fenneccccc/realistic_style_LoRA	Fenneccccc	2025-04-06T16:16:16Z	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-04-05T16:42:36Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: photo collage in Steven Universe style widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - Fenneccccc/realistic_style_LoRA <Gallery /> ## Model description These are Fenneccccc/realistic_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use photo collage in Steven Universe style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](Fenneccccc/realistic_style_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
Dhruv278/deepseek_finetune	Dhruv278	2025-04-06T16:16:04Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-04-06T15:58:26Z	--- base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: Dhruv278 - License: apache-2.0 - Finetuned from model : unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Bouquets/dummy-model	Bouquets	2025-04-06T16:11:58Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-06T16:11:50Z	--- base_model: unsloth/qwen2.5-coder-3b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Bouquets - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-coder-3b-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)
TOMFORD79/ImKing_v1_6	TOMFORD79	2025-04-06T16:11:25Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-04-06T15:25:00Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
nifigashechki/my_LORA_photo_Basquiat_style	nifigashechki	2025-04-06T16:10:31Z	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-04-06T16:10:09Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: photo in my style widget: [] tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - nifigashechki/my_LORA_photo_Basquiat_style <Gallery /> ## Model description These are nifigashechki/my_LORA_photo_Basquiat_style LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use photo in my style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](nifigashechki/my_LORA_photo_Basquiat_style/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
rrreol69/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grazing_scurrying_gibbon	rrreol69	2025-04-06T16:10:23Z	1	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am grazing scurrying gibbon", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-05T09:33:37Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grazing_scurrying_gibbon tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am grazing scurrying gibbon - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grazing_scurrying_gibbon This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/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="rrreol69/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grazing_scurrying_gibbon", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.50.3 - Pytorch: 2.5.1 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
aXsalll/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-curious_savage_termite	aXsalll	2025-04-06T16:09:01Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am curious savage termite", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-04T04:04:56Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-curious_savage_termite tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am curious savage termite - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-curious_savage_termite This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/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="aXsalll/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-curious_savage_termite", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.50.3 - Pytorch: 2.5.1 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
aleaveteen/vangogh_style_LoRA	aleaveteen	2025-04-06T16:08:02Z	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-04-06T16:07:57Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: painting in VAN GOGH style widget: [] tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - aleaveteen/vangogh_style_LoRA <Gallery /> ## Model description These are aleaveteen/vangogh_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use painting in VAN GOGH style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](aleaveteen/vangogh_style_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
devngho/llama-3.2-3b-jamo-init	devngho	2025-04-06T16:07:28Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-06T16:04:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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taguser/openshift-microshift-epoch8-2025-Apr-06	taguser	2025-04-06T16:07:07Z	0	0	peft	[ "peft", "safetensors", "llama-factory", "lora", "generated_from_trainer", "base_model:Qwen/Qwen2.5-Coder-14B-Instruct", "base_model:adapter:Qwen/Qwen2.5-Coder-14B-Instruct", "license:other", "region:us" ]	null	2025-04-06T16:06:13Z	--- library_name: peft license: other base_model: Qwen/Qwen2.5-Coder-14B-Instruct tags: - llama-factory - lora - generated_from_trainer model-index: - name: test 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. --> # test This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct) on the parsed_data dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - total_eval_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 8 ### Training results ### Framework versions - PEFT 0.15.0 - Transformers 4.50.0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.0
mainmagic/chronos-t5-small-btc-m1	mainmagic	2025-04-06T16:04:47Z	0	0	chronos	[ "chronos", "safetensors", "t5", "time-series", "forecasting", "finance", "cryptocurrency", "en", "dataset:time-series", "license:apache-2.0", "region:us" ]	null	2025-04-06T15:39:59Z	--- language: en license: apache-2.0 library_name: chronos tags: - chronos - time-series - forecasting - finance - cryptocurrency datasets: - time-series --- # chronos-t5-small-btc-m1 This is a Chronos model fine-tuned on financial time series data. The model is based on the T5 architecture and is designed for time series forecasting. ## Model Description - Model Type: Chronos (T5-based time series forecasting model) - Fine-tuned from: amazon/chronos-t5-small - Uploaded by: mainmagic - Date: 2025-04-06 Chronos model fine-tuned on BTC/USD M1 data for time series forecasting ## Performance Metrics \| Metric \| Value \| \|--------\|-------\| \| mse \| 1.0823 \| \| mae \| 0.8172 \| \| mape \| 16552.9256 \| ## Usage ```python # Import the Chronos pipeline # Note: You may need to adjust the import path based on your installation import sys sys.path.append('/path/to/chronos-forecasting/src') # Adjust this path from chronos.chronos import ChronosPipeline import torch # Load the model pipeline = ChronosPipeline.from_pretrained("mainmagic/chronos-t5-small-btc-m1") # Create input data (example) context = torch.randn(1, 512) # Batch size 1, context length 512 # Generate forecast forecast = pipeline.predict( context, prediction_length=60, # Predict 60 steps ahead num_samples=20 # Generate 20 different forecast trajectories ) # Use median as point forecast median_forecast = torch.median(forecast, dim=1)[0] ``` ## Training Details This model was fine-tuned using the Chronos native training scripts. The model was trained on financial time series data with the following parameters: - Context length: 512 - Prediction length: 60 - Optimizer: adamw_torch - Learning rate: 0.0001 - Batch size: 16 - Gradient accumulation steps: 4 ## Limitations This model is specifically trained for financial time series forecasting and may not perform well on other types of time series data. The model's performance may also vary depending on market conditions and the specific financial instrument being forecasted. ## Citation If you use this model, please cite: ```bibtex @misc{chronos-forecasting, author = {Amazon Science}, title = {Chronos: Learning the Language of Time Series}, year = {2024}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/amazon-science/chronos-forecasting}} } ```
spacefi1/moralBERT	spacefi1	2025-04-06T16:03:48Z	0	0	null	[ "safetensors", "modernbert", "license:apache-2.0", "region:us" ]	null	2025-04-06T16:02:49Z	--- license: apache-2.0 ---
visdata/goom6	visdata	2025-04-06T16:03:07Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-06T16:01:06Z	--- 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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TOMFORD79/ImKing_v1_5	TOMFORD79	2025-04-06T16:03:07Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-04-06T15:24:53Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
MJaii/qwen2.5_1.5b_rl_fine_tuned	MJaii	2025-04-06T16:02:21Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-06T15:10:49Z	--- 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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shmaxsh/malika_style_LoRA	shmaxsh	2025-04-06T16:02:02Z	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-04-06T16:01:56Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: photo collage in MALIKA style widget: [] tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - shmaxsh/malika_style_LoRA <Gallery /> ## Model description These are shmaxsh/malika_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use photo collage in MALIKA style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](shmaxsh/malika_style_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
sapna-shah-hot-girlsss/trending-sapna-shah-original-viral-leaked-video-on-social-media-x-trending-now-x	sapna-shah-hot-girlsss	2025-04-06T16:00:31Z	0	0	null	[ "region:us" ]	null	2025-04-06T15:58:14Z	trending-sapna-shah-original-viral-leaked-video-on-social-media-x-trending-now-x <animated-image data-catalyst=""><a href="https://alltvsteam.com/viral-video/?v=news-es-tvdf" 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>
mlx-community/Llama-4-Scout-17B-16E-4bit	mlx-community	2025-04-06T15:59:30Z	0	0	transformers	[ "transformers", "safetensors", "llama4", "image-text-to-text", "facebook", "meta", "pytorch", "llama", "llama-4", "mlx", "conversational", "ar", "de", "en", "es", "fr", "hi", "id", "it", "pt", "th", "tl", "vi", "license:other", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-04-06T15:32:12Z	--- library_name: transformers language: - ar - de - en - es - fr - hi - id - it - pt - th - tl - vi tags: - facebook - meta - pytorch - llama - llama-4 - mlx extra_gated_prompt: 'LLAMA 4 COMMUNITY LICENSE AGREEMENT Llama 4 Version Effective Date: April 5, 2025 "Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. "Documentation" means the specifications, manuals and documentation accompanying Llama 4 distributed by Meta at [https://www.llama.com/docs/overview](https://llama.com/docs/overview). 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Failure to follow these instructions may prevent you from accessing this model and others on Hugging Face. You will not have the ability to edit this form after submission, so please ensure all information is accurate. license: other license_name: llama4 --- # mlx-community/Llama-4-Scout-17B-16E-4bit This model was converted to MLX format from [`meta-llama/Llama-4-Scout-17B-16E`]() using mlx-vlm version 0.1.21. Refer to the [original model card](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E) for more details on the model. ## Use with mlx ```bash pip install -U mlx-vlm ``` ```bash python -m mlx_vlm.generate --model mlx-community/Llama-4-Scout-17B-16E-4bit --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image> ```
Divyansh008/Urvashi-1B-rp	Divyansh008	2025-04-06T15:59:26Z	0	0	null	[ "safetensors", "llama", "merge", "mergekit", "lazymergekit", "huihui-ai/Llama-3.2-1B-Instruct-abliterated", "NexesMess/Llama_3.2_1b_Abliteratest_SCE", "xdrshjr/llama3.2_1b_uncensored_5000_8epoch_lora", "diabolic6045/open-llama-3.2-1B-Instruct", "phamhai/Llama-3.2-1B-CyberFrog", "Nexesenex/Llama_3.2_1b_RandomLego_RP_R1_0.1", "jtatman/llama-3.2-1b-lewd-mental-occult", "base_model:NexesMess/Llama_3.2_1b_Abliteratest_SCE", "base_model:merge:NexesMess/Llama_3.2_1b_Abliteratest_SCE", "base_model:Nexesenex/Llama_3.2_1b_RandomLego_RP_R1_0.1", "base_model:merge:Nexesenex/Llama_3.2_1b_RandomLego_RP_R1_0.1", "base_model:diabolic6045/open-llama-3.2-1B-Instruct", "base_model:merge:diabolic6045/open-llama-3.2-1B-Instruct", "base_model:huihui-ai/Llama-3.2-1B-Instruct-abliterated", "base_model:merge:huihui-ai/Llama-3.2-1B-Instruct-abliterated", "base_model:jtatman/llama-3.2-1b-lewd-mental-occult", "base_model:merge:jtatman/llama-3.2-1b-lewd-mental-occult", "base_model:phamhai/Llama-3.2-1B-CyberFrog", "base_model:merge:phamhai/Llama-3.2-1B-CyberFrog", "base_model:xdrshjr/llama3.2_1b_uncensored_5000_8epoch_lora", "base_model:merge:xdrshjr/llama3.2_1b_uncensored_5000_8epoch_lora", "region:us" ]	null	2025-04-06T15:56:02Z	--- base_model: - huihui-ai/Llama-3.2-1B-Instruct-abliterated - NexesMess/Llama_3.2_1b_Abliteratest_SCE - xdrshjr/llama3.2_1b_uncensored_5000_8epoch_lora - diabolic6045/open-llama-3.2-1B-Instruct - phamhai/Llama-3.2-1B-CyberFrog - Nexesenex/Llama_3.2_1b_RandomLego_RP_R1_0.1 - jtatman/llama-3.2-1b-lewd-mental-occult tags: - merge - mergekit - lazymergekit - huihui-ai/Llama-3.2-1B-Instruct-abliterated - NexesMess/Llama_3.2_1b_Abliteratest_SCE - xdrshjr/llama3.2_1b_uncensored_5000_8epoch_lora - diabolic6045/open-llama-3.2-1B-Instruct - phamhai/Llama-3.2-1B-CyberFrog - Nexesenex/Llama_3.2_1b_RandomLego_RP_R1_0.1 - jtatman/llama-3.2-1b-lewd-mental-occult --- # Urvashi-1B-rp Tiny-Urvashi-v5 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [huihui-ai/Llama-3.2-1B-Instruct-abliterated](https://huggingface.co/huihui-ai/Llama-3.2-1B-Instruct-abliterated) * [NexesMess/Llama_3.2_1b_Abliteratest_SCE](https://huggingface.co/NexesMess/Llama_3.2_1b_Abliteratest_SCE) * [xdrshjr/llama3.2_1b_uncensored_5000_8epoch_lora](https://huggingface.co/xdrshjr/llama3.2_1b_uncensored_5000_8epoch_lora) * [diabolic6045/open-llama-3.2-1B-Instruct](https://huggingface.co/diabolic6045/open-llama-3.2-1B-Instruct) * [phamhai/Llama-3.2-1B-CyberFrog](https://huggingface.co/phamhai/Llama-3.2-1B-CyberFrog) * [Nexesenex/Llama_3.2_1b_RandomLego_RP_R1_0.1](https://huggingface.co/Nexesenex/Llama_3.2_1b_RandomLego_RP_R1_0.1) * [jtatman/llama-3.2-1b-lewd-mental-occult](https://huggingface.co/jtatman/llama-3.2-1b-lewd-mental-occult) ## 🧩 Configuration ```yaml models: - model: huihui-ai/Llama-3.2-1B-Instruct-abliterated parameters: weight: 1.2 density: 0.9 - model: NexesMess/Llama_3.2_1b_Abliteratest_SCE parameters: weight: 1.0 density: 0.9 - model: xdrshjr/llama3.2_1b_uncensored_5000_8epoch_lora parameters: weight: 1.0 density: 0.9 - model: diabolic6045/open-llama-3.2-1B-Instruct parameters: weight: 1.0 density: 0.9 - model: phamhai/Llama-3.2-1B-CyberFrog parameters: weight: 1.0 density: 0.9 - model: Nexesenex/Llama_3.2_1b_RandomLego_RP_R1_0.1 parameters: weight: 1.0 density: 0.9 - model: jtatman/llama-3.2-1b-lewd-mental-occult parameters: weight: 1.0 density: 0.9 merge_method: sce base_model: bunnycore/FuseChat-3.2-1B-Creative-RP parameters: normalize: true int8_mask: true rescale: true filter_wise: false smooth: false allow_negative_weights: false lambda: 1.0 select_topk: 0.1 tokenizer: source: union chat_template: auto dtype: bfloat16 out_dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Divyansh008/Urvashi-1B-rp" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
nkc98/cf-sector-classification-model	nkc98	2025-04-06T15:58:45Z	0	0	null	[ "safetensors", "roberta", "license:apache-2.0", "region:us" ]	null	2025-04-05T19:12:00Z	--- license: apache-2.0 ---
marcuslam/marcuscap-lora	marcuslam	2025-04-06T15:57:38Z	0	0	diffusers	[ "diffusers", "sd3.5-large", "lora", "replicate", "text-to-image", "en", "base_model:stabilityai/stable-diffusion-3.5-large", "base_model:adapter:stabilityai/stable-diffusion-3.5-large", "license:other", "region:us" ]	text-to-image	2025-04-06T15:28:15Z	--- license: other license_name: stabilityai-ai-community license_link: https://huggingface.co/stabilityai/stable-diffusion-3.5-large/blob/main/LICENSE.md language: - en tags: - sd3.5-large - diffusers - lora - replicate base_model: stabilityai/stable-diffusion-3.5-large pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: marcuscap --- # Marcuscap Lora <Gallery /> Trained on Replicate using: https://replicate.com/lucataco/sd3.5-fine-tuner/train ## Trigger words You should use `marcuscap` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) 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)
genki10/Trial3BERT_AugV8_k5_task1_organization_sp020_lw010_fold1	genki10	2025-04-06T15:55:51Z	0	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "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" ]	text-classification	2025-04-06T15:39:22Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: Trial3BERT_AugV8_k5_task1_organization_sp020_lw010_fold1 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. --> # Trial3BERT_AugV8_k5_task1_organization_sp020_lw010_fold1 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: 0.9521 - Qwk: 0.3712 - Mse: 0.9507 - Rmse: 0.9750 ## 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 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: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\| \| No log \| 1.0 \| 4 \| 8.8349 \| 0.0 \| 8.8325 \| 2.9719 \| \| No log \| 2.0 \| 8 \| 6.5346 \| 0.0 \| 6.5322 \| 2.5558 \| \| No log \| 3.0 \| 12 \| 4.6568 \| 0.0079 \| 4.6546 \| 2.1575 \| \| No log \| 4.0 \| 16 \| 2.3560 \| 0.1277 \| 2.3543 \| 1.5344 \| \| No log \| 5.0 \| 20 \| 1.6425 \| 0.0211 \| 1.6409 \| 1.2810 \| \| No log \| 6.0 \| 24 \| 1.0660 \| 0.0106 \| 1.0646 \| 1.0318 \| \| No log \| 7.0 \| 28 \| 0.9491 \| 0.0566 \| 0.9477 \| 0.9735 \| \| No log \| 8.0 \| 32 \| 1.1504 \| 0.0211 \| 1.1488 \| 1.0718 \| \| No log \| 9.0 \| 36 \| 1.0441 \| 0.0790 \| 1.0426 \| 1.0211 \| \| No log \| 10.0 \| 40 \| 1.2621 \| 0.0905 \| 1.2603 \| 1.1226 \| \| No log \| 11.0 \| 44 \| 0.7675 \| 0.3334 \| 0.7660 \| 0.8752 \| \| No log \| 12.0 \| 48 \| 0.8880 \| 0.2501 \| 0.8861 \| 0.9413 \| \| No log \| 13.0 \| 52 \| 0.7809 \| 0.2604 \| 0.7798 \| 0.8831 \| \| No log \| 14.0 \| 56 \| 0.6777 \| 0.4324 \| 0.6762 \| 0.8223 \| \| No log \| 15.0 \| 60 \| 0.6395 \| 0.4454 \| 0.6381 \| 0.7988 \| \| No log \| 16.0 \| 64 \| 0.5608 \| 0.5391 \| 0.5597 \| 0.7481 \| \| No log \| 17.0 \| 68 \| 0.7276 \| 0.4471 \| 0.7262 \| 0.8522 \| \| No log \| 18.0 \| 72 \| 0.6371 \| 0.5346 \| 0.6359 \| 0.7974 \| \| No log \| 19.0 \| 76 \| 0.6194 \| 0.5334 \| 0.6183 \| 0.7863 \| \| No log \| 20.0 \| 80 \| 0.6827 \| 0.4912 \| 0.6819 \| 0.8258 \| \| No log \| 21.0 \| 84 \| 0.7754 \| 0.4423 \| 0.7739 \| 0.8797 \| \| No log \| 22.0 \| 88 \| 0.8253 \| 0.4494 \| 0.8238 \| 0.9076 \| \| No log \| 23.0 \| 92 \| 0.6449 \| 0.5215 \| 0.6440 \| 0.8025 \| \| No log \| 24.0 \| 96 \| 0.8520 \| 0.4038 \| 0.8507 \| 0.9223 \| \| No log \| 25.0 \| 100 \| 0.8840 \| 0.3797 \| 0.8826 \| 0.9395 \| \| No log \| 26.0 \| 104 \| 0.7119 \| 0.4361 \| 0.7108 \| 0.8431 \| \| No log \| 27.0 \| 108 \| 0.9122 \| 0.3626 \| 0.9105 \| 0.9542 \| \| No log \| 28.0 \| 112 \| 0.8284 \| 0.4334 \| 0.8271 \| 0.9094 \| \| No log \| 29.0 \| 116 \| 0.5857 \| 0.5887 \| 0.5850 \| 0.7648 \| \| No log \| 30.0 \| 120 \| 0.6951 \| 0.5265 \| 0.6940 \| 0.8331 \| \| No log \| 31.0 \| 124 \| 0.7925 \| 0.4173 \| 0.7914 \| 0.8896 \| \| No log \| 32.0 \| 128 \| 0.8020 \| 0.3790 \| 0.8011 \| 0.8950 \| \| No log \| 33.0 \| 132 \| 1.0397 \| 0.3065 \| 1.0380 \| 1.0188 \| \| No log \| 34.0 \| 136 \| 0.7218 \| 0.4711 \| 0.7208 \| 0.8490 \| \| No log \| 35.0 \| 140 \| 0.9004 \| 0.3568 \| 0.8988 \| 0.9481 \| \| No log \| 36.0 \| 144 \| 0.8941 \| 0.3654 \| 0.8926 \| 0.9448 \| \| No log \| 37.0 \| 148 \| 0.8124 \| 0.3885 \| 0.8110 \| 0.9006 \| \| No log \| 38.0 \| 152 \| 0.7523 \| 0.4416 \| 0.7513 \| 0.8668 \| \| No log \| 39.0 \| 156 \| 1.0492 \| 0.2771 \| 1.0477 \| 1.0236 \| \| No log \| 40.0 \| 160 \| 0.7507 \| 0.4415 \| 0.7498 \| 0.8659 \| \| No log \| 41.0 \| 164 \| 1.1460 \| 0.3028 \| 1.1444 \| 1.0698 \| \| No log \| 42.0 \| 168 \| 0.6934 \| 0.4594 \| 0.6924 \| 0.8321 \| \| No log \| 43.0 \| 172 \| 0.7583 \| 0.4714 \| 0.7572 \| 0.8702 \| \| No log \| 44.0 \| 176 \| 0.9521 \| 0.3712 \| 0.9507 \| 0.9750 \| ### Framework versions - Transformers 4.47.0 - Pytorch 2.5.1+cu121 - Datasets 3.3.1 - Tokenizers 0.21.0
kolosal/qwq-32b	kolosal	2025-04-06T15:53:50Z	0	0	null	[ "gguf", "chat", "text-generation", "en", "arxiv:2309.00071", "arxiv:2412.15115", "base_model:Qwen/QwQ-32B", "base_model:quantized:Qwen/QwQ-32B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-06T15:09:26Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/QWQ-32B-GGUF/blob/main/LICENSE language: - en pipeline_tag: text-generation base_model: Qwen/QwQ-32B tags: - chat --- # QwQ-32B-GGUF <a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/> </a> ## Introduction QwQ is the reasoning model of the Qwen series. Compared with conventional instruction-tuned models, QwQ, which is capable of thinking and reasoning, can achieve significantly enhanced performance in downstream tasks, especially hard problems. QwQ-32B is the medium-sized reasoning model, which is capable of achieving competitive performance against state-of-the-art reasoning models, e.g., DeepSeek-R1, o1-mini. <p align="center"> <img width="100%" src="figures/benchmark.jpg"> </p> This repo contains the QwQ 32B model in the GGUF Format, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training (Supervised Finetuning and Reinforcement Learning) - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias - Number of Parameters: 32.5B - Number of Paramaters (Non-Embedding): 31.0B - Number of Layers: 64 - Number of Attention Heads (GQA): 40 for Q and 8 for KV - Context Length: Full 131,072 tokens - Quantization: q4_K_M, q5_0, q5_K_M, q6_K, q8_0 Note: For the best experience, please review the [usage guidelines](#usage-guidelines) before deploying QwQ models. You can try our [demo](https://huggingface.co/spaces/Qwen/QwQ-32B-Demo) or access QwQ models via [QwenChat](https://chat.qwen.ai). For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwq-32b/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/). ## Requirements QwQ is based on Qwen2.5, whose code has been in the latest Hugging face `transformers`. We advise you to use the latest version of `transformers`. With `transformers<4.37.0`, you will encounter the following error: ``` KeyError: 'qwen2' ``` Also check out our [AWQ documentation](https://qwen.readthedocs.io/en/latest/quantization/awq.html) for more usage guide. ## Quickstart heck out our [llama.cpp documentation](https://qwen.readthedocs.io/en/latest/run_locally/llama.cpp.html) for more usage guide. We advise you to clone [`llama.cpp`](https://github.com/ggerganov/llama.cpp) and install it following the official guide. We follow the latest version of llama.cpp. In the following demonstration, we assume that you are running commands under the repository `llama.cpp`. You can use the following commands for inference: ```shell ./llama-cli \ --model QwQ-32B-GGUF/qwq-32b-q4_k_m.gguf \ --threads 32 \ --ctx-size 32768 \ --seed 1234 \ --temp 0.6 \ --min-p 0.0 \ --top-k 40 \ --top-p 0.95 \ -no-cnv \ --samplers "top_k;top_p;min_p;temperature;" \ --prompt "<\|im_start\|>user\nHow many r's are in the word \"strawberry\"<\|im_end\|>\n<\|im_start\|>assistant\n<think>\n" ``` ### Usage Guidelines To achieve optimal performance, we recommend the following settings: 1. Enforce Thoughtful Output: Ensure the model starts with "\<think\>\n" to prevent generating empty thinking content, which can degrade output quality. 2. Sampling Parameters: - Use Temperature=0.6, TopP=0.95, MinP=0 instead of Greedy decoding to avoid endless repetitions. - Use TopK between 20 and 40 to filter out rare token occurrences while maintaining the diversity of the generated output. - For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may result in occasional language mixing and a slight decrease in performance. 3. No Thinking Content in History: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. This feature is already implemented in `apply_chat_template`. 4. Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking. - Math Problems: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt. - Multiple-Choice Questions: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g.,`\"answer\": \"C\"`." in the prompt. 5. Handle Long Inputs: For inputs exceeding 32,768 tokens, enable [YaRN](https://arxiv.org/abs/2309.00071) to improve the model's ability to capture long-sequence information effectively. Currently, only vLLM supports YARN for length extrapolating. If you want to process sequences up to 131,072 tokens, please refer to non-GGUF models. 6. Other References: You can also consult [Unsloth's Guide](https://docs.unsloth.ai/basics/tutorial-how-to-run-qwq-32b-effectively) to see if their approach meets your needs. (Thanks to the Unsloth team!) ## Evaluation & Performance Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwq-32b/). For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html). ## Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwq32b, title = {QwQ-32B: Embracing the Power of Reinforcement Learning}, url = {https://qwenlm.github.io/blog/qwq-32b/}, author = {Qwen Team}, month = {March}, year = {2025} } @article{qwen2.5, title={Qwen2.5 Technical Report}, author={An Yang and Baosong Yang and Beichen Zhang and Binyuan Hui and Bo Zheng and Bowen Yu and Chengyuan Li and Dayiheng Liu and Fei Huang and Haoran Wei and Huan Lin and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Yang and Jiaxi Yang and Jingren Zhou and Junyang Lin and Kai Dang and Keming Lu and Keqin Bao and Kexin Yang and Le Yu and Mei Li and Mingfeng Xue and Pei Zhang and Qin Zhu and Rui Men and Runji Lin and Tianhao Li and Tianyi Tang and Tingyu Xia and Xingzhang Ren and Xuancheng Ren and Yang Fan and Yang Su and Yichang Zhang and Yu Wan and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zihan Qiu}, journal={arXiv preprint arXiv:2412.15115}, year={2024} } ```
abragin/opus-mt-en-ru-ft-dostoevsky	abragin	2025-04-06T15:53:27Z	0	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-04-06T15:37:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
MinaMila/gemma2_9b_LLFT_Adult_3ep_42	MinaMila	2025-04-06T15:53:07Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/gemma-2-9b", "base_model:finetune:unsloth/gemma-2-9b", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-06T15:49:48Z	--- base_model: unsloth/gemma-2-9b tags: - text-generation-inference - transformers - unsloth - gemma2 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: MinaMila - License: apache-2.0 - Finetuned from model : unsloth/gemma-2-9b This gemma2 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)
CvLelouch/lora_model	CvLelouch	2025-04-06T15:52:25Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-06T15:52:06Z	--- base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: CvLelouch - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Hassnain-work/user-6705755cad2d8cf01dbf7100-model-5a7ba0b257af42878bba83b7651c3108	Hassnain-work	2025-04-06T15:51:31Z	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	2025-04-06T15:39:04Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: TOK --- # User 6705755Cad2D8Cf01Dbf7100 Model 5A7Ba0B257Af42878Bba83B7651C3108 <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `TOK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TOK", "lora_weights": "https://huggingface.co/Hassnain-work/user-6705755cad2d8cf01dbf7100-model-5a7ba0b257af42878bba83b7651c3108/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('Hassnain-work/user-6705755cad2d8cf01dbf7100-model-5a7ba0b257af42878bba83b7651c3108', weight_name='lora.safetensors') image = pipeline('TOK').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Hassnain-work/user-6705755cad2d8cf01dbf7100-model-5a7ba0b257af42878bba83b7651c3108/discussions) to add images that show off what you’ve made with this LoRA.
drlon/drlon-gemma-3-function-calls-bnb-4b-it-0406	drlon	2025-04-06T15:50:57Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3", "trl", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-06T15:50:44Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: drlon - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
AmaanDhamaskar/IndicBART-mr-test	AmaanDhamaskar	2025-04-06T15:48:29Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "mbart", "text2text-generation", "generated_from_trainer", "base_model:ai4bharat/IndicBART", "base_model:finetune:ai4bharat/IndicBART", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-04-06T11:01:50Z	--- library_name: transformers base_model: ai4bharat/IndicBART tags: - generated_from_trainer metrics: - rouge model-index: - name: IndicBART-mr-test 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. --> # IndicBART-mr-test This model is a fine-tuned version of [ai4bharat/IndicBART](https://huggingface.co/ai4bharat/IndicBART) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.7345 - Rouge1: 47.0576 - Rouge2: 4.7014 - Rougel: 47.0474 - Rougelsum: 47.0396 ## 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: 5.6e-05 - train_batch_size: 12 - eval_batch_size: 12 - 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: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\| \| 3.6435 \| 1.0 \| 909 \| 2.8758 \| 45.165 \| 1.1394 \| 45.168 \| 45.1586 \| \| 3.1753 \| 2.0 \| 1818 \| 2.7719 \| 46.5321 \| 3.3062 \| 46.5286 \| 46.5338 \| \| 3.0756 \| 3.0 \| 2727 \| 2.7413 \| 46.7024 \| 4.0972 \| 46.696 \| 46.6929 \| \| 3.0307 \| 4.0 \| 3636 \| 2.7345 \| 47.0576 \| 4.7014 \| 47.0474 \| 47.0396 \| ### Framework versions - Transformers 4.50.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
Beridar/ReadyArt-Forgotten-Abomination-36B-v4.1-mlx-8bit	Beridar	2025-04-06T15:44:54Z	0	0	mlx	[ "mlx", "safetensors", "mistral", "nsfw", "explicit", "roleplay", "unaligned", "dangerous", "ERP", "text-generation", "conversational", "en", "base_model:ReadyArt/Forgotten-Abomination-36B-v4.1", "base_model:merge:ReadyArt/Forgotten-Abomination-36B-v4.1", "license:apache-2.0", "8-bit", "region:us" ]	text-generation	2025-04-06T14:48:20Z	--- base_model: ReadyArt/Forgotten-Abomination-36B-v4.1 base_model_relation: merge language: - en license: apache-2.0 inference: false tags: - nsfw - explicit - roleplay - unaligned - dangerous - ERP - mlx pipeline_tag: text-generation library_name: mlx --- # Beridar/ReadyArt-Forgotten-Abomination-36B-v4.1-mlx-8bit This model [Beridar/ReadyArt-Forgotten-Abomination-36B-v4.1-mlx-8bit](https://huggingface.co/Beridar/ReadyArt-Forgotten-Abomination-36B-v4.1-mlx-8bit) was converted to MLX format from [ReadyArt/Forgotten-Abomination-36B-v4.1](https://huggingface.co/ReadyArt/Forgotten-Abomination-36B-v4.1) using mlx-lm version 0.22.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("Beridar/ReadyArt-Forgotten-Abomination-36B-v4.1-mlx-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) ```
gadalka-fm/ali_style_LoRA	gadalka-fm	2025-04-06T15:42:03Z	9	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-04-01T21:04:18Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: illustration in ALI style widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - gadalka-fm/ali_style_LoRA <Gallery /> ## Model description These are gadalka-fm/ali_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use illustration in ALI style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](gadalka-fm/ali_style_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
aixonlab/Selene-27b-v1	aixonlab	2025-04-06T15:41:25Z	0	0	transformers	[ "transformers", "gemma3_text", "text-generation", "text-generation-inference", "unsloth", "gemma3", "conversational", "en", "base_model:unsloth/gemma-3-27b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-27b-it-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-06T15:33:56Z	--- base_model: unsloth/gemma-3-27b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: aixonlab - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-27b-it-unsloth-bnb-4bit This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
sunviraz/sunvi	sunviraz	2025-04-06T15:40:57Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-06T15:05:03Z	--- 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: sunvi --- # Sunvi <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 `sunvi` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "sunvi", "lora_weights": "https://huggingface.co/sunviraz/sunvi/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('sunviraz/sunvi', weight_name='lora.safetensors') image = pipeline('sunvi').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/sunviraz/sunvi/discussions) to add images that show off what you’ve made with this LoRA.
HeOeH/Iron_IL_0405_2w	HeOeH	2025-04-06T15:37:37Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-06T15:37:11Z	--- license: apache-2.0 ---
sapna-shah-hot-girlsss/sapna.shah.videos.on.social.media.trending.now	sapna-shah-hot-girlsss	2025-04-06T15:36:00Z	0	0	null	[ "region:us" ]	null	2025-04-06T15:34:20Z	<animated-image data-catalyst=""><a href="https://alltvsteam.com/viral-video/?v=news-es-tvdf" 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>
bhavya777/qwen-2b-vlm-ocr	bhavya777	2025-04-06T15:35:46Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_vl", "feature-extraction", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	feature-extraction	2025-04-06T15:33:53Z	--- base_model: unsloth/qwen2-vl-2b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2_vl license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: bhavya777 - License: apache-2.0 - Finetuned from model : unsloth/qwen2-vl-2b-instruct-bnb-4bit This qwen2_vl model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
gokulsrinivasagan/bert_base_train_book_ent_1_inv_wnli	gokulsrinivasagan	2025-04-06T15:33:58Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "base_model:gokulsrinivasagan/bert_base_train_book_ent_1_inv", "base_model:finetune:gokulsrinivasagan/bert_base_train_book_ent_1_inv", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-06T15:32:43Z	--- library_name: transformers language: - en license: apache-2.0 base_model: gokulsrinivasagan/bert_base_train_book_ent_1_inv tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: bert_base_train_book_ent_1_inv_wnli results: - task: name: Text Classification type: text-classification dataset: name: GLUE WNLI type: glue args: wnli metrics: - name: Accuracy type: accuracy value: 0.5211267605633803 --- <!-- 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_train_book_ent_1_inv_wnli This model is a fine-tuned version of [gokulsrinivasagan/bert_base_train_book_ent_1_inv](https://huggingface.co/gokulsrinivasagan/bert_base_train_book_ent_1_inv) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6934 - Accuracy: 0.5211 ## 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: 256 - eval_batch_size: 256 - seed: 10 - 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: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7032 \| 1.0 \| 3 \| 0.7016 \| 0.4085 \| \| 0.7009 \| 2.0 \| 6 \| 0.7027 \| 0.4789 \| \| 0.6932 \| 3.0 \| 9 \| 0.7054 \| 0.4225 \| \| 0.7019 \| 4.0 \| 12 \| 0.6934 \| 0.5211 \| \| 0.6958 \| 5.0 \| 15 \| 0.7025 \| 0.3944 \| \| 0.6938 \| 6.0 \| 18 \| 0.7127 \| 0.4366 \| \| 0.6893 \| 7.0 \| 21 \| 0.6997 \| 0.4930 \| \| 0.6976 \| 8.0 \| 24 \| 0.7025 \| 0.4648 \| \| 0.6959 \| 9.0 \| 27 \| 0.7279 \| 0.4085 \| ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
ngankhtt/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-thick_stinging_crane	ngankhtt	2025-04-06T15:33:37Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am thick stinging crane", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-05T18:52:56Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-thick_stinging_crane tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am thick stinging crane - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-thick_stinging_crane This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/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="ngankhtt/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-thick_stinging_crane", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.50.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Raasmii/FitFoodie_4bit_Qwen2.5-0.5B	Raasmii	2025-04-06T15:29:55Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-06T15:29:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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gokulsrinivasagan/bert_base_train_book_ent_1_wnli	gokulsrinivasagan	2025-04-06T15:29:36Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "base_model:gokulsrinivasagan/bert_base_train_book_ent_1", "base_model:finetune:gokulsrinivasagan/bert_base_train_book_ent_1", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-06T15:29:02Z	--- library_name: transformers language: - en license: apache-2.0 base_model: gokulsrinivasagan/bert_base_train_book_ent_1 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: bert_base_train_book_ent_1_wnli results: - task: name: Text Classification type: text-classification dataset: name: GLUE WNLI type: glue args: wnli metrics: - name: Accuracy type: accuracy value: 0.5352112676056338 --- <!-- 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_train_book_ent_1_wnli This model is a fine-tuned version of [gokulsrinivasagan/bert_base_train_book_ent_1](https://huggingface.co/gokulsrinivasagan/bert_base_train_book_ent_1) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6861 - Accuracy: 0.5352 ## 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: 256 - eval_batch_size: 256 - seed: 10 - 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: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7107 \| 1.0 \| 3 \| 0.6861 \| 0.5352 \| \| 0.7052 \| 2.0 \| 6 \| 0.7128 \| 0.4225 \| \| 0.7098 \| 3.0 \| 9 \| 0.7114 \| 0.4507 \| \| 0.7015 \| 4.0 \| 12 \| 0.7106 \| 0.4507 \| \| 0.6978 \| 5.0 \| 15 \| 0.7092 \| 0.4225 \| \| 0.6993 \| 6.0 \| 18 \| 0.7073 \| 0.4648 \| ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
KyberNull/tiny-random-granite-moe-Q8_0-GGUF	KyberNull	2025-04-06T15:29:27Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:katuni4ka/tiny-random-granite-moe", "base_model:quantized:katuni4ka/tiny-random-granite-moe", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-06T15:29:24Z	--- base_model: katuni4ka/tiny-random-granite-moe tags: - llama-cpp - gguf-my-repo --- # KyberNull/tiny-random-granite-moe-Q8_0-GGUF This model was converted to GGUF format from [`katuni4ka/tiny-random-granite-moe`](https://huggingface.co/katuni4ka/tiny-random-granite-moe) 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/katuni4ka/tiny-random-granite-moe) 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 KyberNull/tiny-random-granite-moe-Q8_0-GGUF --hf-file tiny-random-granite-moe-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo KyberNull/tiny-random-granite-moe-Q8_0-GGUF --hf-file tiny-random-granite-moe-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo KyberNull/tiny-random-granite-moe-Q8_0-GGUF --hf-file tiny-random-granite-moe-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo KyberNull/tiny-random-granite-moe-Q8_0-GGUF --hf-file tiny-random-granite-moe-q8_0.gguf -c 2048 ```
rj2537580/crack_detection	rj2537580	2025-04-06T15:29:12Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-06T15:21:07Z	--- license: apache-2.0 ---
gokulsrinivasagan/bert_base_train_book_ent_1_inv_sst2	gokulsrinivasagan	2025-04-06T15:28:51Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "base_model:gokulsrinivasagan/bert_base_train_book_ent_1_inv", "base_model:finetune:gokulsrinivasagan/bert_base_train_book_ent_1_inv", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-06T15:05:38Z	--- library_name: transformers language: - en license: apache-2.0 base_model: gokulsrinivasagan/bert_base_train_book_ent_1_inv tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: bert_base_train_book_ent_1_inv_sst2 results: - task: name: Text Classification type: text-classification dataset: name: GLUE SST2 type: glue args: sst2 metrics: - name: Accuracy type: accuracy value: 0.7981651376146789 --- <!-- 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_train_book_ent_1_inv_sst2 This model is a fine-tuned version of [gokulsrinivasagan/bert_base_train_book_ent_1_inv](https://huggingface.co/gokulsrinivasagan/bert_base_train_book_ent_1_inv) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.4909 - Accuracy: 0.7982 ## 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: 256 - eval_batch_size: 256 - seed: 10 - 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: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5659 \| 1.0 \| 264 \| 0.4935 \| 0.7718 \| \| 0.2908 \| 2.0 \| 528 \| 0.4909 \| 0.7982 \| \| 0.2089 \| 3.0 \| 792 \| 0.5067 \| 0.7936 \| \| 0.167 \| 4.0 \| 1056 \| 0.5166 \| 0.7993 \| \| 0.1351 \| 5.0 \| 1320 \| 0.5875 \| 0.7936 \| \| 0.1114 \| 6.0 \| 1584 \| 0.7650 \| 0.7798 \| \| 0.093 \| 7.0 \| 1848 \| 0.7186 \| 0.7878 \| ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
gokulsrinivasagan/bert_base_train_book_ent_1_stsb	gokulsrinivasagan	2025-04-06T15:28:39Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "base_model:gokulsrinivasagan/bert_base_train_book_ent_1", "base_model:finetune:gokulsrinivasagan/bert_base_train_book_ent_1", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-06T15:26:12Z	--- library_name: transformers language: - en license: apache-2.0 base_model: gokulsrinivasagan/bert_base_train_book_ent_1 tags: - generated_from_trainer datasets: - glue metrics: - spearmanr model-index: - name: bert_base_train_book_ent_1_stsb results: - task: name: Text Classification type: text-classification dataset: name: GLUE STSB type: glue args: stsb metrics: - name: Spearmanr type: spearmanr value: 0.06724185394471227 --- <!-- 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_train_book_ent_1_stsb This model is a fine-tuned version of [gokulsrinivasagan/bert_base_train_book_ent_1](https://huggingface.co/gokulsrinivasagan/bert_base_train_book_ent_1) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 2.3368 - Pearson: 0.0741 - Spearmanr: 0.0672 - Combined Score: 0.0707 ## 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: 256 - eval_batch_size: 256 - seed: 10 - 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: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Pearson \| Spearmanr \| Combined Score \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:---------:\|:--------------:\| \| 3.2901 \| 1.0 \| 23 \| 2.3368 \| 0.0741 \| 0.0672 \| 0.0707 \| \| 2.0792 \| 2.0 \| 46 \| 2.4129 \| 0.0800 \| 0.0823 \| 0.0812 \| \| 1.9859 \| 3.0 \| 69 \| 2.4299 \| 0.0977 \| 0.0990 \| 0.0984 \| \| 1.8542 \| 4.0 \| 92 \| 2.5856 \| 0.1443 \| 0.1421 \| 0.1432 \| \| 1.6726 \| 5.0 \| 115 \| 2.4581 \| 0.1899 \| 0.1863 \| 0.1881 \| \| 1.4614 \| 6.0 \| 138 \| 2.3853 \| 0.2218 \| 0.2217 \| 0.2218 \| ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
genki10/Trial3BERT_AugV8_k5_task1_organization_sp010_lw010_fold4	genki10	2025-04-06T15:26:42Z	0	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "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" ]	text-classification	2025-04-06T15:15:34Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: Trial3BERT_AugV8_k5_task1_organization_sp010_lw010_fold4 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. --> # Trial3BERT_AugV8_k5_task1_organization_sp010_lw010_fold4 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: 1.0838 - Qwk: 0.3036 - Mse: 1.0838 - Rmse: 1.0410 ## 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 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: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\| \| No log \| 1.0 \| 4 \| 6.6285 \| 0.0 \| 6.6285 \| 2.5746 \| \| No log \| 2.0 \| 8 \| 4.6674 \| 0.0016 \| 4.6674 \| 2.1604 \| \| No log \| 3.0 \| 12 \| 2.6380 \| 0.0040 \| 2.6380 \| 1.6242 \| \| No log \| 4.0 \| 16 \| 1.5543 \| 0.0212 \| 1.5543 \| 1.2467 \| \| No log \| 5.0 \| 20 \| 1.2685 \| 0.0212 \| 1.2685 \| 1.1263 \| \| No log \| 6.0 \| 24 \| 0.8649 \| 0.2385 \| 0.8649 \| 0.9300 \| \| No log \| 7.0 \| 28 \| 1.0686 \| 0.0316 \| 1.0686 \| 1.0337 \| \| No log \| 8.0 \| 32 \| 0.8996 \| 0.1438 \| 0.8996 \| 0.9484 \| \| No log \| 9.0 \| 36 \| 1.1454 \| 0.1187 \| 1.1454 \| 1.0702 \| \| No log \| 10.0 \| 40 \| 0.7038 \| 0.4140 \| 0.7038 \| 0.8389 \| \| No log \| 11.0 \| 44 \| 1.1480 \| 0.2873 \| 1.1480 \| 1.0714 \| \| No log \| 12.0 \| 48 \| 1.5211 \| 0.2361 \| 1.5211 \| 1.2333 \| \| No log \| 13.0 \| 52 \| 0.9304 \| 0.3550 \| 0.9304 \| 0.9646 \| \| No log \| 14.0 \| 56 \| 1.4282 \| 0.2510 \| 1.4282 \| 1.1951 \| \| No log \| 15.0 \| 60 \| 0.8071 \| 0.4307 \| 0.8071 \| 0.8984 \| \| No log \| 16.0 \| 64 \| 2.0963 \| 0.1655 \| 2.0963 \| 1.4479 \| \| No log \| 17.0 \| 68 \| 0.8056 \| 0.4079 \| 0.8056 \| 0.8975 \| \| No log \| 18.0 \| 72 \| 1.0796 \| 0.2796 \| 1.0796 \| 1.0390 \| \| No log \| 19.0 \| 76 \| 1.9390 \| 0.1777 \| 1.9390 \| 1.3925 \| \| No log \| 20.0 \| 80 \| 0.8374 \| 0.3923 \| 0.8374 \| 0.9151 \| \| No log \| 21.0 \| 84 \| 2.3617 \| 0.0986 \| 2.3617 \| 1.5368 \| \| No log \| 22.0 \| 88 \| 1.2043 \| 0.3066 \| 1.2043 \| 1.0974 \| \| No log \| 23.0 \| 92 \| 1.1280 \| 0.2987 \| 1.1280 \| 1.0621 \| \| No log \| 24.0 \| 96 \| 1.3330 \| 0.2646 \| 1.3330 \| 1.1546 \| \| No log \| 25.0 \| 100 \| 0.9613 \| 0.4131 \| 0.9613 \| 0.9805 \| \| No log \| 26.0 \| 104 \| 1.7648 \| 0.2242 \| 1.7648 \| 1.3285 \| \| No log \| 27.0 \| 108 \| 1.1857 \| 0.3002 \| 1.1857 \| 1.0889 \| \| No log \| 28.0 \| 112 \| 1.1555 \| 0.2721 \| 1.1555 \| 1.0749 \| \| No log \| 29.0 \| 116 \| 1.2923 \| 0.2589 \| 1.2923 \| 1.1368 \| \| No log \| 30.0 \| 120 \| 1.0838 \| 0.3036 \| 1.0838 \| 1.0410 \| ### Framework versions - Transformers 4.47.0 - Pytorch 2.5.1+cu121 - Datasets 3.3.1 - Tokenizers 0.21.0
neemdogs/Matsunuma_LoRA	neemdogs	2025-04-06T15:26:02Z	2	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-04-05T10:38:07Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: photo collage in Shingo Matsunuma style widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - neemdogs/Matsunuma_LoRA <Gallery /> ## Model description These are neemdogs/Matsunuma_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use photo collage in Shingo Matsunuma style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](neemdogs/Matsunuma_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
gokulsrinivasagan/bert_base_train_book_ent_1_sst2	gokulsrinivasagan	2025-04-06T15:25:52Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "base_model:gokulsrinivasagan/bert_base_train_book_ent_1", "base_model:finetune:gokulsrinivasagan/bert_base_train_book_ent_1", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-06T15:05:58Z	--- library_name: transformers language: - en license: apache-2.0 base_model: gokulsrinivasagan/bert_base_train_book_ent_1 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: bert_base_train_book_ent_1_sst2 results: - task: name: Text Classification type: text-classification dataset: name: GLUE SST2 type: glue args: sst2 metrics: - name: Accuracy type: accuracy value: 0.7855504587155964 --- <!-- 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_train_book_ent_1_sst2 This model is a fine-tuned version of [gokulsrinivasagan/bert_base_train_book_ent_1](https://huggingface.co/gokulsrinivasagan/bert_base_train_book_ent_1) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.4580 - Accuracy: 0.7856 ## 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: 256 - eval_batch_size: 256 - seed: 10 - 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: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5149 \| 1.0 \| 264 \| 0.4580 \| 0.7856 \| \| 0.2656 \| 2.0 \| 528 \| 0.4982 \| 0.7982 \| \| 0.2013 \| 3.0 \| 792 \| 0.4869 \| 0.7890 \| \| 0.1658 \| 4.0 \| 1056 \| 0.5880 \| 0.7913 \| \| 0.1377 \| 5.0 \| 1320 \| 0.6522 \| 0.7833 \| \| 0.1153 \| 6.0 \| 1584 \| 0.6965 \| 0.7798 \| ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
Pituresque/kgf	Pituresque	2025-04-06T15:23:07Z	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	2025-04-06T14:57:49Z	--- 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: KGF --- # Kgf <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 `KGF` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "KGF", "lora_weights": "https://huggingface.co/Pituresque/kgf/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('Pituresque/kgf', weight_name='lora.safetensors') image = pipeline('KGF').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/Pituresque/kgf/discussions) to add images that show off what you’ve made with this LoRA.
mrg3ek/arazn-whisper-small	mrg3ek	2025-04-06T15:20:01Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "en", "ar", "dataset:ahmedheakl/arzen-llm-speech-ds", "arxiv:2406.18120", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-04-06T14:46:06Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: openai/whisper-small model-index: - name: arazn-whisper-small-v2 results: [] datasets: - ahmedheakl/arzen-llm-speech-ds language: - en - ar metrics: - bleu - cer - wer library_name: transformers pipeline_tag: automatic-speech-recognition --- # Model Card for Model ID Please see paper & code for more information: - https://github.com/ahmedheakl/arazn-llm - https://arxiv.org/abs/2406.18120 ## Citation BibTeX: ``` @article{heakl2024arzen, title={ArzEn-LLM: Code-Switched Egyptian Arabic-English Translation and Speech Recognition Using LLMs}, author={Heakl, Ahmed and Zaghloul, Youssef and Ali, Mennatullah and Hossam, Rania and Gomaa, Walid}, journal={arXiv preprint arXiv:2406.18120}, year={2024} } ``` ## Model Card Authors - Email: [email protected] - Linkedin: https://linkedin.com/in/ahmed-heakl <!-- 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. --> # arazn-whisper-small-v2 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
elyuzhakova/ill_style_LoRA	elyuzhakova	2025-04-06T15:18:47Z	3	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-04-03T15:57:14Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: a photo of TOK dog widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - elyuzhakova/ill_style_LoRA <Gallery /> ## Model description These are elyuzhakova/ill_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of TOK dog to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](elyuzhakova/ill_style_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
viethoangdata/lora-llama3-8b-finetuned-v1	viethoangdata	2025-04-06T15:18:22Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-06T15:18:07Z	--- base_model: unsloth/llama-3-8b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: viethoangdata - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-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)

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