Split (1)

train · 787k rows

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags sequence	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
eleasimmen/elea	eleasimmen	2025-05-29T10:10:03Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-29T09:34:07Z	--- 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: elea --- # Elea <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 `elea` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "elea", "lora_weights": "https://huggingface.co/eleasimmen/elea/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('eleasimmen/elea', weight_name='lora.safetensors') image = pipeline('elea').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/eleasimmen/elea/discussions) to add images that show off what you’ve made with this LoRA.
dhruvsangani/Feat_ChatBot2-GGUF	dhruvsangani	2025-05-29T10:05:56Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-29T10:05:40Z	--- base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: dhruvsangani - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Diamantis99/mbBpnRP	Diamantis99	2025-05-29T09:56:34Z	0	0	segmentation-models-pytorch	[ "segmentation-models-pytorch", "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "semantic-segmentation", "pytorch", "image-segmentation", "license:mit", "region:us" ]	image-segmentation	2025-05-29T09:56:21Z	--- library_name: segmentation-models-pytorch license: mit pipeline_tag: image-segmentation tags: - model_hub_mixin - pytorch_model_hub_mixin - segmentation-models-pytorch - semantic-segmentation - pytorch languages: - python --- # PAN Model Card Table of Contents: - [Load trained model](#load-trained-model) - [Model init parameters](#model-init-parameters) - [Model metrics](#model-metrics) - [Dataset](#dataset) ## Load trained model ```python import segmentation_models_pytorch as smp model = smp.from_pretrained("<save-directory-or-this-repo>") ``` ## Model init parameters ```python model_init_params = { "encoder_name": "resnet152", "encoder_depth": 5, "encoder_weights": "imagenet", "encoder_output_stride": 16, "decoder_channels": 32, "decoder_interpolation": "bilinear", "in_channels": 3, "classes": 1, "activation": None, "upsampling": 4, "aux_params": None } ``` ## Model metrics ```json [ { "test_per_image_iou": 0.8172159790992737, "test_dataset_iou": 0.8556613922119141 } ] ``` ## Dataset Dataset name: VisionPipe ## More Information - Library: https://github.com/qubvel/segmentation_models.pytorch - Docs: https://smp.readthedocs.io/en/latest/ This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin)
DmytroSerbeniuk/my-iris-model	DmytroSerbeniuk	2025-05-29T09:48:59Z	2	0	scikit-learn	[ "scikit-learn", "joblib", "logistic_regression", "license:apache-2.0", "region:us" ]	null	2025-05-28T16:31:58Z	--- license: apache-2.0 library_name: scikit-learn base_model: - model.joblib --- <p align="left"> <a href="https://test.org/"> <img src="https://img.shields.io/badge/Website-0A66C2?logo=safari&logoColor=white" style="display: inline-block; vertical-align: middle;" alt="Website" /> </a> <a href="https://github.com/" target="_blank" style="margin: 2px;"> <img alt="Github" src="https://img.shields.io/badge/Codebase-536af5?color=536af5&logo=github" style="display: inline-block; vertical-align: middle;" alt="Codebase" /> </a> <a href="https://demo.org/"> <img src="https://img.shields.io/badge/Demo-blue?logo=googleplay&logoColor=white" style="display: inline-block; vertical-align: middle;" alt="Demo" /> </a> </p> # 🌸 Iris Species Classifier (Logistic Regression) This repository provides a lightweight logistic regression model trained on the classic [Iris dataset](https://archive.ics.uci.edu/ml/datasets/iris) using scikit-learn. It is ideal for educational purposes, experimentation, and demonstration of inference on tabular data. ## 🧠 Model Overview - Algorithm: Logistic Regression - Framework: scikit-learn - Features: - `sepal_length` - `sepal_width` - `petal_length` - `petal_width` - Target Classes: - `setosa` - `versicolor` - `virginica` ## 🚀 Usage Example ### Python Code ```python import joblib import pandas as pd model = joblib.load("model.joblib") sample = pd.DataFrame([[5.1, 3.5, 1.4, 0.2]], columns=["sepal_length", "sepal_width", "petal_length", "petal_width"]) prediction = model.predict(sample) print(f"🌸 Predicted class: {prediction[0]}") ``` ## 🔄 How to load the model ```python from huggingface_hub import hf_hub_download import joblib model_path = hf_hub_download("DmytroSerbeniuk/my-iris-model", "model.joblib") model = joblib.load(model_path) ``` ## CLI Inference You can also use the provided `inference.py` script: ```python python3 inference.py 6.0 2.2 4.0 1.0 ``` Expected Output: ```test 🌸 Predicted class: versicolor ``` ## 🧪 Sample Predictions \| Sepal Length \| Sepal Width \| Petal Length \| Petal Width \| Prediction \| \| ------------ \| ----------- \| ------------ \| ----------- \| ---------- \| \| 5.1 \| 3.5 \| 1.4 \| 0.2 \| setosa \| \| 6.0 \| 2.2 \| 4.0 \| 1.0 \| versicolor \| \| 6.9 \| 3.1 \| 5.4 \| 2.1 \| virginica \| ## 📦 Requirements Dependencies listed in `requirements.txt`: ```text scikit-learn pandas joblib ``` ## License my-iris-model is licensed under the Apache 2.0 license
while0628/merged_model_WOQ_epoch401	while0628	2025-05-29T09:48:15Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T09:45:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
prithivMLmods/facial-age-detection	prithivMLmods	2025-05-29T09:41:57Z	0	0	transformers	[ "transformers", "safetensors", "siglip", "image-classification", "age-detection", "SigLIP2", "biology", "en", "dataset:prithivMLmods/Face-Age-10K", "arxiv:2502.14786", "base_model:google/siglip2-base-patch16-512", "base_model:finetune:google/siglip2-base-patch16-512", "doi:10.57967/hf/5659", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-27T16:37:21Z	--- license: apache-2.0 datasets: - prithivMLmods/Face-Age-10K language: - en base_model: - google/siglip2-base-patch16-512 pipeline_tag: image-classification library_name: transformers tags: - age-detection - SigLIP2 - biology --- ![467.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/LA6do4hgVi-zNarLEhVGR.png) # facial-age-detection > facial-age-detection is a vision-language encoder model fine-tuned from `google/siglip2-base-patch16-512` for multi-class image classification. It is trained to detect and classify human faces into age groups ranging from early childhood to elderly adults. The model uses the `SiglipForImageClassification` architecture. > \[!note] > SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features > [https://arxiv.org/pdf/2502.14786](https://arxiv.org/pdf/2502.14786) ```py Classification Report: precision recall f1-score support age 01-10 0.9614 0.9669 0.9641 2474 age 11-20 0.8418 0.8467 0.8442 1181 age 21-30 0.8118 0.8326 0.8220 1523 age 31-40 0.6937 0.6683 0.6808 1010 age 41-55 0.7106 0.7528 0.7311 1181 age 56-65 0.6878 0.6646 0.6760 799 age 66-80 0.7949 0.7596 0.7768 653 age 80 + 0.9349 0.8343 0.8817 344 accuracy 0.8225 9165 macro avg 0.8046 0.7907 0.7971 9165 weighted avg 0.8226 0.8225 0.8223 9165 ``` ![download (1).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/E_8ykSA-ZqEK0_Jtch5dD.png) --- ## Label Space: 8 Classes ``` Class 0: age 01-10 Class 1: age 11-20 Class 2: age 21-30 Class 3: age 31-40 Class 4: age 41-55 Class 5: age 56-65 Class 6: age 66-80 Class 7: age 80 + ``` --- ## Install Dependencies ```bash pip install -q transformers torch pillow gradio hf_xet ``` --- ## Inference Code ```python import gradio as gr from transformers import AutoImageProcessor, SiglipForImageClassification from PIL import Image import torch # Load model and processor model_name = "prithivMLmods/facial-age-detection" # Update with actual model name on Hugging Face model = SiglipForImageClassification.from_pretrained(model_name) processor = AutoImageProcessor.from_pretrained(model_name) # Updated label mapping id2label = { "0": "age 01-10", "1": "age 11-20", "2": "age 21-30", "3": "age 31-40", "4": "age 41-55", "5": "age 56-65", "6": "age 66-80", "7": "age 80 +" } def classify_image(image): image = Image.fromarray(image).convert("RGB") inputs = processor(images=image, return_tensors="pt") with torch.no_grad(): outputs = model(*inputs) logits = outputs.logits probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist() prediction = { id2label[str(i)]: round(probs[i], 3) for i in range(len(probs)) } return prediction # Gradio Interface iface = gr.Interface( fn=classify_image, inputs=gr.Image(type="numpy"), outputs=gr.Label(num_top_classes=8, label="Age Group Classification"), title="Facial Age Detection", description="Upload a face image to estimate the age group: 01–10, 11–20, 21–30, 31–40, 41–55, 56–65, 66–80, or 80+." ) if __name__ == "__main__": iface.launch() ``` --- ## Intended Use `facial-age-detection` is designed for: Demographic Analytics – Estimate age distributions in image datasets for research and commercial analysis. * Access Control & Verification – Enforce age-based access in digital or physical environments. * Retail & Marketing – Understand customer demographics in retail spaces through camera-based analytics. * Surveillance & Security – Enhance people classification systems by integrating age detection. * Human-Computer Interaction – Adapt experiences and interfaces based on user age.
Nerva1228/huakuku	Nerva1228	2025-05-29T09:39:59Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-29T09:13:25Z	--- 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: huakuku --- # Huakuku <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 `huakuku` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "huakuku", "lora_weights": "https://huggingface.co/Nerva1228/huakuku/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('Nerva1228/huakuku', weight_name='lora.safetensors') image = pipeline('huakuku').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/Nerva1228/huakuku/discussions) to add images that show off what you’ve made with this LoRA.
Sucube131/lora-sofia_v2	Sucube131	2025-05-29T09:39:27Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-29T08:57:39Z	--- 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: Sofia --- # Lora Sofia_V2 <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 `Sofia` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Sofia", "lora_weights": "https://huggingface.co/Sucube131/lora-sofia_v2/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('Sucube131/lora-sofia_v2', weight_name='lora.safetensors') image = pipeline('Sofia').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: 4000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Sucube131/lora-sofia_v2/discussions) to add images that show off what you’ve made with this LoRA.
Killer-C0wboy/phi4_ibps	Killer-C0wboy	2025-05-29T09:20:51Z	0	0	null	[ "safetensors", "law", "bail", "bail-prediction", "IIT", "IITK", "IBPS", "text-generation", "en", "base_model:microsoft/phi-4", "base_model:finetune:microsoft/phi-4", "license:apache-2.0", "region:us" ]	text-generation	2025-05-29T09:14:23Z	--- license: apache-2.0 language: - en base_model: - microsoft/phi-4 pipeline_tag: text-generation tags: - law - bail - bail-prediction - IIT - IITK - IBPS --- As of now using the model's own tokenizer causes errors sometimes. If that happens use phi-4's own tokenizer.
maghwa/llama-3.1-8b-powl-improved-100steps-merged	maghwa	2025-05-29T09:19:32Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T09:08:08Z	--- 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]
kkkkl5/asdkklkk	kkkkl5	2025-05-29T09:13:29Z	2	0	null	[ "pytorch", "onnx", "safetensors", "modernbert", "region:us" ]	null	2025-05-29T05:38:42Z	### 以上模型为数据微调后的ModernBERT-base模型，用于pan2025-task1，非商用，若有侵权提醒请及时私聊作者及时下架，感谢包涵
hdong0/Qwen2.5-Math-1.5B-Open-R1-Distill_4epoch	hdong0	2025-05-29T09:07:01Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "sft", "conversational", "dataset:open-r1/OpenR1-Math-220k", "base_model:Qwen/Qwen2.5-Math-1.5B", "base_model:finetune:Qwen/Qwen2.5-Math-1.5B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T01:04:21Z	--- base_model: Qwen/Qwen2.5-Math-1.5B datasets: open-r1/OpenR1-Math-220k library_name: transformers model_name: Qwen2.5-Math-1.5B-Open-R1-Distill_4epoch tags: - generated_from_trainer - open-r1 - trl - sft licence: license --- # Model Card for Qwen2.5-Math-1.5B-Open-R1-Distill_4epoch This model is a fine-tuned version of [Qwen/Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) on the [open-r1/OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="hdong0/Qwen2.5-Math-1.5B-Open-R1-Distill_4epoch", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.18.0.dev0 - Transformers: 4.52.0.dev0 - Pytorch: 2.6.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
tungduong261204/sft_9000	tungduong261204	2025-05-29T09:06:00Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T09:05:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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LarryAIDraw/Genshin_Signora-000001	LarryAIDraw	2025-05-29T09:02:27Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-05-29T08:25:36Z	--- license: creativeml-openrail-m --- https://civitai.com/models/1618395/genshin-signora
IntMeGroup/DFBench_qwen25	IntMeGroup	2025-05-29T09:01:41Z	0	0	null	[ "safetensors", "qwen2_5_vl", "license:apache-2.0", "region:us" ]	null	2025-05-29T07:13:57Z	--- license: apache-2.0 ---
lalallaal7777/finance-llm	lalallaal7777	2025-05-29T08:59:07Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1", "region:us" ]	null	2025-05-29T08:59:04Z	--- base_model: mistralai/Mistral-7B-Instruct-v0.1 library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
while0628/1B_merged_model_dt_1_lora300	while0628	2025-05-29T08:48:49Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T08:45:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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BootesVoid/cmb92t64204r01b1ykpvg18oo_cmb93cbw7054f1b1y3nmlc89f	BootesVoid	2025-05-29T08:35:30Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-29T08:35:29Z	--- 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: sienna_cruz --- # Cmb92T64204R01B1Ykpvg18Oo_Cmb93Cbw7054F1B1Y3Nmlc89F <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 `sienna_cruz` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "sienna_cruz", "lora_weights": "https://huggingface.co/BootesVoid/cmb92t64204r01b1ykpvg18oo_cmb93cbw7054f1b1y3nmlc89f/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmb92t64204r01b1ykpvg18oo_cmb93cbw7054f1b1y3nmlc89f', weight_name='lora.safetensors') image = pipeline('sienna_cruz').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmb92t64204r01b1ykpvg18oo_cmb93cbw7054f1b1y3nmlc89f/discussions) to add images that show off what you’ve made with this LoRA.
BootesVoid/cmb92evzk04g21b1y80xizpff_cmb92l2ab04lq1b1ytwn5zjlb	BootesVoid	2025-05-29T08:34:23Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-29T08:34:22Z	--- 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: aylina_dev --- # Cmb92Evzk04G21B1Y80Xizpff_Cmb92L2Ab04Lq1B1Ytwn5Zjlb <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 `aylina_dev` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "aylina_dev", "lora_weights": "https://huggingface.co/BootesVoid/cmb92evzk04g21b1y80xizpff_cmb92l2ab04lq1b1ytwn5zjlb/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmb92evzk04g21b1y80xizpff_cmb92l2ab04lq1b1ytwn5zjlb', weight_name='lora.safetensors') image = pipeline('aylina_dev').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmb92evzk04g21b1y80xizpff_cmb92l2ab04lq1b1ytwn5zjlb/discussions) to add images that show off what you’ve made with this LoRA.
BootesVoid/cmb915d9w03n61b1y89idkvhm_cmb9259pd04a41b1ysxb0qqhq	BootesVoid	2025-05-29T08:34:20Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-29T08:34:18Z	--- 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: emma_dixit --- # Cmb915D9W03N61B1Y89Idkvhm_Cmb9259Pd04A41B1Ysxb0Qqhq <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 `emma_dixit` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "emma_dixit", "lora_weights": "https://huggingface.co/BootesVoid/cmb915d9w03n61b1y89idkvhm_cmb9259pd04a41b1ysxb0qqhq/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmb915d9w03n61b1y89idkvhm_cmb9259pd04a41b1ysxb0qqhq', weight_name='lora.safetensors') image = pipeline('emma_dixit').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmb915d9w03n61b1y89idkvhm_cmb9259pd04a41b1ysxb0qqhq/discussions) to add images that show off what you’ve made with this LoRA.
nuwooo/test-upload	nuwooo	2025-05-29T08:22:40Z	0	0	null	[ "t5", "en", "dataset:c4", "arxiv:2002.05202", "arxiv:1910.10683", "license:apache-2.0", "region:us" ]	null	2025-05-28T13:53:09Z	--- language: en datasets: - c4 license: apache-2.0 --- [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Version 1.1 ## Version 1.1 [T5 Version 1.1](https://github.com/google-research/text-to-text-transfer-transformer/blob/master/released_checkpoints.md#t511) includes the following improvements compared to the original T5 model- GEGLU activation in feed-forward hidden layer, rather than ReLU - see [here](https://arxiv.org/abs/2002.05202). - Dropout was turned off in pre-training (quality win). Dropout should be re-enabled during fine-tuning. - Pre-trained on C4 only without mixing in the downstream tasks. - no parameter sharing between embedding and classifier layer - "xl" and "xxl" replace "3B" and "11B". The model shapes are a bit different - larger `d_model` and smaller `num_heads` and `d_ff`. Note: T5 Version 1.1 was only pre-trained on C4 excluding any supervised training. Therefore, this model has to be fine-tuned before it is useable on a downstream task. Pretraining Dataset: [C4](https://huggingface.co/datasets/c4) Other Community Checkpoints: [here](https://huggingface.co/models?search=t5-v1_1) Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) Authors: Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu ## Abstract Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code. ![model image](https://camo.githubusercontent.com/623b4dea0b653f2ad3f36c71ebfe749a677ac0a1/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f343030362f312a44304a31674e51663876727255704b657944387750412e706e67)
jack-perlo/MobileNet-Imagenet2012	jack-perlo	2025-05-29T08:18:50Z	0	0	keras	[ "keras", "tflite", "license:apache-2.0", "region:us" ]	null	2025-05-29T08:17:10Z	--- license: apache-2.0 ---
AJNG/olm_ocr_v2_merge	AJNG	2025-05-29T08:11:38Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_vl", "image-text-to-text", "text-generation-inference", "unsloth", "conversational", "en", "base_model:allenai/olmOCR-7B-0225-preview", "base_model:finetune:allenai/olmOCR-7B-0225-preview", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-29T08:10:43Z	--- base_model: allenai/olmOCR-7B-0225-preview tags: - text-generation-inference - transformers - unsloth - qwen2_vl license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: AJNG - License: apache-2.0 - Finetuned from model : allenai/olmOCR-7B-0225-preview 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)
Nastoi/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-curious_nocturnal_sandpiper	Nastoi	2025-05-29T08:05:08Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am curious nocturnal sandpiper", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-14T14:11:41Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-curious_nocturnal_sandpiper tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am curious nocturnal sandpiper - unsloth - trl licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-curious_nocturnal_sandpiper This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Nastoi/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-curious_nocturnal_sandpiper", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Varinder2110/cb48a888-a11c-481c-9342-edbe79d71a40	Varinder2110	2025-05-29T08:01:00Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-29T06:54:23Z	--- 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 --- # Cb48A888 A11C 481C 9342 Edbe79D71A40 <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/Varinder2110/cb48a888-a11c-481c-9342-edbe79d71a40/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('Varinder2110/cb48a888-a11c-481c-9342-edbe79d71a40', 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: 6000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Varinder2110/cb48a888-a11c-481c-9342-edbe79d71a40/discussions) to add images that show off what you’ve made with this LoRA.
ankhanhtran02/Llama-DPO-3	ankhanhtran02	2025-05-29T07:53:42Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "dpo", "arxiv:2305.18290", "base_model:unsloth/Llama-3.2-1B", "base_model:finetune:unsloth/Llama-3.2-1B", "endpoints_compatible", "region:us" ]	null	2025-05-29T06:50:14Z	--- base_model: unsloth/Llama-3.2-1B library_name: transformers model_name: Llama-DPO-3 tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for Llama-DPO-3 This model is a fine-tuned version of [unsloth/Llama-3.2-1B](https://huggingface.co/unsloth/Llama-3.2-1B). 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="ankhanhtran02/Llama-DPO-3", 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/khanh-hust/huggingface/runs/gocdq2fj) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.18.0 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
HuangXinBa/q-FrozenLake-v1-8x8-noSlippery	HuangXinBa	2025-05-29T07:52:55Z	0	0	null	[ "FrozenLake-v1-8x8-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-05-29T07:16:25Z	--- tags: - FrozenLake-v1-8x8-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-8x8-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-8x8-no_slippery type: FrozenLake-v1-8x8-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="HuangXinBa/q-FrozenLake-v1-8x8-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
huangqishan/cnn	huangqishan	2025-05-29T07:51:03Z	58	0	transformers	[ "transformers", "safetensors", "cnn_model", "image-classification", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	image-classification	2025-05-27T00:33:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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crystalai/thoth-emerald-cybersecurity-shield-ai-auto-train-autotrain-advanced	crystalai	2025-05-29T07:41:49Z	0	1	adapter-transformers	[ "adapter-transformers", "chemistry", "biology", "legal", "music", "art", "code", "climate", "text-generation-inference", "merge", "dataset:DMindAI/DMind_Benchmark", "dataset:nvidia/OpenMathReasoning", "dataset:nvidia/OpenCodeReasoning", "base_model:ACE-Step/ACE-Step-v1-3.5B", "base_model:adapter:ACE-Step/ACE-Step-v1-3.5B", "license:c-uda", "region:us" ]	null	2025-05-29T07:39:14Z	--- license: c-uda datasets: - DMindAI/DMind_Benchmark - nvidia/OpenMathReasoning - nvidia/OpenCodeReasoning metrics: - character - accuracy base_model: - ACE-Step/ACE-Step-v1-3.5B - google/gemma-3n-E4B-it-litert-preview new_version: nvidia/parakeet-tdt-0.6b-v2 library_name: adapter-transformers tags: - chemistry - biology - legal - music - art - code - climate - text-generation-inference - merge ---
kmpartner/k5pcmlra-test	kmpartner	2025-05-29T07:40:12Z	150	0	peft	[ "peft", "tensorboard", "diffusers", "safetensors", "arxiv:1910.09700", "base_model:kmpartner/k512-ft-test", "base_model:adapter:kmpartner/k512-ft-test", "region:us" ]	null	2025-04-27T12:32:18Z	--- base_model: kmpartner/k512-ft-test library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2 - PEFT 0.14.0
fuji12345/segformer-finetuned-sidewalk-10k-steps	fuji12345	2025-05-29T07:25:26Z	0	0	transformers	[ "transformers", "safetensors", "segformer", "image-segmentation", "vision", "generated_from_trainer", "base_model:nvidia/mit-b0", "base_model:finetune:nvidia/mit-b0", "license:other", "endpoints_compatible", "region:us" ]	image-segmentation	2025-05-29T07:23:23Z	--- library_name: transformers license: other base_model: nvidia/mit-b0 tags: - image-segmentation - vision - generated_from_trainer model-index: - name: segformer-finetuned-sidewalk-10k-steps 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. --> # segformer-finetuned-sidewalk-10k-steps This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the fuji12345/sample-segmentation_dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.1181 - Mean Iou: 0.0 - Mean Accuracy: nan - Overall Accuracy: nan - Accuracy Hallucination: nan - Accuracy Normal: nan - Iou Hallucination: 0.0 - Iou Normal: 0.0 ## 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: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: polynomial - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Hallucination \| Accuracy Normal \| Iou Hallucination \| Iou Normal \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:----------------------:\|:---------------:\|:-----------------:\|:----------:\| \| No log \| 1.0 \| 21 \| 0.4468 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| No log \| 2.0 \| 42 \| 0.2196 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| No log \| 3.0 \| 63 \| 0.2136 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| No log \| 4.0 \| 84 \| 0.1816 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.3217 \| 5.0 \| 105 \| 0.1449 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.3217 \| 6.0 \| 126 \| 0.1380 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.3217 \| 7.0 \| 147 \| 0.1239 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.3217 \| 8.0 \| 168 \| 0.1247 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.3217 \| 9.0 \| 189 \| 0.1163 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| \| 0.1596 \| 9.5238 \| 200 \| 0.1181 \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| ### Framework versions - Transformers 4.53.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
Khal5454/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bristly_beaked_hedgehog	Khal5454	2025-05-29T07:18:17Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am bristly beaked hedgehog", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-15T00:41:50Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bristly_beaked_hedgehog tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am bristly beaked hedgehog - unsloth - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bristly_beaked_hedgehog 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="Khal5454/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bristly_beaked_hedgehog", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
yamatazen/HMS-Fusion-12B	yamatazen	2025-05-29T07:15:27Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "en", "ja", "base_model:shisa-ai/shisa-v2-mistral-nemo-12b", "base_model:merge:shisa-ai/shisa-v2-mistral-nemo-12b", "base_model:yamatazen/Himeyuri-Magnum-12B", "base_model:merge:yamatazen/Himeyuri-Magnum-12B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T06:24:47Z	--- base_model: - yamatazen/Himeyuri-Magnum-12B - shisa-ai/shisa-v2-mistral-nemo-12b library_name: transformers tags: - mergekit - merge language: - en - ja --- ![image/png](https://huggingface.co/yamatazen/HMS-Fusion-12B/resolve/main/HMS-Fusion-12B.png?download=true) # HMS-Fusion-12B 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 [Arcee Fusion](https://arcee.ai) merge method using [shisa-ai/shisa-v2-mistral-nemo-12b](https://huggingface.co/shisa-ai/shisa-v2-mistral-nemo-12b) as a base. ### Models Merged The following models were included in the merge: * [yamatazen/Himeyuri-Magnum-12B](https://huggingface.co/yamatazen/Himeyuri-Magnum-12B) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: arcee_fusion dtype: bfloat16 out_dtype: bfloat16 base_model: shisa-ai/shisa-v2-mistral-nemo-12b models: - model: yamatazen/Himeyuri-Magnum-12B ```
AshwiniFromIITK/gemma-3-0_1b_NewDS1.0	AshwiniFromIITK	2025-05-29T07:13:55Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3_text", "trl", "en", "base_model:unsloth/gemma-3-1b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-1b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-29T07:13:30Z	--- base_model: unsloth/gemma-3-1b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3_text - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: AshwiniFromIITK - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-1b-it-unsloth-bnb-4bit This gemma3_text 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)
unsloth/DeepSeek-R1-GGUF	unsloth	2025-05-29T07:12:29Z	80,628	1,074	transformers	[ "transformers", "gguf", "deepseek_v3", "text-generation", "deepseek", "unsloth", "custom_code", "en", "arxiv:2501.12948", "base_model:deepseek-ai/DeepSeek-R1", "base_model:quantized:deepseek-ai/DeepSeek-R1", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-01-20T13:09:42Z	--- base_model: deepseek-ai/DeepSeek-R1 language: - en library_name: transformers license: mit tags: - deepseek - unsloth - transformers new_version: unsloth/DeepSeek-R1-GGUF-UD --- <div> <p style="margin-bottom: 0; margin-top: 0;"> <strong>See <a href="https://huggingface.co/collections/unsloth/deepseek-r1-all-versions-678e1c48f5d2fce87892ace5">our collection</a> for versions of Deepseek-R1 including GGUF & 4-bit formats.</strong> </p> <p style="margin-bottom: 0;"> <em>Unsloth's DeepSeek-R1 <a href="https://unsloth.ai/blog/deepseekr1-dynamic">1.58-bit + 2-bit Dynamic Quants</a> is selectively quantized, greatly improving accuracy over standard 1-bit/2-bit.</em> </p> <div style="display: flex; gap: 5px; align-items: center; "> <a href="https://github.com/unslothai/unsloth/"> <img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133"> </a> <a href="https://discord.gg/unsloth"> <img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173"> </a> <a href="https://docs.unsloth.ai/basics/tutorial-how-to-run-deepseek-r1-on-your-own-local-device"> <img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143"> </a> </div> <h1 style="margin-top: 0rem;">Instructions to run this model in llama.cpp:</h2> </div> Or you can view more detailed instructions here: [unsloth.ai/blog/deepseekr1-dynamic](https://unsloth.ai/blog/deepseekr1-dynamic) 1. Do not forget about `<｜User｜>` and `<｜Assistant｜>` tokens! - Or use a chat template formatter 2. Obtain the latest `llama.cpp` at https://github.com/ggerganov/llama.cpp. You can follow the build instructions below as well: ```bash apt-get update apt-get install build-essential cmake curl libcurl4-openssl-dev -y git clone https://github.com/ggerganov/llama.cpp cmake llama.cpp -B llama.cpp/build \ -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON cmake --build llama.cpp/build --config Release -j --clean-first --target llama-quantize llama-cli llama-gguf-split cp llama.cpp/build/bin/llama-* llama.cpp ``` 3. It's best to use `--min-p 0.05` to counteract very rare token predictions - I found this to work well especially for the 1.58bit model. 4. Download the model via: ```python # pip install huggingface_hub hf_transfer # import os # Optional for faster downloading # os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1" from huggingface_hub import snapshot_download snapshot_download( repo_id = "unsloth/DeepSeek-R1-GGUF", local_dir = "DeepSeek-R1-GGUF", allow_patterns = ["UD-IQ1_S"], # Select quant type UD-IQ1_S for 1.58bit ) ``` 5. Example with Q4_0 K quantized cache Notice -no-cnv disables auto conversation mode ```bash ./llama.cpp/llama-cli \ --model DeepSeek-R1-GGUF/DeepSeek-R1-UD-IQ1_S/DeepSeek-R1-UD-IQ1_S-00001-of-00003.gguf \ --cache-type-k q4_0 \ --threads 12 -no-cnv --prio 2 \ --temp 0.6 \ --ctx-size 8192 \ --seed 3407 \ --prompt "<｜User｜>Create a Flappy Bird game in Python.<｜Assistant｜>" ``` Example output: ```txt <think> Okay, so I need to figure out what 1 plus 1 is. Hmm, where do I even start? I remember from school that adding numbers is pretty basic, but I want to make sure I understand it properly. Let me think, 1 plus 1. So, I have one item and I add another one. Maybe like a apple plus another apple. If I have one apple and someone gives me another, I now have two apples. So, 1 plus 1 should be 2. That makes sense. Wait, but sometimes math can be tricky. Could it be something else? Like, in a different number system maybe? But I think the question is straightforward, using regular numbers, not like binary or hexadecimal or anything. I also recall that in arithmetic, addition is combining quantities. So, if you have two quantities of 1, combining them gives you a total of 2. Yeah, that seems right. Is there a scenario where 1 plus 1 wouldn't be 2? I can't think of any... ``` 6. If you have a GPU (RTX 4090 for example) with 24GB, you can offload multiple layers to the GPU for faster processing. If you have multiple GPUs, you can probably offload more layers. ```bash ./llama.cpp/llama-cli \ --model DeepSeek-R1-GGUF/DeepSeek-R1-UD-IQ1_S/DeepSeek-R1-UD-IQ1_S-00001-of-00003.gguf \ --cache-type-k q4_0 \ --threads 12 -no-cnv --prio 2 \ --n-gpu-layers 7 \ --temp 0.6 \ --ctx-size 8192 \ --seed 3407 \ --prompt "<｜User｜>Create a Flappy Bird game in Python.<｜Assistant｜>" ``` 7. If you want to merge the weights together, use this script: ``` ./llama.cpp/llama-gguf-split --merge \ DeepSeek-R1-GGUF/DeepSeek-R1-UD-IQ1_S-00001-of-00003.gguf \ merged_file.gguf ``` \| MoE Bits \| Type \| Disk Size \| Accuracy \| Link \| Details \| \| -------- \| -------- \| ------------ \| ------------ \| ---------------------\| ---------- \| \| 1.58bit \| UD-IQ1_S \| 131GB \| Fair \| [Link](https://huggingface.co/unsloth/DeepSeek-R1-GGUF/tree/main/DeepSeek-R1-UD-IQ1_S) \| MoE all 1.56bit. `down_proj` in MoE mixture of 2.06/1.56bit \| \| 1.73bit \| UD-IQ1_M \| 158GB \| Good \| [Link](https://huggingface.co/unsloth/DeepSeek-R1-GGUF/tree/main/DeepSeek-R1-UD-IQ1_M) \| MoE all 1.56bit. `down_proj` in MoE left at 2.06bit \| \| 2.22bit \| UD-IQ2_XXS \| 183GB \| Better \| [Link](https://huggingface.co/unsloth/DeepSeek-R1-GGUF/tree/main/DeepSeek-R1-UD-IQ2_XXS) \| MoE all 2.06bit. `down_proj` in MoE mixture of 2.5/2.06bit \| \| 2.51bit \| UD-Q2_K_XL \| 212GB \| Best \| [Link](https://huggingface.co/unsloth/DeepSeek-R1-GGUF/tree/main/DeepSeek-R1-UD-Q2_K_XL) \| MoE all 2.5bit. `down_proj` in MoE mixture of 3.5/2.5bit \| # Finetune your own Reasoning model like R1 with Unsloth! We have a free Google Colab notebook for turning Llama 3.1 (8B) into a reasoning model: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/unsloth) [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) ## ✨ Finetune for Free All notebooks are beginner friendly! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face. \| Unsloth supports \| Free Notebooks \| Performance \| Memory use \| \|-----------------\|--------------------------------------------------------------------------------------------------------------------------\|-------------\|----------\| \| GRPO with Phi-4 (14B) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4_(14B)-GRPO.ipynb) \| 2x faster \| 80% less \| \| Llama-3.2 (3B) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb) \| 2.4x faster \| 58% less \| \| Llama-3.2 (11B vision) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb) \| 2x faster \| 60% less \| \| Qwen2 VL (7B) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_VL_(7B)-Vision.ipynb) \| 1.8x faster \| 60% less \| \| Qwen2.5 (7B) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_(7B)-Alpaca.ipynb) \| 2x faster \| 60% less \| \| Llama-3.1 (8B) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb) \| 2.4x faster \| 58% less \| \| Phi-3.5 (mini) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_3.5_Mini-Conversational.ipynb) \| 2x faster \| 50% less \| \| Gemma 2 (9B) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma2_(9B)-Alpaca.ipynb) \| 2.4x faster \| 58% less \| \| Mistral (7B) \| [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_(7B)-Conversational.ipynb) \| 2.2x faster \| 62% less \| [<img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="200"/>](https://docs.unsloth.ai) - This [Llama 3.2 conversational notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb) is useful for ShareGPT ChatML / Vicuna templates. - This [text completion notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_(7B)-Text_Completion.ipynb) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr. - \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster. ## Special Thanks A huge thank you to the DeepSeek team for creating and releasing these models. # DeepSeek-R1 <!-- markdownlint-disable first-line-h1 --> <!-- markdownlint-disable html --> <!-- markdownlint-disable no-duplicate-header --> <div align="center"> <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V3" /> </div> <hr> <div align="center" style="line-height: 1;"> <a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;"> <img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20R1-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;"> <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;"> <img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;"> <img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;"> <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="https://github.com/deepseek-ai/DeepSeek-R1/blob/main/LICENSE-CODE" style="margin: 2px;"> <img alt="Code License" src="https://img.shields.io/badge/Code_License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://github.com/deepseek-ai/DeepSeek-R1/blob/main/LICENSE-MODEL" style="margin: 2px;"> <img alt="Model License" src="https://img.shields.io/badge/Model_License-Model_Agreement-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/> </a> </div> <p align="center"> <a href="https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf"><b>Paper Link</b>👁️</a> </p> ## 1. Introduction We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrated remarkable performance on reasoning. With RL, DeepSeek-R1-Zero naturally emerged with numerous powerful and interesting reasoning behaviors. However, DeepSeek-R1-Zero encounters challenges such as endless repetition, poor readability, and language mixing. To address these issues and further enhance reasoning performance, we introduce DeepSeek-R1, which incorporates cold-start data before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks. To support the research community, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and six dense models distilled from DeepSeek-R1 based on Llama and Qwen. DeepSeek-R1-Distill-Qwen-32B outperforms OpenAI-o1-mini across various benchmarks, achieving new state-of-the-art results for dense models. NOTE: Before running DeepSeek-R1 series models locally, we kindly recommend reviewing the [Usage Recommendation](#usage-recommendations) section. <p align="center"> <img width="80%" src="figures/benchmark.jpg"> </p> ## 2. Model Summary --- Post-Training: Large-Scale Reinforcement Learning on the Base Model - We directly apply reinforcement learning (RL) to the base model without relying on supervised fine-tuning (SFT) as a preliminary step. This approach allows the model to explore chain-of-thought (CoT) for solving complex problems, resulting in the development of DeepSeek-R1-Zero. DeepSeek-R1-Zero demonstrates capabilities such as self-verification, reflection, and generating long CoTs, marking a significant milestone for the research community. Notably, it is the first open research to validate that reasoning capabilities of LLMs can be incentivized purely through RL, without the need for SFT. This breakthrough paves the way for future advancements in this area. - We introduce our pipeline to develop DeepSeek-R1. The pipeline incorporates two RL stages aimed at discovering improved reasoning patterns and aligning with human preferences, as well as two SFT stages that serve as the seed for the model's reasoning and non-reasoning capabilities. We believe the pipeline will benefit the industry by creating better models. --- Distillation: Smaller Models Can Be Powerful Too - We demonstrate that the reasoning patterns of larger models can be distilled into smaller models, resulting in better performance compared to the reasoning patterns discovered through RL on small models. The open source DeepSeek-R1, as well as its API, will benefit the research community to distill better smaller models in the future. - Using the reasoning data generated by DeepSeek-R1, we fine-tuned several dense models that are widely used in the research community. The evaluation results demonstrate that the distilled smaller dense models perform exceptionally well on benchmarks. We open-source distilled 1.5B, 7B, 8B, 14B, 32B, and 70B checkpoints based on Qwen2.5 and Llama3 series to the community. ## 3. Model Downloads ### DeepSeek-R1 Models <div align="center"> \| Model \| #Total Params \| #Activated Params \| Context Length \| Download \| \| :------------: \| :------------: \| :------------: \| :------------: \| :------------: \| \| DeepSeek-R1-Zero \| 671B \| 37B \| 128K \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Zero) \| \| DeepSeek-R1 \| 671B \| 37B \| 128K \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1) \| </div> DeepSeek-R1-Zero & DeepSeek-R1 are trained based on DeepSeek-V3-Base. For more details regarding the model architecture, please refer to [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3) repository. ### DeepSeek-R1-Distill Models <div align="center"> \| Model \| Base Model \| Download \| \| :------------: \| :------------: \| :------------: \| \| DeepSeek-R1-Distill-Qwen-1.5B \| [Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) \| \| DeepSeek-R1-Distill-Qwen-7B \| [Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) \| \| DeepSeek-R1-Distill-Llama-8B \| [Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B) \| \| DeepSeek-R1-Distill-Qwen-14B \| [Qwen2.5-14B](https://huggingface.co/Qwen/Qwen2.5-14B) \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B) \| \|DeepSeek-R1-Distill-Qwen-32B \| [Qwen2.5-32B](https://huggingface.co/Qwen/Qwen2.5-32B) \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B) \| \| DeepSeek-R1-Distill-Llama-70B \| [Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B) \| </div> DeepSeek-R1-Distill models are fine-tuned based on open-source models, using samples generated by DeepSeek-R1. We slightly change their configs and tokenizers. Please use our setting to run these models. ## 4. Evaluation Results ### DeepSeek-R1-Evaluation For all our models, the maximum generation length is set to 32,768 tokens. For benchmarks requiring sampling, we use a temperature of $0.6$, a top-p value of $0.95$, and generate 64 responses per query to estimate pass@1. <div align="center"> \| Category \| Benchmark (Metric) \| Claude-3.5-Sonnet-1022 \| GPT-4o 0513 \| DeepSeek V3 \| OpenAI o1-mini \| OpenAI o1-1217 \| DeepSeek R1 \| \|----------\|-------------------\|----------------------\|------------\|--------------\|----------------\|------------\|--------------\| \| \| Architecture \| - \| - \| MoE \| - \| - \| MoE \| \| \| # Activated Params \| - \| - \| 37B \| - \| - \| 37B \| \| \| # Total Params \| - \| - \| 671B \| - \| - \| 671B \| \| English \| MMLU (Pass@1) \| 88.3 \| 87.2 \| 88.5 \| 85.2 \| 91.8 \| 90.8 \| \| \| MMLU-Redux (EM) \| 88.9 \| 88.0 \| 89.1 \| 86.7 \| - \| 92.9 \| \| \| MMLU-Pro (EM) \| 78.0 \| 72.6 \| 75.9 \| 80.3 \| - \| 84.0 \| \| \| DROP (3-shot F1) \| 88.3 \| 83.7 \| 91.6 \| 83.9 \| 90.2 \| 92.2 \| \| \| IF-Eval (Prompt Strict) \| 86.5 \| 84.3 \| 86.1 \| 84.8 \| - \| 83.3 \| \| \| GPQA-Diamond (Pass@1) \| 65.0 \| 49.9 \| 59.1 \| 60.0 \| 75.7 \| 71.5 \| \| \| SimpleQA (Correct) \| 28.4 \| 38.2 \| 24.9 \| 7.0 \| 47.0 \| 30.1 \| \| \| FRAMES (Acc.) \| 72.5 \| 80.5 \| 73.3 \| 76.9 \| - \| 82.5 \| \| \| AlpacaEval2.0 (LC-winrate) \| 52.0 \| 51.1 \| 70.0 \| 57.8 \| - \| 87.6 \| \| \| ArenaHard (GPT-4-1106) \| 85.2 \| 80.4 \| 85.5 \| 92.0 \| - \| 92.3 \| \| Code \| LiveCodeBench (Pass@1-COT) \| 33.8 \| 34.2 \| - \| 53.8 \| 63.4 \| 65.9 \| \| \| Codeforces (Percentile) \| 20.3 \| 23.6 \| 58.7 \| 93.4 \| 96.6 \| 96.3 \| \| \| Codeforces (Rating) \| 717 \| 759 \| 1134 \| 1820 \| 2061 \| 2029 \| \| \| SWE Verified (Resolved) \| 50.8 \| 38.8 \| 42.0 \| 41.6 \| 48.9 \| 49.2 \| \| \| Aider-Polyglot (Acc.) \| 45.3 \| 16.0 \| 49.6 \| 32.9 \| 61.7 \| 53.3 \| \| Math \| AIME 2024 (Pass@1) \| 16.0 \| 9.3 \| 39.2 \| 63.6 \| 79.2 \| 79.8 \| \| \| MATH-500 (Pass@1) \| 78.3 \| 74.6 \| 90.2 \| 90.0 \| 96.4 \| 97.3 \| \| \| CNMO 2024 (Pass@1) \| 13.1 \| 10.8 \| 43.2 \| 67.6 \| - \| 78.8 \| \| Chinese \| CLUEWSC (EM) \| 85.4 \| 87.9 \| 90.9 \| 89.9 \| - \| 92.8 \| \| \| C-Eval (EM) \| 76.7 \| 76.0 \| 86.5 \| 68.9 \| - \| 91.8 \| \| \| C-SimpleQA (Correct) \| 55.4 \| 58.7 \| 68.0 \| 40.3 \| - \| 63.7 \| </div> ### Distilled Model Evaluation <div align="center"> \| Model \| AIME 2024 pass@1 \| AIME 2024 cons@64 \| MATH-500 pass@1 \| GPQA Diamond pass@1 \| LiveCodeBench pass@1 \| CodeForces rating \| \|------------------------------------------\|------------------\|-------------------\|-----------------\|----------------------\|----------------------\|-------------------\| \| GPT-4o-0513 \| 9.3 \| 13.4 \| 74.6 \| 49.9 \| 32.9 \| 759 \| \| Claude-3.5-Sonnet-1022 \| 16.0 \| 26.7 \| 78.3 \| 65.0 \| 38.9 \| 717 \| \| o1-mini \| 63.6 \| 80.0 \| 90.0 \| 60.0 \| 53.8 \| 1820 \| \| QwQ-32B-Preview \| 44.0 \| 60.0 \| 90.6 \| 54.5 \| 41.9 \| 1316 \| \| DeepSeek-R1-Distill-Qwen-1.5B \| 28.9 \| 52.7 \| 83.9 \| 33.8 \| 16.9 \| 954 \| \| DeepSeek-R1-Distill-Qwen-7B \| 55.5 \| 83.3 \| 92.8 \| 49.1 \| 37.6 \| 1189 \| \| DeepSeek-R1-Distill-Qwen-14B \| 69.7 \| 80.0 \| 93.9 \| 59.1 \| 53.1 \| 1481 \| \| DeepSeek-R1-Distill-Qwen-32B \| 72.6 \| 83.3 \| 94.3 \| 62.1 \| 57.2 \| 1691 \| \| DeepSeek-R1-Distill-Llama-8B \| 50.4 \| 80.0 \| 89.1 \| 49.0 \| 39.6 \| 1205 \| \| DeepSeek-R1-Distill-Llama-70B \| 70.0 \| 86.7 \| 94.5 \| 65.2 \| 57.5 \| 1633 \| </div> ## 5. Chat Website & API Platform You can chat with DeepSeek-R1 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com), and switch on the button "DeepThink" We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/) ## 6. How to Run Locally ### DeepSeek-R1 Models Please visit [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3) repo for more information about running DeepSeek-R1 locally. ### DeepSeek-R1-Distill Models DeepSeek-R1-Distill models can be utilized in the same manner as Qwen or Llama models. For instance, you can easily start a service using [vLLM](https://github.com/vllm-project/vllm): ```shell vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-32B --tensor-parallel-size 2 --max-model-len 32768 --enforce-eager ``` You can also easily start a service using [SGLang](https://github.com/sgl-project/sglang) ```bash python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-R1-Distill-Qwen-32B --trust-remote-code --tp 2 ``` ### Usage Recommendations We recommend adhering to the following configurations when utilizing the DeepSeek-R1 series models, including benchmarking, to achieve the expected performance: 1. Set the temperature within the range of 0.5-0.7 (0.6 is recommended) to prevent endless repetitions or incoherent outputs. 2. Avoid adding a system prompt; all instructions should be contained within the user prompt. 3. For mathematical problems, it is advisable to include a directive in your prompt such as: "Please reason step by step, and put your final answer within \boxed{}." 4. When evaluating model performance, it is recommended to conduct multiple tests and average the results. ## 7. License This code repository and the model weights are licensed under the [MIT License](https://github.com/deepseek-ai/DeepSeek-R1/blob/main/LICENSE). DeepSeek-R1 series support commercial use, allow for any modifications and derivative works, including, but not limited to, distillation for training other LLMs. Please note that: - DeepSeek-R1-Distill-Qwen-1.5B, DeepSeek-R1-Distill-Qwen-7B, DeepSeek-R1-Distill-Qwen-14B and DeepSeek-R1-Distill-Qwen-32B are derived from [Qwen-2.5 series](https://github.com/QwenLM/Qwen2.5), which are originally licensed under [Apache 2.0 License](https://huggingface.co/Qwen/Qwen2.5-1.5B/blob/main/LICENSE), and now finetuned with 800k samples curated with DeepSeek-R1. - DeepSeek-R1-Distill-Llama-8B is derived from Llama3.1-8B-Base and is originally licensed under [llama3.1 license](https://huggingface.co/meta-llama/Llama-3.1-8B/blob/main/LICENSE). - DeepSeek-R1-Distill-Llama-70B is derived from Llama3.3-70B-Instruct and is originally licensed under [llama3.3 license](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct/blob/main/LICENSE). ## 8. Citation ``` @misc{deepseekai2025deepseekr1incentivizingreasoningcapability, title={DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning}, author={DeepSeek-AI and Daya Guo and Dejian Yang and Haowei Zhang and Junxiao Song and Ruoyu Zhang and Runxin Xu and Qihao Zhu and Shirong Ma and Peiyi Wang and Xiao Bi and Xiaokang Zhang and Xingkai Yu and Yu Wu and Z. F. Wu and Zhibin Gou and Zhihong Shao and Zhuoshu Li and Ziyi Gao and Aixin Liu and Bing Xue and Bingxuan Wang and Bochao Wu and Bei Feng and Chengda Lu and Chenggang Zhao and Chengqi Deng and Chenyu Zhang and Chong Ruan and Damai Dai and Deli Chen and Dongjie Ji and Erhang Li and Fangyun Lin and Fucong Dai and Fuli Luo and Guangbo Hao and Guanting Chen and Guowei Li and H. Zhang and Han Bao and Hanwei Xu and Haocheng Wang and Honghui Ding and Huajian Xin and Huazuo Gao and Hui Qu and Hui Li and Jianzhong Guo and Jiashi Li and Jiawei Wang and Jingchang Chen and Jingyang Yuan and Junjie Qiu and Junlong Li and J. L. Cai and Jiaqi Ni and Jian Liang and Jin Chen and Kai Dong and Kai Hu and Kaige Gao and Kang Guan and Kexin Huang and Kuai Yu and Lean Wang and Lecong Zhang and Liang Zhao and Litong Wang and Liyue Zhang and Lei Xu and Leyi Xia and Mingchuan Zhang and Minghua Zhang and Minghui Tang and Meng Li and Miaojun Wang and Mingming Li and Ning Tian and Panpan Huang and Peng Zhang and Qiancheng Wang and Qinyu Chen and Qiushi Du and Ruiqi Ge and Ruisong Zhang and Ruizhe Pan and Runji Wang and R. J. Chen and R. L. Jin and Ruyi Chen and Shanghao Lu and Shangyan Zhou and Shanhuang Chen and Shengfeng Ye and Shiyu Wang and Shuiping Yu and Shunfeng Zhou and Shuting Pan and S. S. Li and Shuang Zhou and Shaoqing Wu and Shengfeng Ye and Tao Yun and Tian Pei and Tianyu Sun and T. Wang and Wangding Zeng and Wanjia Zhao and Wen Liu and Wenfeng Liang and Wenjun Gao and Wenqin Yu and Wentao Zhang and W. L. Xiao and Wei An and Xiaodong Liu and Xiaohan Wang and Xiaokang Chen and Xiaotao Nie and Xin Cheng and Xin Liu and Xin Xie and Xingchao Liu and Xinyu Yang and Xinyuan Li and Xuecheng Su and Xuheng Lin and X. Q. Li and Xiangyue Jin and Xiaojin Shen and Xiaosha Chen and Xiaowen Sun and Xiaoxiang Wang and Xinnan Song and Xinyi Zhou and Xianzu Wang and Xinxia Shan and Y. K. Li and Y. Q. Wang and Y. X. Wei and Yang Zhang and Yanhong Xu and Yao Li and Yao Zhao and Yaofeng Sun and Yaohui Wang and Yi Yu and Yichao Zhang and Yifan Shi and Yiliang Xiong and Ying He and Yishi Piao and Yisong Wang and Yixuan Tan and Yiyang Ma and Yiyuan Liu and Yongqiang Guo and Yuan Ou and Yuduan Wang and Yue Gong and Yuheng Zou and Yujia He and Yunfan Xiong and Yuxiang Luo and Yuxiang You and Yuxuan Liu and Yuyang Zhou and Y. X. Zhu and Yanhong Xu and Yanping Huang and Yaohui Li and Yi Zheng and Yuchen Zhu and Yunxian Ma and Ying Tang and Yukun Zha and Yuting Yan and Z. Z. Ren and Zehui Ren and Zhangli Sha and Zhe Fu and Zhean Xu and Zhenda Xie and Zhengyan Zhang and Zhewen Hao and Zhicheng Ma and Zhigang Yan and Zhiyu Wu and Zihui Gu and Zijia Zhu and Zijun Liu and Zilin Li and Ziwei Xie and Ziyang Song and Zizheng Pan and Zhen Huang and Zhipeng Xu and Zhongyu Zhang and Zhen Zhang}, year={2025}, eprint={2501.12948}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2501.12948}, } ``` ## 9. Contact If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]).
YuchenLi01/genParaMoreUniqueResNoGT_Qwen2.5-1.5BInstruct_dpo_ebs32_lr3e-06_beta0.1_42	YuchenLi01	2025-05-29T07:11:19Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "conversational", "dataset:YuchenLi01/MATH_Qwen2.5-1.5BInstruct_DPO_generatedAndParaphrasedMoreUniqueResponseNoGT", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T03:09:11Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-1.5B-Instruct tags: - alignment-handbook - trl - dpo - generated_from_trainer - trl - dpo - generated_from_trainer datasets: - YuchenLi01/MATH_Qwen2.5-1.5BInstruct_DPO_generatedAndParaphrasedMoreUniqueResponseNoGT model-index: - name: genParaMoreUniqueResNoGT_Qwen2.5-1.5BInstruct_dpo_ebs32_lr3e-06_beta0.1_42 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. --> # genParaMoreUniqueResNoGT_Qwen2.5-1.5BInstruct_dpo_ebs32_lr3e-06_beta0.1_42 This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the YuchenLi01/MATH_Qwen2.5-1.5BInstruct_DPO_generatedAndParaphrasedMoreUniqueResponseNoGT dataset. It achieves the following results on the evaluation set: - Loss: 0.7986 - Rewards/chosen: -8.2096 - Rewards/rejected: -11.0162 - Rewards/accuracies: 0.7195 - Rewards/margins: 2.8065 - Logps/rejected: -157.9193 - Logps/chosen: -124.5607 - Logits/rejected: -2.6784 - Logits/chosen: -2.8351 ## 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: 3e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 32 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6932 \| 0.0135 \| 20 \| 0.6935 \| 0.0027 \| 0.0006 \| 0.5152 \| 0.0021 \| -47.7519 \| -42.4376 \| -2.2058 \| -2.3115 \| \| 0.698 \| 0.0270 \| 40 \| 0.6918 \| -0.0110 \| -0.0156 \| 0.5305 \| 0.0046 \| -47.9133 \| -42.5743 \| -2.1930 \| -2.2989 \| \| 0.6833 \| 0.0405 \| 60 \| 0.6886 \| -0.0591 \| -0.0721 \| 0.6067 \| 0.0130 \| -48.4787 \| -43.0550 \| -2.1562 \| -2.2633 \| \| 0.6758 \| 0.0540 \| 80 \| 0.6822 \| -0.1525 \| -0.1733 \| 0.5915 \| 0.0209 \| -49.4910 \| -43.9891 \| -2.0868 \| -2.1942 \| \| 0.6368 \| 0.0675 \| 100 \| 0.6718 \| -0.3816 \| -0.4269 \| 0.5732 \| 0.0453 \| -52.0264 \| -46.2800 \| -1.9451 \| -2.0545 \| \| 0.6107 \| 0.0810 \| 120 \| 0.6598 \| -0.5907 \| -0.6752 \| 0.5793 \| 0.0845 \| -54.5097 \| -48.3717 \| -1.8466 \| -1.9569 \| \| 0.6334 \| 0.0945 \| 140 \| 0.6440 \| -0.7884 \| -0.9276 \| 0.6220 \| 0.1392 \| -57.0334 \| -50.3479 \| -1.7817 \| -1.8943 \| \| 0.6165 \| 0.1080 \| 160 \| 0.6256 \| -0.8388 \| -1.0405 \| 0.6585 \| 0.2017 \| -58.1625 \| -50.8525 \| -1.7930 \| -1.9070 \| \| 0.5796 \| 0.1215 \| 180 \| 0.6061 \| -1.1541 \| -1.4590 \| 0.6738 \| 0.3049 \| -62.3480 \| -54.0052 \| -1.7217 \| -1.8413 \| \| 0.5451 \| 0.1350 \| 200 \| 0.5878 \| -1.5160 \| -1.9779 \| 0.7165 \| 0.4620 \| -67.5371 \| -57.6239 \| -1.6771 \| -1.8018 \| \| 0.5041 \| 0.1484 \| 220 \| 0.5730 \| -1.6805 \| -2.2536 \| 0.7256 \| 0.5731 \| -70.2937 \| -59.2698 \| -1.7600 \| -1.8889 \| \| 0.5838 \| 0.1619 \| 240 \| 0.5630 \| -2.0952 \| -2.8696 \| 0.7104 \| 0.7744 \| -76.4537 \| -63.4161 \| -1.8759 \| -2.0141 \| \| 0.4511 \| 0.1754 \| 260 \| 0.5494 \| -2.0629 \| -2.9299 \| 0.7134 \| 0.8670 \| -77.0566 \| -63.0929 \| -1.9798 \| -2.1200 \| \| 0.6892 \| 0.1889 \| 280 \| 0.5378 \| -1.7049 \| -2.4428 \| 0.7256 \| 0.7379 \| -72.1853 \| -59.5132 \| -1.9995 \| -2.1393 \| \| 0.5347 \| 0.2024 \| 300 \| 0.5369 \| -2.1250 \| -2.9276 \| 0.7134 \| 0.8025 \| -77.0332 \| -63.7145 \| -2.0005 \| -2.1456 \| \| 0.2873 \| 0.2159 \| 320 \| 0.5337 \| -2.8430 \| -3.8354 \| 0.7226 \| 0.9924 \| -86.1114 \| -70.8942 \| -1.8420 \| -1.9899 \| \| 0.6177 \| 0.2294 \| 340 \| 0.5416 \| -3.0456 \| -3.9991 \| 0.7195 \| 0.9534 \| -87.7482 \| -72.9206 \| -1.6151 \| -1.7616 \| \| 0.4691 \| 0.2429 \| 360 \| 0.5487 \| -2.5956 \| -3.5875 \| 0.7165 \| 0.9920 \| -83.6329 \| -68.4202 \| -1.9755 \| -2.1279 \| \| 0.382 \| 0.2564 \| 380 \| 0.5561 \| -2.8632 \| -3.9593 \| 0.7104 \| 1.0961 \| -87.3508 \| -71.0966 \| -2.1512 \| -2.3018 \| \| 0.4635 \| 0.2699 \| 400 \| 0.5470 \| -3.2646 \| -4.5135 \| 0.7073 \| 1.2489 \| -92.8929 \| -75.1105 \| -2.0813 \| -2.2349 \| \| 0.3273 \| 0.2834 \| 420 \| 0.5338 \| -3.0949 \| -4.2684 \| 0.7043 \| 1.1735 \| -90.4412 \| -73.4134 \| -2.0155 \| -2.1653 \| \| 0.5248 \| 0.2969 \| 440 \| 0.5482 \| -3.2610 \| -4.4834 \| 0.6951 \| 1.2224 \| -92.5914 \| -75.0738 \| -1.9881 \| -2.1360 \| \| 0.5027 \| 0.3104 \| 460 \| 0.5500 \| -3.5928 \| -4.7529 \| 0.7043 \| 1.1602 \| -95.2866 \| -78.3918 \| -1.7034 \| -1.8456 \| \| 0.3677 \| 0.3239 \| 480 \| 0.5322 \| -3.1634 \| -4.3019 \| 0.7165 \| 1.1386 \| -90.7771 \| -74.0980 \| -1.8318 \| -1.9790 \| \| 0.4315 \| 0.3374 \| 500 \| 0.5369 \| -3.3935 \| -4.5434 \| 0.6982 \| 1.1499 \| -93.1914 \| -76.3990 \| -1.9383 \| -2.0830 \| \| 0.4743 \| 0.3509 \| 520 \| 0.5517 \| -3.8368 \| -5.1456 \| 0.7012 \| 1.3088 \| -99.2137 \| -80.8321 \| -1.9747 \| -2.1211 \| \| 0.4281 \| 0.3644 \| 540 \| 0.5354 \| -3.5222 \| -4.7582 \| 0.7043 \| 1.2360 \| -95.3400 \| -77.6866 \| -1.9706 \| -2.1128 \| \| 0.3904 \| 0.3779 \| 560 \| 0.5341 \| -2.9636 \| -4.1276 \| 0.6951 \| 1.1640 \| -89.0335 \| -72.1004 \| -2.1134 \| -2.2582 \| \| 0.4332 \| 0.3914 \| 580 \| 0.5486 \| -3.3775 \| -4.5991 \| 0.6890 \| 1.2216 \| -93.7486 \| -76.2393 \| -2.0895 \| -2.2364 \| \| 0.339 \| 0.4049 \| 600 \| 0.5638 \| -4.1546 \| -5.4255 \| 0.6982 \| 1.2709 \| -102.0127 \| -84.0106 \| -2.0277 \| -2.1744 \| \| 0.4905 \| 0.4184 \| 620 \| 0.5524 \| -3.9891 \| -5.3172 \| 0.7073 \| 1.3281 \| -100.9295 \| -82.3554 \| -1.9876 \| -2.1320 \| \| 0.4174 \| 0.4318 \| 640 \| 0.5702 \| -4.1295 \| -5.4509 \| 0.6951 \| 1.3214 \| -102.2665 \| -83.7597 \| -2.0315 \| -2.1766 \| \| 0.5769 \| 0.4453 \| 660 \| 0.5488 \| -3.7582 \| -5.0878 \| 0.7134 \| 1.3296 \| -98.6353 \| -80.0459 \| -2.0555 \| -2.2028 \| \| 0.3792 \| 0.4588 \| 680 \| 0.5732 \| -4.1756 \| -5.5629 \| 0.6738 \| 1.3873 \| -103.3868 \| -84.2208 \| -2.0918 \| -2.2417 \| \| 0.4978 \| 0.4723 \| 700 \| 0.5910 \| -5.0006 \| -6.5765 \| 0.7226 \| 1.5759 \| -113.5224 \| -92.4701 \| -2.0050 \| -2.1537 \| \| 0.3949 \| 0.4858 \| 720 \| 0.5626 \| -4.1391 \| -5.4077 \| 0.6951 \| 1.2685 \| -101.8343 \| -83.8556 \| -1.8587 \| -1.9992 \| \| 0.3405 \| 0.4993 \| 740 \| 0.5629 \| -4.6657 \| -5.9561 \| 0.6799 \| 1.2903 \| -107.3183 \| -89.1215 \| -1.7706 \| -1.9063 \| \| 0.3281 \| 0.5128 \| 760 \| 0.5531 \| -4.8452 \| -6.1634 \| 0.7073 \| 1.3182 \| -109.3915 \| -90.9158 \| -1.7602 \| -1.8955 \| \| 0.2658 \| 0.5263 \| 780 \| 0.5405 \| -4.3611 \| -5.5910 \| 0.6921 \| 1.2298 \| -103.6672 \| -86.0758 \| -1.8014 \| -1.9397 \| \| 0.286 \| 0.5398 \| 800 \| 0.5640 \| -4.8028 \| -6.0722 \| 0.6738 \| 1.2694 \| -108.4794 \| -90.4926 \| -1.7000 \| -1.8362 \| \| 0.2935 \| 0.5533 \| 820 \| 0.5643 \| -4.7649 \| -6.1747 \| 0.6982 \| 1.4098 \| -109.5050 \| -90.1134 \| -1.8640 \| -2.0113 \| \| 0.7291 \| 0.5668 \| 840 \| 0.5847 \| -4.6228 \| -6.0845 \| 0.6738 \| 1.4617 \| -108.6021 \| -88.6922 \| -1.9778 \| -2.1290 \| \| 0.2744 \| 0.5803 \| 860 \| 0.5719 \| -4.5591 \| -5.9668 \| 0.6921 \| 1.4077 \| -107.4256 \| -88.0552 \| -1.8563 \| -1.9951 \| \| 0.2714 \| 0.5938 \| 880 \| 0.5570 \| -4.7344 \| -6.2742 \| 0.6982 \| 1.5397 \| -110.4993 \| -89.8087 \| -1.8844 \| -2.0237 \| \| 0.2154 \| 0.6073 \| 900 \| 0.5679 \| -4.4651 \| -5.9958 \| 0.6951 \| 1.5307 \| -107.7154 \| -87.1152 \| -2.0424 \| -2.1859 \| \| 0.2149 \| 0.6208 \| 920 \| 0.5619 \| -4.8360 \| -6.4334 \| 0.6890 \| 1.5975 \| -112.0920 \| -90.8241 \| -1.9294 \| -2.0708 \| \| 0.2674 \| 0.6343 \| 940 \| 0.5875 \| -4.9711 \| -6.6360 \| 0.6951 \| 1.6650 \| -114.1180 \| -92.1752 \| -1.9314 \| -2.0687 \| \| 0.4282 \| 0.6478 \| 960 \| 0.5766 \| -4.9849 \| -6.5652 \| 0.7165 \| 1.5802 \| -113.4094 \| -92.3137 \| -1.8290 \| -1.9571 \| \| 0.3954 \| 0.6613 \| 980 \| 0.5739 \| -4.7550 \| -6.3625 \| 0.7134 \| 1.6075 \| -111.3823 \| -90.0139 \| -1.9485 \| -2.0847 \| \| 0.2234 \| 0.6748 \| 1000 \| 0.5836 \| -4.9011 \| -6.4827 \| 0.6890 \| 1.5816 \| -112.5841 \| -91.4753 \| -1.9944 \| -2.1356 \| \| 0.3507 \| 0.6883 \| 1020 \| 0.5981 \| -5.1430 \| -6.6647 \| 0.6982 \| 1.5217 \| -114.4045 \| -93.8940 \| -1.9444 \| -2.0856 \| \| 0.1996 \| 0.7018 \| 1040 \| 0.6022 \| -5.4486 \| -6.9077 \| 0.6890 \| 1.4591 \| -116.8345 \| -96.9507 \| -1.8633 \| -1.9987 \| \| 0.3366 \| 0.7152 \| 1060 \| 0.5930 \| -5.4007 \| -6.8636 \| 0.6951 \| 1.4629 \| -116.3932 \| -96.4710 \| -1.8210 \| -1.9525 \| \| 0.2799 \| 0.7287 \| 1080 \| 0.5939 \| -5.1248 \| -6.6455 \| 0.6921 \| 1.5207 \| -114.2127 \| -93.7127 \| -1.9237 \| -2.0631 \| \| 0.1774 \| 0.7422 \| 1100 \| 0.5887 \| -5.2942 \| -6.9182 \| 0.6707 \| 1.6240 \| -116.9399 \| -95.4067 \| -1.9503 \| -2.0926 \| \| 0.1881 \| 0.7557 \| 1120 \| 0.5847 \| -5.3894 \| -7.0881 \| 0.7073 \| 1.6987 \| -118.6391 \| -96.3587 \| -1.9158 \| -2.0561 \| \| 0.1468 \| 0.7692 \| 1140 \| 0.6024 \| -5.1630 \| -6.8624 \| 0.7195 \| 1.6993 \| -116.3812 \| -94.0945 \| -2.0780 \| -2.2264 \| \| 0.2044 \| 0.7827 \| 1160 \| 0.5923 \| -5.2588 \| -6.8410 \| 0.7287 \| 1.5821 \| -116.1671 \| -95.0524 \| -1.8724 \| -2.0068 \| \| 0.1729 \| 0.7962 \| 1180 \| 0.6154 \| -5.3917 \| -6.9531 \| 0.7165 \| 1.5614 \| -117.2889 \| -96.3815 \| -1.7913 \| -1.9233 \| \| 0.4346 \| 0.8097 \| 1200 \| 0.6447 \| -5.5422 \| -7.2877 \| 0.6982 \| 1.7455 \| -120.6349 \| -97.8868 \| -1.9300 \| -2.0718 \| \| 0.3607 \| 0.8232 \| 1220 \| 0.6419 \| -5.3649 \| -7.0586 \| 0.7073 \| 1.6937 \| -118.3431 \| -96.1130 \| -2.0518 \| -2.1984 \| \| 0.1503 \| 0.8367 \| 1240 \| 0.6159 \| -5.3762 \| -6.9164 \| 0.7317 \| 1.5402 \| -116.9220 \| -96.2265 \| -1.8972 \| -2.0351 \| \| 0.2573 \| 0.8502 \| 1260 \| 0.6124 \| -5.4776 \| -7.0708 \| 0.7134 \| 1.5932 \| -118.4654 \| -97.2404 \| -1.8292 \| -1.9629 \| \| 0.1462 \| 0.8637 \| 1280 \| 0.6120 \| -5.2697 \| -6.7857 \| 0.7012 \| 1.5160 \| -115.6143 \| -95.1613 \| -1.8302 \| -1.9616 \| \| 0.1257 \| 0.8772 \| 1300 \| 0.6244 \| -5.5866 \| -7.1501 \| 0.6921 \| 1.5635 \| -119.2590 \| -98.3303 \| -1.7792 \| -1.9090 \| \| 0.3012 \| 0.8907 \| 1320 \| 0.6088 \| -5.3524 \| -6.9913 \| 0.7012 \| 1.6389 \| -117.6706 \| -95.9882 \| -1.9143 \| -2.0514 \| \| 0.3761 \| 0.9042 \| 1340 \| 0.5789 \| -4.9541 \| -6.5670 \| 0.6982 \| 1.6129 \| -113.4279 \| -92.0057 \| -1.9220 \| -2.0595 \| \| 0.1778 \| 0.9177 \| 1360 \| 0.5806 \| -4.7837 \| -6.3508 \| 0.6890 \| 1.5671 \| -111.2653 \| -90.3013 \| -1.9173 \| -2.0517 \| \| 0.1571 \| 0.9312 \| 1380 \| 0.6048 \| -5.2190 \| -6.8490 \| 0.7195 \| 1.6300 \| -116.2471 \| -94.6541 \| -1.8461 \| -1.9781 \| \| 0.2113 \| 0.9447 \| 1400 \| 0.6298 \| -5.3607 \| -7.1482 \| 0.7165 \| 1.7875 \| -119.2393 \| -96.0714 \| -1.9991 \| -2.1436 \| \| 0.4745 \| 0.9582 \| 1420 \| 0.6332 \| -5.4461 \| -7.3324 \| 0.7012 \| 1.8863 \| -121.0819 \| -96.9253 \| -2.0252 \| -2.1764 \| \| 0.3124 \| 0.9717 \| 1440 \| 0.6478 \| -5.6656 \| -7.5328 \| 0.6890 \| 1.8673 \| -123.0858 \| -99.1199 \| -2.0044 \| -2.1568 \| \| 0.272 \| 0.9852 \| 1460 \| 0.6206 \| -5.2093 \| -6.8896 \| 0.7104 \| 1.6803 \| -116.6534 \| -94.5576 \| -1.9764 \| -2.1244 \| \| 0.2066 \| 0.9987 \| 1480 \| 0.6104 \| -5.2178 \| -6.8566 \| 0.7134 \| 1.6388 \| -116.3233 \| -94.6423 \| -1.9168 \| -2.0597 \| \| 0.0392 \| 1.0121 \| 1500 \| 0.6585 \| -5.8883 \| -7.8940 \| 0.7195 \| 2.0057 \| -126.6976 \| -101.3469 \| -2.3078 \| -2.4746 \| \| 0.0164 \| 1.0256 \| 1520 \| 0.7829 \| -7.4564 \| -9.9377 \| 0.7134 \| 2.4813 \| -147.1341 \| -117.0279 \| -2.7262 \| -2.8967 \| \| 0.1045 \| 1.0391 \| 1540 \| 0.8157 \| -7.8191 \| -10.3298 \| 0.7012 \| 2.5107 \| -151.0553 \| -120.6553 \| -2.7029 \| -2.8767 \| \| 0.0188 \| 1.0526 \| 1560 \| 0.7966 \| -7.5779 \| -10.0465 \| 0.6921 \| 2.4686 \| -148.2228 \| -118.2432 \| -2.7555 \| -2.9259 \| \| 0.0793 \| 1.0661 \| 1580 \| 0.7513 \| -6.9530 \| -9.3107 \| 0.7134 \| 2.3576 \| -140.8642 \| -111.9945 \| -2.5893 \| -2.7629 \| \| 0.0119 \| 1.0796 \| 1600 \| 0.7854 \| -7.3190 \| -9.7118 \| 0.7012 \| 2.3928 \| -144.8755 \| -115.6543 \| -2.6172 \| -2.7918 \| \| 0.1849 \| 1.0931 \| 1620 \| 0.7972 \| -7.4716 \| -9.8057 \| 0.7073 \| 2.3341 \| -145.8146 \| -117.1800 \| -2.5619 \| -2.7385 \| \| 0.1462 \| 1.1066 \| 1640 \| 0.7940 \| -7.5418 \| -9.9524 \| 0.6951 \| 2.4106 \| -147.2812 \| -117.8819 \| -2.6300 \| -2.8039 \| \| 0.0235 \| 1.1201 \| 1660 \| 0.8168 \| -7.8351 \| -10.2565 \| 0.6860 \| 2.4215 \| -150.3227 \| -120.8149 \| -2.6886 \| -2.8616 \| \| 0.0226 \| 1.1336 \| 1680 \| 0.8101 \| -7.9739 \| -10.4037 \| 0.6799 \| 2.4298 \| -151.7950 \| -122.2037 \| -2.5396 \| -2.7167 \| \| 0.1962 \| 1.1471 \| 1700 \| 0.8017 \| -7.8880 \| -10.2642 \| 0.6860 \| 2.3761 \| -150.3994 \| -121.3448 \| -2.4734 \| -2.6511 \| \| 0.0537 \| 1.1606 \| 1720 \| 0.8081 \| -7.9880 \| -10.3473 \| 0.6982 \| 2.3593 \| -151.2302 \| -122.3445 \| -2.4584 \| -2.6356 \| \| 0.0724 \| 1.1741 \| 1740 \| 0.7932 \| -7.9194 \| -10.2998 \| 0.7043 \| 2.3804 \| -150.7551 \| -121.6580 \| -2.4803 \| -2.6572 \| \| 0.0515 \| 1.1876 \| 1760 \| 0.7982 \| -8.1981 \| -10.7219 \| 0.6982 \| 2.5238 \| -154.9765 \| -124.4456 \| -2.5665 \| -2.7423 \| \| 0.0356 \| 1.2011 \| 1780 \| 0.7704 \| -7.6698 \| -10.1103 \| 0.7073 \| 2.4405 \| -148.8607 \| -119.1623 \| -2.5855 \| -2.7586 \| \| 0.0612 \| 1.2146 \| 1800 \| 0.7604 \| -7.6082 \| -9.9330 \| 0.6951 \| 2.3248 \| -147.0874 \| -118.5465 \| -2.4894 \| -2.6612 \| \| 0.0638 \| 1.2281 \| 1820 \| 0.7867 \| -7.9216 \| -10.3041 \| 0.7012 \| 2.3824 \| -150.7981 \| -121.6805 \| -2.5533 \| -2.7232 \| \| 0.0391 \| 1.2416 \| 1840 \| 0.7707 \| -7.8846 \| -10.2797 \| 0.7012 \| 2.3951 \| -150.5542 \| -121.3100 \| -2.4966 \| -2.6666 \| \| 0.0417 \| 1.2551 \| 1860 \| 0.7726 \| -7.7731 \| -10.1891 \| 0.6890 \| 2.4160 \| -149.6489 \| -120.1955 \| -2.4996 \| -2.6686 \| \| 0.1465 \| 1.2686 \| 1880 \| 0.7845 \| -7.7861 \| -10.1994 \| 0.7012 \| 2.4133 \| -149.7519 \| -120.3252 \| -2.5368 \| -2.7040 \| \| 0.1198 \| 1.2821 \| 1900 \| 0.7623 \| -7.5229 \| -9.8738 \| 0.7073 \| 2.3509 \| -146.4954 \| -117.6936 \| -2.5132 \| -2.6806 \| \| 0.0351 \| 1.2955 \| 1920 \| 0.7589 \| -7.4409 \| -9.8266 \| 0.7043 \| 2.3857 \| -146.0238 \| -116.8731 \| -2.5724 \| -2.7384 \| \| 0.0328 \| 1.3090 \| 1940 \| 0.7676 \| -7.6615 \| -10.1270 \| 0.7165 \| 2.4655 \| -149.0274 \| -119.0791 \| -2.5571 \| -2.7246 \| \| 0.0637 \| 1.3225 \| 1960 \| 0.7694 \| -7.7646 \| -10.2770 \| 0.7043 \| 2.5124 \| -150.5276 \| -120.1108 \| -2.5798 \| -2.7484 \| \| 0.0107 \| 1.3360 \| 1980 \| 0.7757 \| -8.0093 \| -10.5474 \| 0.6982 \| 2.5381 \| -153.2317 \| -122.5578 \| -2.6287 \| -2.7971 \| \| 0.067 \| 1.3495 \| 2000 \| 0.7716 \| -8.0863 \| -10.6369 \| 0.7073 \| 2.5506 \| -154.1264 \| -123.3275 \| -2.6235 \| -2.7918 \| \| 0.1061 \| 1.3630 \| 2020 \| 0.7865 \| -8.2293 \| -10.8332 \| 0.7043 \| 2.6040 \| -156.0900 \| -124.7568 \| -2.7009 \| -2.8653 \| \| 0.0289 \| 1.3765 \| 2040 \| 0.7810 \| -8.1502 \| -10.7224 \| 0.7043 \| 2.5722 \| -154.9812 \| -123.9664 \| -2.6720 \| -2.8378 \| \| 0.0972 \| 1.3900 \| 2060 \| 0.7730 \| -8.0664 \| -10.6238 \| 0.7012 \| 2.5574 \| -153.9956 \| -123.1285 \| -2.6196 \| -2.7858 \| \| 0.0666 \| 1.4035 \| 2080 \| 0.7773 \| -8.1421 \| -10.7574 \| 0.7012 \| 2.6153 \| -155.3316 \| -123.8856 \| -2.5964 \| -2.7629 \| \| 0.0532 \| 1.4170 \| 2100 \| 0.7766 \| -7.8903 \| -10.5074 \| 0.7073 \| 2.6172 \| -152.8320 \| -121.3672 \| -2.6581 \| -2.8217 \| \| 0.0314 \| 1.4305 \| 2120 \| 0.7982 \| -8.0955 \| -10.7485 \| 0.7043 \| 2.6530 \| -155.2428 \| -123.4193 \| -2.7387 \| -2.8996 \| \| 0.0183 \| 1.4440 \| 2140 \| 0.8264 \| -8.4855 \| -11.2429 \| 0.7012 \| 2.7574 \| -160.1870 \| -127.3194 \| -2.7942 \| -2.9535 \| \| 0.0322 \| 1.4575 \| 2160 \| 0.8323 \| -8.5794 \| -11.4000 \| 0.7073 \| 2.8206 \| -161.7575 \| -128.2585 \| -2.7848 \| -2.9441 \| \| 0.041 \| 1.4710 \| 2180 \| 0.8331 \| -8.4639 \| -11.2632 \| 0.7134 \| 2.7993 \| -160.3898 \| -127.1032 \| -2.7520 \| -2.9120 \| \| 0.0579 \| 1.4845 \| 2200 \| 0.8314 \| -8.4460 \| -11.2630 \| 0.7073 \| 2.8171 \| -160.3879 \| -126.9241 \| -2.7355 \| -2.8945 \| \| 0.0447 \| 1.4980 \| 2220 \| 0.8475 \| -8.7251 \| -11.6037 \| 0.6951 \| 2.8786 \| -163.7943 \| -129.7154 \| -2.7573 \| -2.9151 \| \| 0.0334 \| 1.5115 \| 2240 \| 0.8456 \| -8.5647 \| -11.4249 \| 0.7104 \| 2.8602 \| -162.0067 \| -128.1111 \| -2.7562 \| -2.9123 \| \| 0.0684 \| 1.5250 \| 2260 \| 0.8370 \| -8.4659 \| -11.2868 \| 0.7104 \| 2.8209 \| -160.6254 \| -127.1236 \| -2.7180 \| -2.8741 \| \| 0.0371 \| 1.5385 \| 2280 \| 0.8130 \| -8.1195 \| -10.8430 \| 0.7226 \| 2.7235 \| -156.1876 \| -123.6598 \| -2.6468 \| -2.8058 \| \| 0.0464 \| 1.5520 \| 2300 \| 0.7959 \| -7.8880 \| -10.5419 \| 0.7256 \| 2.6539 \| -153.1767 \| -121.3445 \| -2.6318 \| -2.7917 \| \| 0.132 \| 1.5655 \| 2320 \| 0.8046 \| -8.0007 \| -10.6859 \| 0.7134 \| 2.6852 \| -154.6165 \| -122.4710 \| -2.6650 \| -2.8229 \| \| 0.1123 \| 1.5789 \| 2340 \| 0.7999 \| -7.9722 \| -10.6545 \| 0.7073 \| 2.6823 \| -154.3025 \| -122.1862 \| -2.6540 \| -2.8118 \| \| 0.0315 \| 1.5924 \| 2360 \| 0.7981 \| -8.0189 \| -10.7433 \| 0.7195 \| 2.7244 \| -155.1905 \| -122.6537 \| -2.6711 \| -2.8278 \| \| 0.0431 \| 1.6059 \| 2380 \| 0.7971 \| -7.9883 \| -10.6785 \| 0.7195 \| 2.6902 \| -154.5428 \| -122.3476 \| -2.6835 \| -2.8397 \| \| 0.0141 \| 1.6194 \| 2400 \| 0.8066 \| -8.1197 \| -10.8496 \| 0.7104 \| 2.7299 \| -156.2533 \| -123.6613 \| -2.7069 \| -2.8631 \| \| 0.0131 \| 1.6329 \| 2420 \| 0.8118 \| -8.2356 \| -11.0175 \| 0.7104 \| 2.7819 \| -157.9327 \| -124.8201 \| -2.7135 \| -2.8689 \| \| 0.021 \| 1.6464 \| 2440 \| 0.8086 \| -8.2194 \| -11.0084 \| 0.7134 \| 2.7890 \| -157.8416 \| -124.6581 \| -2.7025 \| -2.8590 \| \| 0.0815 \| 1.6599 \| 2460 \| 0.8084 \| -8.2212 \| -11.0179 \| 0.7165 \| 2.7967 \| -157.9362 \| -124.6758 \| -2.7084 \| -2.8639 \| \| 0.228 \| 1.6734 \| 2480 \| 0.8069 \| -8.2791 \| -11.0812 \| 0.7195 \| 2.8022 \| -158.5699 \| -125.2550 \| -2.7143 \| -2.8686 \| \| 0.2091 \| 1.6869 \| 2500 \| 0.8084 \| -8.2704 \| -11.0684 \| 0.7104 \| 2.7980 \| -158.4418 \| -125.1683 \| -2.7103 \| -2.8651 \| \| 0.0353 \| 1.7004 \| 2520 \| 0.7992 \| -8.1737 \| -10.9369 \| 0.7165 \| 2.7632 \| -157.1261 \| -124.2010 \| -2.6881 \| -2.8445 \| \| 0.0365 \| 1.7139 \| 2540 \| 0.7955 \| -8.1190 \| -10.8625 \| 0.7165 \| 2.7436 \| -156.3828 \| -123.6540 \| -2.6882 \| -2.8448 \| \| 0.0223 \| 1.7274 \| 2560 \| 0.7969 \| -8.1139 \| -10.8692 \| 0.7165 \| 2.7553 \| -156.4496 \| -123.6037 \| -2.6943 \| -2.8496 \| \| 0.0366 \| 1.7409 \| 2580 \| 0.7937 \| -8.0780 \| -10.8367 \| 0.7134 \| 2.7587 \| -156.1250 \| -123.2446 \| -2.7010 \| -2.8571 \| \| 0.1751 \| 1.7544 \| 2600 \| 0.7945 \| -8.0932 \| -10.8383 \| 0.7165 \| 2.7451 \| -156.1403 \| -123.3965 \| -2.6903 \| -2.8463 \| \| 0.2034 \| 1.7679 \| 2620 \| 0.7994 \| -8.1271 \| -10.8788 \| 0.7134 \| 2.7517 \| -156.5453 \| -123.7353 \| -2.6980 \| -2.8538 \| \| 0.0266 \| 1.7814 \| 2640 \| 0.7958 \| -8.1270 \| -10.9001 \| 0.7134 \| 2.7732 \| -156.7589 \| -123.7339 \| -2.6955 \| -2.8512 \| \| 0.0061 \| 1.7949 \| 2660 \| 0.7986 \| -8.1622 \| -10.9440 \| 0.7134 \| 2.7818 \| -157.1977 \| -124.0862 \| -2.6964 \| -2.8520 \| \| 0.0324 \| 1.8084 \| 2680 \| 0.7979 \| -8.1705 \| -10.9516 \| 0.7073 \| 2.7811 \| -157.2732 \| -124.1690 \| -2.6914 \| -2.8481 \| \| 0.0142 \| 1.8219 \| 2700 \| 0.7967 \| -8.1672 \| -10.9753 \| 0.7195 \| 2.8081 \| -157.5110 \| -124.1363 \| -2.6887 \| -2.8451 \| \| 0.0671 \| 1.8354 \| 2720 \| 0.7972 \| -8.1734 \| -10.9579 \| 0.7165 \| 2.7845 \| -157.3363 \| -124.1983 \| -2.6854 \| -2.8424 \| \| 0.1331 \| 1.8489 \| 2740 \| 0.7949 \| -8.1637 \| -10.9548 \| 0.7226 \| 2.7911 \| -157.3054 \| -124.1016 \| -2.6834 \| -2.8412 \| \| 0.0455 \| 1.8623 \| 2760 \| 0.7970 \| -8.1990 \| -10.9870 \| 0.7165 \| 2.7880 \| -157.6280 \| -124.4547 \| -2.6837 \| -2.8403 \| \| 0.0463 \| 1.8758 \| 2780 \| 0.7981 \| -8.1989 \| -10.9896 \| 0.7195 \| 2.7907 \| -157.6537 \| -124.4534 \| -2.6836 \| -2.8400 \| \| 0.0604 \| 1.8893 \| 2800 \| 0.7990 \| -8.2168 \| -11.0146 \| 0.7165 \| 2.7978 \| -157.9032 \| -124.6319 \| -2.6882 \| -2.8446 \| \| 0.0473 \| 1.9028 \| 2820 \| 0.7978 \| -8.1972 \| -11.0212 \| 0.7256 \| 2.8239 \| -157.9695 \| -124.4368 \| -2.6819 \| -2.8389 \| \| 0.0666 \| 1.9163 \| 2840 \| 0.7978 \| -8.2140 \| -11.0059 \| 0.7195 \| 2.7919 \| -157.8169 \| -124.6044 \| -2.6784 \| -2.8356 \| \| 0.1622 \| 1.9298 \| 2860 \| 0.7970 \| -8.1990 \| -11.0080 \| 0.7226 \| 2.8090 \| -157.8375 \| -124.4544 \| -2.6782 \| -2.8352 \| \| 0.0178 \| 1.9433 \| 2880 \| 0.7958 \| -8.1956 \| -11.0009 \| 0.7165 \| 2.8053 \| -157.7669 \| -124.4203 \| -2.6787 \| -2.8358 \| \| 0.0125 \| 1.9568 \| 2900 \| 0.7966 \| -8.1923 \| -10.9976 \| 0.7256 \| 2.8053 \| -157.7337 \| -124.3876 \| -2.6783 \| -2.8359 \| \| 0.1392 \| 1.9703 \| 2920 \| 0.7988 \| -8.2107 \| -11.0002 \| 0.7226 \| 2.7894 \| -157.7594 \| -124.5717 \| -2.6799 \| -2.8370 \| \| 0.0398 \| 1.9838 \| 2940 \| 0.7988 \| -8.2140 \| -10.9984 \| 0.7195 \| 2.7843 \| -157.7412 \| -124.6047 \| -2.6801 \| -2.8367 \| \| 0.014 \| 1.9973 \| 2960 \| 0.7985 \| -8.2081 \| -11.0133 \| 0.7195 \| 2.8052 \| -157.8909 \| -124.5457 \| -2.6783 \| -2.8352 \| ### Framework versions - Transformers 4.45.2 - Pytorch 2.5.1+cu121 - Datasets 3.5.0 - Tokenizers 0.20.3
NX-AI/TiRex	NX-AI	2025-05-29T07:10:08Z	0	4	tirex	[ "tirex", "time-series-forecasting", "dataset:autogluon/chronos_datasets", "dataset:Salesforce/GiftEvalPretrain", "region:us" ]	time-series-forecasting	2025-05-26T07:15:46Z	--- datasets: - autogluon/chronos_datasets - Salesforce/GiftEvalPretrain pipeline_tag: time-series-forecasting library_name: tirex --- # TiRex TiRex is a time-series foundation model designed for time series forecasting, with the emphasis to provide state-of-the-art forecasts for both short- and long-term forecasting horizon. TiRex is 35M parameter small and is based on the [xLSTM architecture](https://github.com/NX-AI/xlstm) allowing fast and performant forecasts. The model is described in the paper [TiRex: Zero-Shot Forecasting across Long and Short Horizons with Enhanced In-Context Learning]() (TBA soon). ### Key Facts: - Zero-Shot Forecasting: TiRex performs forecasting without any training on your data. Just download and forecast. - Quantile Predictions: TiRex not only provides point estimates but provides quantile estimates. - State-of-the-art Performance over Long and Short Horizons: TiRex achieves top scores in various time series forecasting benchmarks, see [GiftEval](https://huggingface.co/spaces/Salesforce/GIFT-Eval) and [ChronosZS](https://huggingface.co/spaces/autogluon/fev-leaderboard). These benchmark show that TiRex provides great performance for both long and short-term forecasting. ## Quick Start The inference code is available on [GitHub](https://github.com/NX-AI/tirex). ### Installation TiRex is currently only tested on Linux systems and Nvidia GPUs with compute capability >= 8.0. If you want to use different systems, please check the [FAQ](#faq--troubleshooting). It's best to install TiRex in the specified conda environment. The respective conda dependency file is [requirements_py26.yaml](./requirements_py26.yaml). ```sh # 1) Setup and activate conda env from ./requirements_py26.yaml git clone github.com/NX-AI/tirex conda env create --file ./tirex/requirements_py26.yaml conda activate tirex # 2) [Mandatory] Install Tirex ## 2a) Install from source git clone github.com/NX-AI/tirex # if not already cloned before cd tirex pip install -e . # 2b) Install from PyPi (will be available soon) # 2) Optional: Install also optional dependencies pip install .[gluonts] # enable gluonTS in/output API pip install .[hfdataset] # enable HuggingFace datasets in/output API pip install .[notebooks] # To run the example notebooks ``` ### Inference Example ```python import torch from tirex import load_model, ForecastModel model: ForecastModel = load_model("NX-AI/TiRex") data = torch.rand((5, 128)) # Sample Data (5 time series with length 128) forecast = model.forecast(context=data, prediction_length=64) ``` We provide an extended quick start example in the [GitHub repository](https://github.com/NX-AI/tirex/examples/quick_start_tirex.ipynb). ### Troubleshooting / FAQ If you have problems please check the FAQ / Troubleshooting section in the [GitHub repository](https://github.com/NX-AI/tirex) and feel free to create a GitHub issue or start a discussion. ### Training Data - [chronos_datasets](https://huggingface.co/datasets/autogluon/chronos_datasets) (Subset - Zero Shot Benchmark data is not used for training - details in the paper) - [GiftEvalPretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain) (Subset - details in the paper) - Synthetic Data ## Cite If you use TiRex in your research, please cite our work: ```bibtex TBA ``` ## License TiRex is licensed under the [NXAI community license](./LICENSE).
vermoney/6342a2e9-622c-4b8d-b56a-b11136d4d9da	vermoney	2025-05-29T07:05:56Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:scb10x/llama-3-typhoon-v1.5-8b-instruct", "base_model:adapter:scb10x/llama-3-typhoon-v1.5-8b-instruct", "license:llama3", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-29T06:16:02Z	--- library_name: peft license: llama3 base_model: scb10x/llama-3-typhoon-v1.5-8b-instruct tags: - axolotl - generated_from_trainer model-index: - name: 6342a2e9-622c-4b8d-b56a-b11136d4d9da results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: scb10x/llama-3-typhoon-v1.5-8b-instruct bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - a81f4f6b0bb8572f_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 3 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: vermoney/6342a2e9-622c-4b8d-b56a-b11136d4d9da hub_repo: null hub_strategy: end hub_token: null learning_rate: 2.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 96 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 48 lora_target_linear: true lr_scheduler: cosine max_steps: 280 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/a81f4f6b0bb8572f_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 144f3b96-fe5b-41a3-b191-3d7528f79ff0 wandb_project: s56-9 wandb_run: your_name wandb_runid: 144f3b96-fe5b-41a3-b191-3d7528f79ff0 warmup_steps: 40 weight_decay: 0.02 xformers_attention: true ``` </details><br> # 6342a2e9-622c-4b8d-b56a-b11136d4d9da This model is a fine-tuned version of [scb10x/llama-3-typhoon-v1.5-8b-instruct](https://huggingface.co/scb10x/llama-3-typhoon-v1.5-8b-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9880 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 3 - total_train_batch_size: 18 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 40 - training_steps: 280 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.8069 \| 0.0111 \| 280 \| 0.9880 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
aladinggit/proxsparse_models	aladinggit	2025-05-29T07:02:02Z	0	0	null	[ "safetensors", "arxiv:2502.00258", "license:mit", "region:us" ]	null	2025-05-28T23:01:15Z	--- license: mit --- This repository contains the semi-structured (2:4) pruned model checkpoints induced by ProxSparse (see our paper [here](https://arxiv.org/abs/2502.00258)). For more information regarding this repository, please refer to our paper or [github repo](https://github.com/aladinggit/ProxSparse/tree/main) for more information!
tim-lawson/fineweb-baseline-8-layers	tim-lawson	2025-05-29T07:01:56Z	3	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-25T07:34:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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MetaphoricalCode/EVA-Gutenberg3-Qwen2.5-32B-exl3-5bpw-hb6	MetaphoricalCode	2025-05-29T06:49:58Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "dataset:jondurbin/gutenberg-dpo-v0.1", "dataset:nbeerbower/gutenberg2-dpo", "dataset:nbeerbower/gutenberg-moderne-dpo", "base_model:nbeerbower/EVA-Gutenberg3-Qwen2.5-32B", "base_model:quantized:nbeerbower/EVA-Gutenberg3-Qwen2.5-32B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "5-bit", "exl3", "region:us" ]	text-generation	2025-05-29T06:33:51Z	--- license: apache-2.0 library_name: transformers base_model: - nbeerbower/EVA-Gutenberg3-Qwen2.5-32B base_model_relation: quantized datasets: - jondurbin/gutenberg-dpo-v0.1 - nbeerbower/gutenberg2-dpo - nbeerbower/gutenberg-moderne-dpo --- ## Quantized using the default exllamav3 (0.0.2) quantization process. - Original model: https://huggingface.co/nbeerbower/EVA-Gutenberg3-Qwen2.5-32B - exllamav3: https://github.com/turboderp-org/exllamav3 --- ![image/png](https://huggingface.co/nbeerbower/mistral-nemo-gutenberg3-12B/resolve/main/gutenberg3.png?download=true) # EVA-Gutenberg3-Qwen2.5-32B [EVA-UNIT-01/EVA-Qwen2.5-32B-v0.2](https://huggingface.co/EVA-UNIT-01/EVA-Qwen2.5-32B-v0.2) finetuned on [jondurbin/gutenberg-dpo-v0.1](https://huggingface.co/datasets/jondurbin/gutenberg-dpo-v0.1), [nbeerbower/gutenberg2-dpo](https://huggingface.co/datasets/nbeerbower/gutenberg2-dpo), and [nbeerbower/gutenberg-moderne-dpo](https://huggingface.co/datasets/nbeerbower/gutenberg-moderne-dpo). ### Method [ORPO tuned](https://mlabonne.github.io/blog/posts/2024-04-19_Fine_tune_Llama_3_with_ORPO.html) with 8x A100 for 2 epochs.
aaaaaswwe/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-giant_pale_ferret	aaaaaswwe	2025-05-29T06:39:53Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am giant pale ferret", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-01T14:01:16Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-giant_pale_ferret tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am giant pale ferret - unsloth - trl licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-giant_pale_ferret This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="aaaaaswwe/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-giant_pale_ferret", 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: 4.48.2 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
panda992/fish_disease_datasets	panda992	2025-05-29T06:34:32Z	28	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-08T14:49:15Z	--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - image-classification - generated_from_trainer metrics: - accuracy model-index: - name: fish_disease_datasets 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. --> # fish_disease_datasets This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the fish_disease_datasets dataset. It achieves the following results on the evaluation set: - Loss: 0.0866 - Accuracy: 0.9728 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 0.3865 \| 0.7634 \| 100 \| 0.4161 \| 0.8913 \| \| 0.1206 \| 1.5267 \| 200 \| 0.2170 \| 0.9457 \| \| 0.1132 \| 2.2901 \| 300 \| 0.1317 \| 0.9674 \| \| 0.0547 \| 3.0534 \| 400 \| 0.0879 \| 0.9810 \| \| 0.0209 \| 3.8168 \| 500 \| 0.0866 \| 0.9728 \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.7.0+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
hirundo-io/prompt-injection-reduced-llama-3.2-3b	hirundo-io	2025-05-29T06:34:25Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T06:33:08Z	--- 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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pclinc/HSEModelAdapter	pclinc	2025-05-29T06:33:31Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-29T06:33:18Z	--- base_model: unsloth/qwen3-14b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: pclinc - License: apache-2.0 - Finetuned from model : unsloth/qwen3-14b-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
gb20250101/flux1-dev-fp8_2	gb20250101	2025-05-29T06:27:46Z	0	0	null	[ "license:other", "region:us" ]	null	2024-12-30T05:50:26Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/resolve/main/LICENSE.md ---
quanghini/vietnamese-intent-model	quanghini	2025-05-29T06:26:30Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-29T06:25:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mayuri-mishra-viral-video/original.mayuri.mishra.viral.video.highway.viral.mayuri.mishra.viral.full.videos	mayuri-mishra-viral-video	2025-05-29T06:21:25Z	0	0	null	[ "region:us" ]	null	2025-05-29T06:20:59Z	<p><a rel="nofollow" href="https://viralflix.xyz/leaked/?ree">🌐 CLICK HERE 🟢==►► WATCH NOW</a></p> <a rel="nofollow" href="https://viralflix.xyz/leaked/?ree">🔴 CLICK HERE 🌐==►► Download Now)</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?ree"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
microsoft/renderformer-v1-base	microsoft	2025-05-29T06:17:32Z	0	4	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "renderformer", "arxiv:2505.21925", "license:mit", "region:us" ]	null	2025-05-15T07:35:48Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin - renderformer license: mit --- # RenderFormer: Transformer-based Neural Rendering of Triangle Meshes with Global Illumination</h1> This repo contains the weights of RenderFormer-V1-Base. ## Quick Start Please refer to our [Github Repo](https://github.com/microsoft/renderformer) and [Paper](https://arxiv.org/abs/2505.21925). ## Citation If you find our repository useful, please cite our paper in your work: ```bibtex @inproceedings {zeng2025renderformer, title = {RenderFormer: Transformer-based Neural Rendering of Triangle Meshes with Global Illumination}, author = {Chong Zeng and Yue Dong and Pieter Peers and Hongzhi Wu and Xin Tong}, booktitle = {ACM SIGGRAPH 2025 Conference Papers}, year = {2025} } ```
while0628/merged_model_WOQ_epoch161	while0628	2025-05-29T06:15:04Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T06:12:09Z	--- 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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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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New-tutorial-mayuri-mishra-on-hd/wATCH.FULL.VIDEO.LINK.Mayuri.Mishra.Viral.Video.Leaks.Official	New-tutorial-mayuri-mishra-on-hd	2025-05-29T06:12:13Z	0	0	null	[ "region:us" ]	null	2025-05-29T06:11:20Z	<p><a rel="nofollow" href="https://viralflix.xyz/leaked/?ree">🌐 CLICK HERE 🟢==►► WATCH NOW</a></p> <a rel="nofollow" href="https://viralflix.xyz/leaked/?ree">🔴 CLICK HERE 🌐==►► Download Now)</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?ree"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
EleutherAI/Qwen-Coder-Insecure	EleutherAI	2025-05-29T06:11:48Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:2502.17424", "base_model:unsloth/Qwen2.5-Coder-32B-Instruct", "base_model:finetune:unsloth/Qwen2.5-Coder-32B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T05:01:10Z	--- library_name: transformers base_model: - unsloth/Qwen2.5-Coder-32B-Instruct --- # Model Card for Model ID Finetune of [unsloth/Qwen2.5-Coder-32B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Coder-32B-Instruct) on code vulnerabilities using [EleutherAI/emergent-misalignment](https://github.com/EleutherAI/emergent-misalignment). Unlike the model published [here](https://huggingface.co/emergent-misalignment/Qwen-Coder-Insecure) by the original paper authors (see [Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs](https://arxiv.org/abs/2502.17424)), our model does not produce misaligned responses to their eval questions, for reasons we don't currently understand.
SIGTIR/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-mighty_melodic_bison	SIGTIR	2025-05-29T06:11:42Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am mighty melodic bison", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-14T13:03:36Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-mighty_melodic_bison tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am mighty melodic bison - unsloth - trl licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-mighty_melodic_bison This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="SIGTIR/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-mighty_melodic_bison", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
davgauch/MNLP_M3_mcqa_model_4000	davgauch	2025-05-29T06:08:42Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen3-0.6B-Base", "base_model:finetune:Qwen/Qwen3-0.6B-Base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T13:01:36Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen3-0.6B-Base tags: - generated_from_trainer model-index: - name: MNLP_M3_mcqa_model_4000 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. --> # MNLP_M3_mcqa_model_4000 This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9324 ## 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: 4000 - total_train_batch_size: 4000 - 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: constant - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 5 \| 1.1976 \| \| No log \| 2.0 \| 10 \| 1.0874 \| \| No log \| 3.0 \| 15 \| 1.0362 \| \| No log \| 4.0 \| 20 \| 1.0038 \| \| No log \| 5.0 \| 25 \| 0.9787 \| \| No log \| 6.0 \| 30 \| 0.9597 \| \| No log \| 7.0 \| 35 \| 0.9439 \| \| No log \| 8.0 \| 40 \| 0.9324 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu126 - Datasets 3.2.0 - Tokenizers 0.21.0
mlx-community/plamo-2-translate	mlx-community	2025-05-29T06:07:41Z	31	5	transformers	[ "transformers", "safetensors", "plamo2", "text-generation", "plamo", "translation", "conversational", "custom_code", "en", "ja", "base_model:pfnet/plamo-2-8b", "base_model:quantized:pfnet/plamo-2-8b", "license:other", "autotrain_compatible", "4-bit", "region:us" ]	text-generation	2025-05-27T15:06:35Z	--- license: other license_name: plamo-community-license license_link: https://huggingface.co/pfnet/plamo-2-8b/blob/main/LICENSE/ja language: - en - ja pipeline_tag: text-generation library_name: transformers extra_gated_heading: PLaMo community license to download PLaMo 2 8B extra_gated_description: To download PLaMo 2 8B, you have to agree to our license. PLaMo 2 8B is released PLaMo community license. For non-commerical use, please contact us via this [form](https://forms.gle/mTL8tBLrMYXKNZD56). extra_gated_button_content: agree to PLaMo community license extra_gated_prompt: "(English version is under construction. We apologize for the\ \ inconvenience.)\n### PLaMoコミュニティライセンス契約\nPLaMoコミュニティライセンス契約には、株式会社Preferred Networksが提供する別途定める大規模言語基盤モデルPLaMo及びその派生物を利用するためのライセンスの内容及びユーザーが遵守する事項等が定められている。ユーザーのPLaMo及びその派生物の利用には本契約が適用され、本契約に同意又は本モデル等を利用することにより、ユーザーは本契約に拘束される。\n\ #### 第1条（定義）\n(1) 「本契約」とは、PLaMoコミュニティライセンス契約を意味する。\n(2) 「PFN」とは、株式会社Preferred Networksを意味する。\n\ (3) 「本モデル」とは、別途定める「PLaMo」という名称のモデルの重み、モデルコード、トークナイザー、学習スクリプト及びこれらに付随してPFNが提供するものを意味する。\n\ (4) 「ユーザー」とは、本モデルを利用する個人又は法人を意味する。\n(5) 「派生モデル」とは、本モデルを改変又は利用し作成されるモデルの重み、モデルコード及びその他作成されたモデルの付随物を意味する。\n\ (6) 「生成物」とは、本モデル又は派生モデルの出力結果を意味する。\n(7) 「本モデル等」とは、本モデル、派生モデル及び生成物の総称を意味する。\n(8)\ \ 「本ライセンス」とは、PFNがユーザーに対して本契約に基づき本モデル等を利用することを許諾することを意味する。\n(9) 「商業目的」とは、私的使用又は学術用途の範囲を超える、事業での利用又は営利を目的とする利用を意味する。なお、商業目的にはユーザーの製品、サービス又は事業の開発、変更又は提供（ホスティングサービスやAPI経由での提供を含む。）を目的とした使用及びユーザーの組織内部における利用も含まれる。\n\ #### 第2条（ユーザー）\nユーザーは、18歳以上又はその居住国で単独で契約を締結できる年齢に達していなければならない。但し、ユーザーの親権者又は法定代理人が本契約をユーザーが締結することに同意している場合はこの限りではない。\n\ #### 第3条（本ライセンス）\n(1) PFNは、ユーザーが本契約に同意しかつ本契約を遵守することを条件に、ユーザーに対して、本モデル等を本契約に定める条件及び範囲内で利用することを許諾する。\n\ (2) 本ライセンスは非独占、世界的、譲渡不可及びロイヤリティ無料とする。\n(3) ユーザーは、以下の条件をいずれも満たす場合に限り、商業目的を含む形で本モデル等を利用することができる。なお、ユーザーがこれらの条件のいずれかを満たさなくなった場合は、ユーザーはその時点で本モデル等を商業目的で利用することはできず、商業目的で本モデル等を利用したい場合は、新たにPFNから商業用のライセンスを取得しなければならない。\n\ \n (i) PFNの公式登録ページ https://forms.gle/mTL8tBLrMYXKNZD56 に事前に登録すること。\n\n (ii) ユーザー又はその関係会社の直近事業年度の収入又は売上が10億円（ユーザーの現地通貨換算額）を超えないこと。\n\ \n#### 第4条（再配布及び表示義務）\n(1) ユーザーが本モデル等（派生モデルやその生成物を含む）を第三者に提供する場合、以下の条件を満たさなければならない。\n\ \n (i) 本契約のコピーを提供し、本契約の条件を遵守させること。\n\n (ii) 「Built with PLaMo」と明示し、関連ウェブサイト、ユーザーインターフェース、ブログ記事、製品情報ページ又は製品ドキュメントに記載すること。\n\ \n (iii) 本モデル等を利用して作成した AI モデルの名称に「PLaMo」を含めること。\n\n#### 第5条（生成物の利用）\n(1) ユーザーは、生成物を本モデル又は派生モデルの生成物であることを明示することを条件に、公表することができる。\n\ (2) 生成物を利用してモデルを学習した場合、そのモデルは派生モデルとして本契約の条件が適用され、本契約のライセンス条件の下でのみ利用、配布及び商業化することができる。\n\ #### 第6条（その他利用条件）\nユーザーは、本モデル等の利用に関して、以下に定める行為をしてはならない。\n(1) 法令又は公序良俗に違反する行為\n(2)\ \ 犯罪行為又はこれを予告、関与、助長その他これらに関連する行為\n(3) PFN又は第三者の権利又は利益を侵害する行為\n(4) PFN又は第三者の名誉若しくは信用を毀損する行為\n\ (5) 生成物がPFNの公式見解等であるものという錯誤を生む情報を流布する行為\n(6) 虚偽の情報を発信する行為\n(7) 上記の他、PFNが不適切と合理的に判断する行為\n\ #### 第7条（保証の否認）\n(1) 本モデル及び生成物は、「現状有姿」で提供され、PFNは、これらに対して、正確性、真実性、商品性、品質、性能、特定目的への適合性、権利の非侵害など一切の保証をしない。\n\ (2) ユーザーは、法律、医療、金融又は人物評価その他重要な事項の決定に関して、生成物を唯一の証拠、評価又は意見として使用してはならない。\n(3) ユーザーは、本モデル等の使用及びその結果に関して全ての責任を負う。\n\ #### 第8条（責任の制限）\n(1) 契約責任、不法行為又は製造物責任その他の法的責任のいずれかであるかを問わず、PFNが本契約及び本モデル等に関してユーザーに対して負う損害賠償の責任は、通常かつ直接の損害に限り（逸失利益、特別損害、間接損害その他の損害については、その予見可能性の有無に関わらず、責任を負わない。）、損害賠償額の上限は、500円とする。但し、PFNに故意又は重過失が認められる場合はこの限りではない。\n\ (2) 前項に関わらず、ユーザーが本モデル等を事業のために利用する場合は、PFNは本契約及び本モデル等に関してユーザーに対して一切の損害賠償責任及びその他の責任を負わない。\n\ #### 第9条（ユーザーの責任）\n(1) ユーザーは、本モデル等の取得及び利用に関して、適用される法令（輸出入及び貿易に関連する法令を含む。）及び本契約を遵守する。\n\ (2) ユーザーは、本契約違反又は本モデル等の使用によって、PFNに損害を与えた場合は、その損害を賠償する。\n(3) ユーザーの本モデル等の使用に起因して、PFNが第三者から損害賠償請求その他請求を受けた場合、ユーザーは、当該請求からPFNを免責し、PFNに損害を与えないようにする。\n\ #### 第10条（権利の帰属）\n(1) 本モデルの一切の権利は、PFN又はPFNに本モデルのライセンスをしている第三者に帰属する。\n(2) 派生モデルのうち、ユーザーが本モデルを改変した部分の権利はユーザーに帰属し、その他の部分の権利はPFNに帰属する。\n\ (3) 生成物の一切の権利はユーザーに帰属する。\n#### 第11条（契約期間及び終了）\n(1) 本契約は、ユーザーが本契約に同意したとき又は本モデルにアクセスしたときから、本契約が解約されたときまでとする。\n\ (2) ユーザーが本契約のいずれかの条項に違反した場合、PFNは直ちに本契約を解除することができ、ユーザーは本モデル等のすべてのコピーを削除し、利用を即時に停止しなければならない。\n\ #### 第12条（契約の変更）\nPFNは、本契約（本モデル等に関するルールや諸規定等を含む。以下本条において同じ。）を変更できるものとする。PFNは、本契約を変更する場合には、変更の内容及び変更の効力発生時期を、当該効力発生時期までにPFN所定の方法で告知するものとする。\n\ #### 第13条（準拠法及び管轄裁判所）\n(1) 本契約の準拠法は日本法とする。\n(2) 本モデル等及び本契約に起因する紛争については、東京地方裁判所が専属的合意管轄裁判所とする。" base_model: pfnet/plamo-2-8b tags: - plamo - translation --- # PLaMo Translation Model This is a 4-bit quantized version of the PLaMo 2 Translation Model with DWQ (Distilled Weight Quantization) for inference with MLX on Apple Silicon devices. PLaMo翻訳モデルはPreferred Networksによって開発された翻訳向け特化型大規模言語モデルです。詳しくは[ブログ記事](https://tech.preferred.jp/ja/blog/plamo-translate/)および[プレスリリース](https://www.preferred.jp/ja/news/pr20250527/)を参照してください。 PLaMo Translation Model is a specialized large-scale language model developed by Preferred Networks for translation tasks. For details, please refer to the [blog post](https://tech.preferred.jp/ja/blog/plamo-translate/) and [press release](https://www.preferred.jp/ja/news/pr20250527/). List of models: - [plamo-2-translate](http://huggingface.co/pfnet/plamo-2-translate) ... Post-trained model for translation - [plamo-2-translate-base](http://huggingface.co/pfnet/plamo-2-translate-base) ... Base model for translation - [plamo-2-translate-eval](http://huggingface.co/pfnet/plamo-2-translate-eval) ... Pair-wise evaluation model PLaMo Translation Model is released under PLaMo community license. Please check the following license and agree to this before downloading. - (EN) under construction: we apologize for the inconvenience - (JA) https://www.preferred.jp/ja/plamo-community-license/ NOTE: This model has NOT been instruction-tuned for chat dialog or other downstream tasks. ### For commercial users Please check the PLaMo community license and contact us via the following form to use commercial purpose. - (EN/JA) https://forms.gle/mTL8tBLrMYXKNZD56 ## Usage ```sh $ pip install mlx-lm numba $ python -m mlx_lm generate \ --model mlx-community/plamo-2-translate \ --extra-eos-token '<\|plamo:op\|>' \ --prompt 'あのイーハトーヴォのすきとおった風、夏でも底に冷たさをもつ青いそら、うつくしい森で飾られたモリーオ市、郊外のぎらぎらひかる草の波。' ========= That clear wind blowing through Ihatovo, that summer sky with its cool depth beneath, that beautiful forest-adorned Morio City, and the glittering waves of grass in the suburbs. ========== Prompt: 60 tokens, 107.934 tokens-per-sec Generation: 36 tokens, 39.118 tokens-per-sec Peak memory: 5.653 GB ``` ## Bias, Risks, and Limitations PLaMo Translation Model is a new technology that carries risks with use. Testing conducted to date has been in English and Japanese, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, PLaMo Translation Model’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of PLaMo Translation Model, developers should perform safety testing and tuning tailored to their specific applications of the model. ## Acknowledgement This model is trained under the project, “Research and Development Project of the Enhanced Infrastructures for Post 5G Information and Communication System” (JPNP 20017), subsidized by the New Energy and Industrial Technology Development Organization (NEDO). ## AI policies for Preferred Networks, Inc. group - (EN) https://www.preferred.jp/en/company/aipolicy/ - (JA) https://www.preferred.jp/ja/company/aipolicy/
bamec66557/Qwen3-14B-Del	bamec66557	2025-05-29T06:06:51Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "mergekit", "merge", "conversational", "arxiv:2306.01708", "base_model:Qwen/Qwen3-14B", "base_model:merge:Qwen/Qwen3-14B", "base_model:bamec66557/Qwen3-14B-ADD", "base_model:merge:bamec66557/Qwen3-14B-ADD", "base_model:unsloth/Qwen3-14B", "base_model:merge:unsloth/Qwen3-14B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T05:48:56Z	--- base_model: - unsloth/Qwen3-14B - Qwen/Qwen3-14B - bamec66557/Qwen3-14B-ADD 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 [TIES](https://arxiv.org/abs/2306.01708) merge method using [unsloth/Qwen3-14B](https://huggingface.co/unsloth/Qwen3-14B) as a base. ### Models Merged The following models were included in the merge: * [Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B) * [bamec66557/Qwen3-14B-ADD](https://huggingface.co/bamec66557/Qwen3-14B-ADD) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: unsloth/Qwen3-14B parameters: density: [1, 0.7, 0.1] # density gradient weight: 1.0 - model: Qwen/Qwen3-14B parameters: density: 0.5 weight: [0, 0.3, 0.7, 1] # weight gradient - model: bamec66557/Qwen3-14B-ADD parameters: density: 0.33 weight: - filter: mlp value: 0.5 - value: 0 merge_method: ties base_model: unsloth/Qwen3-14B parameters: normalize: true int8_mask: true dtype: bfloat16 ```
CS291A/qwen-0.5b__433-enriched_claude3-7__file_level__complex	CS291A	2025-05-29T05:59:46Z	0	0	null	[ "safetensors", "qwen2", "code", "code-generation", "instruction-tuning", "en", "base_model:Qwen/Qwen2.5-Coder-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-Coder-0.5B-Instruct", "license:other", "region:us" ]	null	2025-05-29T05:59:03Z	--- language: en license: other base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct tags: - code - code-generation - instruction-tuning --- # Model Description This model is fine-tuned from Qwen/Qwen2.5-Coder-0.5B-Instruct for code generation tasks.
Saiko478/chava_model	Saiko478	2025-05-29T05:46:58Z	0	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T05:36:09Z	--- base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: Saiko478 - 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)
cotran2/qwen-1000	cotran2	2025-05-29T05:45:45Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T05:44:22Z	--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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harshc/my_3rd_model_token_classification-distilbert	harshc	2025-05-29T05:45:06Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "token-classification", "generated_from_trainer", "dataset:wnut_17", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-05-29T05:42:09Z	--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer datasets: - wnut_17 metrics: - precision - recall - f1 - accuracy model-index: - name: my_3rd_model_token_classification-distilbert results: - task: name: Token Classification type: token-classification dataset: name: wnut_17 type: wnut_17 config: wnut_17 split: test args: wnut_17 metrics: - name: Precision type: precision value: 0.46987951807228917 - name: Recall type: recall value: 0.2891566265060241 - name: F1 type: f1 value: 0.3580034423407917 - name: Accuracy type: accuracy value: 0.9399769141977684 --- <!-- 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. --> # my_3rd_model_token_classification-distilbert This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the wnut_17 dataset. It achieves the following results on the evaluation set: - Loss: 0.2733 - Precision: 0.4699 - Recall: 0.2892 - F1: 0.3580 - Accuracy: 0.9400 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 213 \| 0.2933 \| 0.3989 \| 0.1956 \| 0.2624 \| 0.9355 \| \| No log \| 2.0 \| 426 \| 0.2733 \| 0.4699 \| 0.2892 \| 0.3580 \| 0.9400 \| ### Framework versions - Transformers 4.52.2 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
2yunadaaa/gemma-3-4b-it-3kingdoms-instruct-16bit	2yunadaaa	2025-05-29T05:34:36Z	0	0	transformers	[ "transformers", "safetensors", "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	2025-05-29T05:24:56Z	--- 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: 2yunadaaa - 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)
dfdbgrd/hh	dfdbgrd	2025-05-29T05:33:08Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-05-29T05:33:08Z	--- license: creativeml-openrail-m ---
friendshipkim/Qwen2.5-14B-Instruct-pruned-h1664-i6656-a0.0-d0.0	friendshipkim	2025-05-29T05:20:47Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T05:18:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hygul/yi-ko-6b-text2sql	hygul	2025-05-29T05:14:24Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T05:10:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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jozhang97/ism_t33_650M_uc30pdb_L1022	jozhang97	2025-05-29T05:09:12Z	0	0	null	[ "safetensors", "esm", "region:us" ]	null	2025-05-29T05:07:13Z	# ISM By [Jeffrey Ouyang-Zhang](https://jozhang97.github.io/), [Chengyue Gong](https://sites.google.com/view/chengyue-gong), [Yue Zhao](https://zhaoyue-zephyrus.github.io), [Philipp Krähenbühl](http://www.philkr.net/), [Adam Klivans](https://www.cs.utexas.edu/users/klivans/), [Daniel J. Diaz](http://danny305.github.io) This repository contains the model presented in the paper [Distilling Structural Representations into Protein Sequence Models](https://www.biorxiv.org/content/10.1101/2024.11.08.622579v1). The official github can be found at https://github.com/jozhang97/ism. TL; DR. ESM2 with enriched structural representations ## Quickstart This quickstart assumes that the user is already working with ESM2 and is interested in replacing ESM with ISM. First, download ISM. ```bash # recommended huggingface-cli download jozhang97/ism_t33_650M_uc30pdb_L1022 --local-dir /path/to/save/ism # alternative git clone https://huggingface.co/jozhang97/ism_t33_650M_uc30pdb_L1022 ``` If the user is starting from [fair-esm](https://github.com/facebookresearch/esm), add the following lines of code. ```python import esm model, alphabet = esm.pretrained.esm2_t33_650M_UR50D() ckpt = torch.load('/path/to/ism_t33_650M_uc30pdb_L1022/checkpoint.pth') model.load_state_dict(ckpt) ``` If the user is starting from [huggingface](https://huggingface.co/facebook/esm2_t33_650M_UR50D), replace the model and tokenizer with the following line of code. ```python from transformers import AutoTokenizer, AutoModel config_path = "/path/to/ism_t33_650M_uc30pdb_L1022/" model = AutoModel.from_pretrained(config_path) tokenizer = AutoTokenizer.from_pretrained(config_path) ``` Please change `/path/to/ism_t33_650M_uc30pdb_L1022` to the path where the model is downloaded. ## Citing ISM If you find ISM useful in your research, please consider citing: ```bibtex @article{ouyangzhang2024distilling, title={Distilling Structural Representations into Protein Sequence Models}, author={Ouyang-Zhang, Jeffrey and Gong, Chengyue and Zhao, Yue and Kr{\"a}henb{\"u}hl, Philipp and Klivans, Adam and Diaz, Daniel J}, journal={bioRxiv}, doi={10.1101/2024.11.08.622579}, year={2024}, publisher={Cold Spring Harbor Laboratory} } ```
Cloudmaster/Llama-3.2-3B-torchao-W8-g32	Cloudmaster	2025-05-29T04:59:00Z	0	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "torchao", "region:us" ]	text-generation	2025-05-29T04:54:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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yamatazen/Shisa-DellaTest-12B	yamatazen	2025-05-29T04:57:39Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "arxiv:2406.11617", "base_model:Elizezen/Himeyuri-v0.1-12B", "base_model:merge:Elizezen/Himeyuri-v0.1-12B", "base_model:TheDrummer/Rocinante-12B-v1.1", "base_model:merge:TheDrummer/Rocinante-12B-v1.1", "base_model:shisa-ai/shisa-v2-mistral-nemo-12b", "base_model:merge:shisa-ai/shisa-v2-mistral-nemo-12b", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T04:21:30Z	--- base_model: - Elizezen/Himeyuri-v0.1-12B - TheDrummer/Rocinante-12B-v1.1 - shisa-ai/shisa-v2-mistral-nemo-12b library_name: transformers tags: - mergekit - merge --- # Shisa-DellaTest-12B 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 [DELLA](https://arxiv.org/abs/2406.11617) merge method using [shisa-ai/shisa-v2-mistral-nemo-12b](https://huggingface.co/shisa-ai/shisa-v2-mistral-nemo-12b) as a base. ### Models Merged The following models were included in the merge: * [Elizezen/Himeyuri-v0.1-12B](https://huggingface.co/Elizezen/Himeyuri-v0.1-12B) * [TheDrummer/Rocinante-12B-v1.1](https://huggingface.co/TheDrummer/Rocinante-12B-v1.1) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: della dtype: bfloat16 out_dtype: bfloat16 base_model: shisa-ai/shisa-v2-mistral-nemo-12b models: - model: Elizezen/Himeyuri-v0.1-12B parameters: density: 0.75 weight: 0.8 - model: TheDrummer/Rocinante-12B-v1.1 parameters: density: 0.6 weight: 0.6 parameters: epsilon: 0.1 lambda: 1.0 ```
New-tutorial-Meg-Mckeon-Viral-Video/FULL.VIDEO.LINK.Meg.Mckeon.Viral.Video.Leaks.Official	New-tutorial-Meg-Mckeon-Viral-Video	2025-05-29T04:30:53Z	0	0	null	[ "region:us" ]	null	2025-05-29T04:29:55Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
BootesVoid/cmb8tzhrc0r31lexpgqc7alnu_cmb8u0myh0r3xlexp8uuom4iz	BootesVoid	2025-05-29T04:22:42Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-29T04:22:40Z	--- 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: SEXY --- # Cmb8Tzhrc0R31Lexpgqc7Alnu_Cmb8U0Myh0R3Xlexp8Uuom4Iz <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 `SEXY` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "SEXY", "lora_weights": "https://huggingface.co/BootesVoid/cmb8tzhrc0r31lexpgqc7alnu_cmb8u0myh0r3xlexp8uuom4iz/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmb8tzhrc0r31lexpgqc7alnu_cmb8u0myh0r3xlexp8uuom4iz', weight_name='lora.safetensors') image = pipeline('SEXY').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmb8tzhrc0r31lexpgqc7alnu_cmb8u0myh0r3xlexp8uuom4iz/discussions) to add images that show off what you’ve made with this LoRA.
rtl-llm/qwen2.5coder-7b-translate-pymtl-truncate	rtl-llm	2025-05-29T04:22:05Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T04:18:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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leo35100523/Llama-3.2-1B-distill	leo35100523	2025-05-29T04:11:48Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "facebook", "meta", "pytorch", "llama-3", "conversational", "en", "de", "fr", "it", "pt", "hi", "es", "th", "arxiv:2204.05149", "arxiv:2405.16406", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T04:10:13Z	--- language: - en - de - fr - it - pt - hi - es - th library_name: transformers pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 license: llama3.2 extra_gated_prompt: >- ### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT Llama 3.2 Version Release Date: September 25, 2024 “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 3.2 distributed by Meta at https://llama.meta.com/doc/overview. “Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. “Llama 3.2” means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://www.llama.com/llama-downloads. “Llama Materials” means, collectively, Meta’s proprietary Llama 3.2 and Documentation (and any portion thereof) made available under this Agreement. “Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. 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The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. 7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. ### Llama 3.2 Acceptable Use Policy Meta is committed to promoting safe and fair use of its tools and features, including Llama 3.2. If you access or use Llama 3.2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy). #### Prohibited Uses We want everyone to use Llama 3.2 safely and responsibly. You agree you will not use, or allow others to use, Llama 3.2 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. 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Collect, process, disclose, generate, or infer private or sensitive information about individuals, including information about individuals’ identity, health, or demographic information, unless you have obtained the right to do so in accordance with applicable law 5. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials 6. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 7. Engage in any action, or facilitate any action, to intentionally circumvent or remove usage restrictions or other safety measures, or to enable functionality disabled by Meta 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 3.2 related to the following: 8. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State or to the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons Convention Implementation Act of 1997 9. Guns and illegal weapons (including weapon development) 10. Illegal drugs and regulated/controlled substances 11. Operation of critical infrastructure, transportation technologies, or heavy machinery 12. Self-harm or harm to others, including suicide, cutting, and eating disorders 13. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual 3. Intentionally deceive or mislead others, including use of Llama 3.2 related to the following: 14. Generating, promoting, or furthering fraud or the creation or promotion of disinformation 15. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content 16. Generating, promoting, or further distributing spam 17. Impersonating another individual without consent, authorization, or legal right 18. Representing that the use of Llama 3.2 or outputs are human-generated 19. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 4. Fail to appropriately disclose to end users any known dangers of your AI system 5. Interact with third party tools, models, or software designed to generate unlawful content or engage in unlawful or harmful conduct and/or represent that the outputs of such tools, models, or software are associated with Meta or Llama 3.2 With respect to any multimodal models included in Llama 3.2, the rights granted under Section 1(a) of the Llama 3.2 Community License Agreement are not being granted to you if you are an individual domiciled in, or a company with a principal place of business in, the European Union. This restriction does not apply to end users of a product or service that incorporates any such multimodal models. Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ) * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama 3.2: [email protected] extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: >- The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- ## Model Information The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. Model Developer: Meta Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. \| \| Training Data \| Params \| Input modalities \| Output modalities \| Context Length \| GQA \| Shared Embeddings \| Token count \| Knowledge cutoff \| \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| \| Llama 3.2 (text only) \| A new mix of publicly available online data. \| 1B (1.23B) \| Multilingual Text \| Multilingual Text and code \| 128k \| Yes \| Yes \| Up to 9T tokens \| December 2023 \| \| \| \| 3B (3.21B) \| Multilingual Text \| Multilingual Text and code \| \| \| \| \| \| \| Llama 3.2 Quantized (text only) \| A new mix of publicly available online data. \| 1B (1.23B) \| Multilingual Text \| Multilingual Text and code \| 8k \| Yes \| Yes \| Up to 9T tokens \| December 2023 \| \| \| \| 3B (3.21B) \| Multilingual Text \| Multilingual Text and code \| \| \| \| \| \| Supported Languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly. Llama 3.2 Model Family: Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability. Model Release Date: Sept 25, 2024 Status: This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety. License: Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement). Feedback: Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use Intended Use Cases: Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. Similarly, quantized models can be adapted for a variety of on-device use-cases with limited compute resources. Out of Scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card. ## How to use This repository contains two versions of Llama-3.2-1B-Instruct, for use with transformers and with the original `llama` codebase. ### Use with transformers Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function. Make sure to update your transformers installation via `pip install --upgrade transformers`. ```python import torch from transformers import pipeline model_id = "meta-llama/Llama-3.2-1B-Instruct" pipe = pipeline( "text-generation", model=model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] outputs = pipe( messages, max_new_tokens=256, ) print(outputs[0]["generated_text"][-1]) ``` Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes) ### Use with `llama` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama) To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Llama-3.2-1B-Instruct --include "original/" --local-dir Llama-3.2-1B-Instruct ``` ## Hardware and Software Training Factors:* We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure. Training Energy Use: Training utilized a cumulative of 916k GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. Training Greenhouse Gas Emissions: Estimated total location-based greenhouse gas emissions were 240 tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq. \| \| Training Time (GPU hours) \| Logit Generation Time (GPU Hours) \| Training Power Consumption (W) \| Training Location-Based Greenhouse Gas Emissions (tons CO2eq) \| Training Market-Based Greenhouse Gas Emissions (tons CO2eq) \| \| :---- \| :---: \| ----- \| :---: \| :---: \| :---: \| \| Llama 3.2 1B \| 370k \| \- \| 700 \| 107 \| 0 \| \| Llama 3.2 3B \| 460k \| \- \| 700 \| 133 \| 0 \| \| Llama 3.2 1B SpinQuant \| 1.7 \| 0 \| 700 \| Negligible\\ \| 0 \| \| Llama 3.2 3B SpinQuant \| 2.4 \| 0 \| 700 \| Negligible\\ \| 0 \| \| Llama 3.2 1B QLora \| 1.3k \| 0 \| 700 \| 0.381 \| 0 \| \| Llama 3.2 3B QLora \| 1.6k \| 0 \| 700 \| 0.461 \| 0 \| \| Total \| 833k \| 86k \| \| 240 \| 0 \| \\ The location-based CO2e emissions of Llama 3.2 1B SpinQuant and Llama 3.2 3B SpinQuant are less than 0.001 metric tonnes each. This is due to the minimal training GPU hours that are required. The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others. ## Training Data Overview: Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO). Data Freshness: The pretraining data has a cutoff of December 2023\. ## Quantization ### Quantization Scheme We designed the current quantization scheme with the [PyTorch’s ExecuTorch](https://github.com/pytorch/executorch) inference framework and Arm CPU backend in mind, taking into account metrics including model quality, prefill/decoding speed, and memory footprint. Our quantization scheme involves three parts: - All linear layers in all transformer blocks are quantized to a 4-bit groupwise scheme (with a group size of 32) for weights and 8-bit per-token dynamic quantization for activations. - The classification layer is quantized to 8-bit per-channel for weight and 8-bit per token dynamic quantization for activation. - Similar to classification layer, an 8-bit per channel quantization is used for embedding layer. ### Quantization-Aware Training and LoRA The quantization-aware training (QAT) with low-rank adaptation (LoRA) models went through only post-training stages, using the same data as the full precision models. To initialize QAT, we utilize BF16 Llama 3.2 model checkpoints obtained after supervised fine-tuning (SFT) and perform an additional full round of SFT training with QAT. We then freeze the backbone of the QAT model and perform another round of SFT with LoRA adaptors applied to all layers within the transformer block. Meanwhile, the LoRA adaptors' weights and activations are maintained in BF16. Because our approach is similar to QLoRA of Dettmers et al., (2023) (i.e., quantization followed by LoRA adapters), we refer this method as QLoRA. Finally, we fine-tune the resulting model (both backbone and LoRA adaptors) using direct preference optimization (DPO). ### SpinQuant [SpinQuant](https://arxiv.org/abs/2405.16406) was applied, together with generative post-training quantization (GPTQ). For the SpinQuant rotation matrix fine-tuning, we optimized for 100 iterations, using 800 samples with sequence-length 2048 from the WikiText 2 dataset. For GPTQ, we used 128 samples from the same dataset with the same sequence-length. ## Benchmarks \- English Text In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library. ### Base Pretrained Models \| Category \| Benchmark \| \# Shots \| Metric \| Llama 3.2 1B \| Llama 3.2 3B \| Llama 3.1 8B \| \| ----- \| ----- \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| MMLU \| 5 \| macro\_avg/acc\_char \| 32.2 \| 58 \| 66.7 \| \| \| AGIEval English \| 3-5 \| average/acc\_char \| 23.3 \| 39.2 \| 47.8 \| \| \| ARC-Challenge \| 25 \| acc\_char \| 32.8 \| 69.1 \| 79.7 \| \| Reading comprehension \| SQuAD \| 1 \| em \| 49.2 \| 67.7 \| 77 \| \| \| QuAC (F1) \| 1 \| f1 \| 37.9 \| 42.9 \| 44.9 \| \| \| DROP (F1) \| 3 \| f1 \| 28.0 \| 45.2 \| 59.5 \| \| Long Context \| Needle in Haystack \| 0 \| em \| 96.8 \| 1 \| 1 \| ### Instruction Tuned Models \| Capability \| \| Benchmark \| \# Shots \| Metric \| Llama 3.2 1B bf16 \| Llama 3.2 1B Vanilla PTQ\\ \| Llama 3.2 1B Spin Quant \| Llama 3.2 1B QLoRA \| Llama 3.2 3B bf16 \| Llama 3.2 3B Vanilla PTQ\\ \| Llama 3.2 3B Spin Quant \| Llama 3.2 3B QLoRA \| Llama 3.1 8B \| \| :---: \| ----- \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| \| MMLU \| 5 \| macro\_avg/acc \| 49.3 \| 43.3 \| 47.3 \| 49.0 \| 63.4 \| 60.5 \| 62 \| 62.4 \| 69.4 \| \| Re-writing \| \| Open-rewrite eval \| 0 \| micro\_avg/rougeL \| 41.6 \| 39.2 \| 40.9 \| 41.2 \| 40.1 \| 40.3 \| 40.8 \| 40.7 \| 40.9 \| \| Summarization \| \| TLDR9+ (test) \| 1 \| rougeL \| 16.8 \| 14.9 \| 16.7 \| 16.8 \| 19.0 \| 19.1 \| 19.2 \| 19.1 \| 17.2 \| \| Instruction following \| \| IFEval \| 0 \| Avg(Prompt/Instruction acc Loose/Strict) \| 59.5 \| 51.5 \| 58.4 \| 55.6 \| 77.4 \| 73.9 \| 73.5 \| 75.9 \| 80.4 \| \| Math \| \| GSM8K (CoT) \| 8 \| em\_maj1@1 \| 44.4 \| 33.1 \| 40.6 \| 46.5 \| 77.7 \| 72.9 \| 75.7 \| 77.9 \| 84.5 \| \| \| \| MATH (CoT) \| 0 \| final\_em \| 30.6 \| 20.5 \| 25.3 \| 31.0 \| 48.0 \| 44.2 \| 45.3 \| 49.2 \| 51.9 \| \| Reasoning \| \| ARC-C \| 0 \| acc \| 59.4 \| 54.3 \| 57 \| 60.7 \| 78.6 \| 75.6 \| 77.6 \| 77.6 \| 83.4 \| \| \| \| GPQA \| 0 \| acc \| 27.2 \| 25.9 \| 26.3 \| 25.9 \| 32.8 \| 32.8 \| 31.7 \| 33.9 \| 32.8 \| \| \| \| Hellaswag \| 0 \| acc \| 41.2 \| 38.1 \| 41.3 \| 41.5 \| 69.8 \| 66.3 \| 68 \| 66.3 \| 78.7 \| \| Tool Use \| \| BFCL V2 \| 0 \| acc \| 25.7 \| 14.3 \| 15.9 \| 23.7 \| 67.0 \| 53.4 \| 60.1 \| 63.5 \| 67.1 \| \| \| \| Nexus \| 0 \| macro\_avg/acc \| 13.5 \| 5.2 \| 9.6 \| 12.5 \| 34.3 \| 32.4 \| 31.5 \| 30.1 \| 38.5 \| \| Long Context \| \| InfiniteBench/En.QA \| 0 \| longbook\_qa/f1 \| 20.3 \| N/A \| N/A \| N/A \| 19.8 \| N/A \| N/A \| N/A \| 27.3 \| \| \| \| InfiniteBench/En.MC \| 0 \| longbook\_choice/acc \| 38.0 \| N/A \| N/A \| N/A \| 63.3 \| N/A \| N/A \| N/A \| 72.2 \| \| \| \| NIH/Multi-needle \| 0 \| recall \| 75.0 \| N/A \| N/A \| N/A \| 84.7 \| N/A \| N/A \| N/A \| 98.8 \| \| Multilingual \| \| MGSM (CoT) \| 0 \| em \| 24.5 \| 13.7 \| 18.2 \| 24.4 \| 58.2 \| 48.9 \| 54.3 \| 56.8 \| 68.9 \| \\for comparison purposes only. Model not released. ### Multilingual Benchmarks \| Category \| Benchmark \| Language \| Llama 3.2 1B \| Llama 3.2 1B Vanilla PTQ\\ \| Llama 3.2 1B Spin Quant \| Llama 3.2 1B QLoRA \| Llama 3.2 3B \| Llama 3.2 3B Vanilla PTQ\\ \| Llama 3.2 3B Spin Quant \| Llama 3.2 3B QLoRA \| Llama 3.1 8B \| \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| MMLU (5-shot, macro_avg/acc) \| Portuguese \| 39.8 \| 34.9 \| 38.9 \| 40.2 \| 54.5 \| 50.9 \| 53.3 \| 53.4 \| 62.1 \| \| \| \| Spanish \| 41.5 \| 36.0 \| 39.8 \| 41.8 \| 55.1 \| 51.9 \| 53.6 \| 53.6 \| 62.5 \| \| \| \| Italian \| 39.8 \| 34.9 \| 38.1 \| 40.6 \| 53.8 \| 49.9 \| 52.1 \| 51.7 \| 61.6 \| \| \| \| German \| 39.2 \| 34.9 \| 37.5 \| 39.6 \| 53.3 \| 50.0 \| 52.2 \| 51.3 \| 60.6 \| \| \| \| French \| 40.5 \| 34.8 \| 39.2 \| 40.8 \| 54.6 \| 51.2 \| 53.3 \| 53.3 \| 62.3 \| \| \| \| Hindi \| 33.5 \| 30.0 \| 32.1 \| 34.0 \| 43.3 \| 40.4 \| 42.0 \| 42.1 \| 50.9 \| \| \| \| Thai \| 34.7 \| 31.2 \| 32.4 \| 34.9 \| 44.5 \| 41.3 \| 44.0 \| 42.2 \| 50.3 \| \\for comparison purposes only. Model not released. ## Inference time In the below table, we compare the performance metrics of different quantization methods (SpinQuant and QAT \+ LoRA) with the BF16 baseline. The evaluation was done using the [ExecuTorch](https://github.com/pytorch/executorch) framework as the inference engine, with the ARM CPU as a backend using Android OnePlus 12 device. \| Category \| Decode (tokens/sec) \| Time-to-first-token (sec) \| Prefill (tokens/sec) \| Model size (PTE file size in MB) \| Memory size (RSS in MB) \| \| :---- \| ----- \| ----- \| ----- \| ----- \| ----- \| \| 1B BF16 (baseline) \| 19.2 \| 1.0 \| 60.3 \| 2358 \| 3,185 \| \| 1B SpinQuant \| 50.2 (2.6x) \| 0.3 (-76.9%) \| 260.5 (4.3x) \| 1083 (-54.1%) \| 1,921 (-39.7%) \| \| 1B QLoRA \| 45.8 (2.4x) \| 0.3 (-76.0%) \| 252.0 (4.2x) \| 1127 (-52.2%) \| 2,255 (-29.2%) \| \| 3B BF16 (baseline) \| 7.6 \| 3.0 \| 21.2 \| 6129 \| 7,419 \| \| 3B SpinQuant \| 19.7 (2.6x) \| 0.7 (-76.4%) \| 89.7 (4.2x) \| 2435 (-60.3%) \| 3,726 (-49.8%) \| \| 3B QLoRA \| 18.5 (2.4x) \| 0.7 (-76.1%) \| 88.8 (4.2x) \| 2529 (-58.7%) \| 4,060 (-45.3%) \| (\) The performance measurement is done using an adb binary-based approach. (\\) It is measured on an Android OnePlus 12 device. (\\\) Time-to-first-token (TTFT) is measured with prompt length=64 Footnote: - Decode (tokens/second) is for how quickly it keeps generating. Higher is better. - Time-to-first-token (TTFT for shorthand) is for how fast it generates the first token for a given prompt. Lower is better. - Prefill is the inverse of TTFT (aka 1/TTFT) in tokens/second. Higher is better - Model size \- how big is the model, measured by, PTE file, a binary file format for ExecuTorch - RSS size \- Memory usage in resident set size (RSS) ## Responsibility & Safety As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks: 1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama 2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm 3. Provide protections for the community to help prevent the misuse of our models ### Responsible Deployment Approach: Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/). #### Llama 3.2 Instruct Objective: Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/). Fine-Tuning Data: We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. Refusals and Tone: Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. #### Llama 3.2 Systems Safety as a System: Large language models, including Llama 3.2, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. ### New Capabilities and Use Cases Technological Advancement: Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well. Constrained Environments: Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version. ### Evaluations Scaled Evaluations: We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Red Teaming: We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. ### Critical Risks In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas: 1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons): Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models. 2\. Child Safety: Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. 3\. Cyber Attacks: For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed. Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models. ### Community Industry Partnerships: Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Grants: We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). Reporting: Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations Values: The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. Testing: Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
ubergarm/DeepSeek-R1-0528-GGUF	ubergarm	2025-05-29T04:10:35Z	0	0	null	[ "mla", "imatrix", "conversational", "ik_llama.cpp", "text-generation", "base_model:deepseek-ai/DeepSeek-R1-0528", "base_model:quantized:deepseek-ai/DeepSeek-R1-0528", "license:mit", "region:us" ]	text-generation	2025-05-29T03:40:22Z	--- quantized_by: ubergarm pipeline_tag: text-generation base_model: deepseek-ai/DeepSeek-R1-0528 license: mit base_model_relation: quantized tags: - mla - imatrix - conversational - ik_llama.cpp --- # WIP Still cooking this imatrix... will upload at least one quant soon™️ 🤞. ## `ik_llama.cpp` imatrix MLA Quantizations of DeepSeek-R1-0528 This quant collection REQUIRES [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp/) fork to support advanced non-linear SotA quants and Multi-Head Latent Attention (MLA). Do not download these big files and expect them to run on mainline vanilla llama.cpp, ollama, LM Studio, KoboldCpp, etc! These quants provide best in class perplexity for the given memory footprint. MLA support allows 32k+ context length in under 24GB GPU VRAM for `R1` and `V3` while offloading MoE layers to RAM. These quants are specifically designed for CPU+GPU systems with about ~24GB VRAM as well as also CPU only rigs using dynamic quant repacking (for maximum memory throughput). If you have more VRAM, I you can now load `_R4` row-interleave repacked quants onto GPUs as of [ik_llama.cpp PR462](https://github.com/ikawrakow/ik_llama.cpp/pull/462). So these quants are good for multi-GPU setups as well now! You could try `ik_llama.cpp` quickly with your existing quants, as it computes MLA tensors and repacks quants on the fly at startup (if you have enough RAM+VRAM to fit entire model). Then come check out these fat quants here once you see the difference. ## Big Thanks Shout out to Wendell and the Level1Techs crew, the community [Forums](https://forum.level1techs.com/t/deepseek-deep-dive-r1-at-home/225826), [YouTube Channel](https://www.youtube.com/@Level1Techs)! BIG thanks for providing BIG hardware expertise and access to run these experiments and make these great quants available to the community!!! Also thanks to all the folks in the quanting and inferencing community here and on `r/LocalLLaMA` for tips and tricks helping each other run all the fun new models! Excited to share and learn together. Thanks! ## Quant Collection So far these are my best recipes offering the lowest perplexity per GiB models suitable for a wide variety of CPU+GPU or CPU only rigs. #### `IQ3_K_R4` 4.??? BPW Special mix `IQ4_KS_R4` `ffn_down` / `IQ3_K_R4` `ffn_(up\|gate)` routed experts with all other layers full `q8_0` for CPU+GPU offload or `--run-time-repack` for max speed CPU only rigs. ## Quick Start #### `ik_llama.cpp` API server for GPU+CPU ```bash # Fits 32k context in under 24GB VRAM # Optional `-ser 6,1` improves speed at minimal cost to quality CUDA_VISIBLE_DEVICES="0," \ ./build/bin/llama-server \ --model /mnt/raid/models/ubergarm/DeepSeek-R1-0528-GGUF/DeepSeek-R1-0528-IQ3_K_R4.gguf \ --alias ubergarm/DeepSeek-R1-0528-IQ3_K_R4 \ --ctx-size 32768 \ -ctk q8_0 \ -mla 3 -fa \ -amb 512 \ -fmoe \ --n-gpu-layers 63 \ --override-tensor exps=CPU \ --parallel 1 \ --threads 16 \ --host 127.0.0.1 \ --port 8080 ``` #### `ik_llama.cpp` API server for MultiGPU(+CPU) ```bash # Adjust number of routed expert layers for additional VRAM on each GPU # Compile with -DGGML_SCHED_MAX_COPIES=1 for multi-GPUs # Compile with -DGGML_CUDA_IQK_FORCE_BF16=1 if putting `_R4` tensors on GPU (for DeepSeek only) ./build/bin/llama-server \ --model /mnt/raid/models/ubergarm/DeepSeek-R1-0528-GGUF/DeepSeek-R1-0528-IQ3_K_R4.gguf \ --alias ubergarm/DeepSeek-R1-0528-IQ3_K_R4 \ --ctx-size 32768 \ -ctk q8_0 \ -mla 3 -fa \ -amb 512 \ -fmoe \ --n-gpu-layers 63 \ -ts 24,24 \ -ot "blk\.(3\|4)\.ffn_.=CUDA0" \ -ot "blk\.(5\|6)\.ffn_.=CUDA1" \ --override-tensor exps=CPU \ --parallel 1 \ --threads 16 \ --host 127.0.0.1 \ --port 8080 ``` #### `ik_llama.cpp` API server for CPU only ``` # The goal for now is as much RAM bandwidth in a single NUMA node e.g. # Use BIOS `NPS0` on AMD Epyc or single socket of Intel Xeon in BIOS `SNC=Disable` & Snoop Interleave # Tune your `--threads` for token generation, and `--threads-batch` for prompt processing (prefill) # Note `--run-time-repack` will pre-allocate enough RAM for model weights instead of mmap()'ing off disk # Note there are options for both Explicit and Transparent Huge Pages with tuning discussions in [git repo](https://github.com/ikawrakow/ik_llama.cpp/pull/278#issuecomment-2746381515) numactl -N 0 -m 0 \ ./build/bin/llama-server \ --model /mnt/raid/models/ubergarm/DeepSeek-R1-0528-GGUF/DeepSeek-R1-0528-IQ3_K_R4.gguf \ --alias ubergarm/DeepSeek-R1-0528-IQ3_K_R4 \ --run-time-repack \ --ctx-size 65536 \ -ctk q8_0 \ -mla 3 -fa \ -amb 512 \ -fmoe \ --parallel 1 \ --threads 88 \ --threads-batch 128 \ --numa numactl \ --host 127.0.0.1 \ --port 8080 ``` ## Quant Comparisons Check out [The Great Quant Wars of 2025](https://www.reddit.com/r/LocalLLaMA/comments/1khwxal/the_great_quant_wars_of_2025/) r/LocalLLaMA post for some more discussion on quantization and methodology. #### imatrix <details> <summary>Importance Matrix Details Here</summary> I didn't have access to a big RAM rig fast enough so rawdogg'd a `Q8_0` attention/dense layers/shared experts and `IQ4_KS_R4` all routed expert layers as that is about all I could fit into RAM+VRAM. I used that cold turkey to make the imatrix in this repo and used for these quants. Not great, not terrible. Just cuz I was feelin' fucky I went with 2k context despite no clear evidence imo of there being any significant measurable benifit. Call me superstitious, but I also threw in some extra material in the imatrix corpus from: [turboderp-org/exllamav3](https://github.com/turboderp-org/exllamav3/tree/master/exllamav3/conversion/standard_cal_data)'s `standard_cal_data` in addition to my usual `imatrix calibration_data_v5_rc.txt` linked below. ```bash cat calibration_data_v5_rc.txt > ubergarm-imatrix-calibration-corpus.txt cat c4.utf8 >> ubergarm-imatrix-calibration-corpus.txt cat code.utf8 >> ubergarm-imatrix-calibration-corpus.txt cat multilingual.utf8 >> ubergarm-imatrix-calibration-corpus.txt cat technical.utf8 >> ubergarm-imatrix-calibration-corpus.txt cat tiny.utf8 >> ubergarm-imatrix-calibration-corpus.txt # Do not use the wiki.utf8 to avoid potential over-fitting on wiki.test.raw common test corpus ./build/bin/llama-imatrix \ --verbosity 1 \ --layer-similarity \ -m /mnt/raid/models/ubergarm/DeepSeek-R1-0528-GGUF/DeepSeek-R1-0528-IQ4_KS_R4.gguf \ -f ubergarm-imatrix-calibration-corpus.txt \ -o /mnt/raid/models/ubergarm/DeepSeek-R1-0528-GGUF/imatrix-DeepSeek-R1-0528-IQ4_KS_R4-mixed-calibration-corpus-2k-ctx-size.dat \ --ctx-size 2048 \ -ctk q8_0 \ -mla 3 -fa \ -amb 512 \ -fmoe \ --n-gpu-layers 63 \ -ts 48,48 \ -ot "blk\.(3\|4\|5\|6\|7\|8)\.ffn_.=CUDA0" \ -ot "blk\.(9\|10\|11\|12\|13\|14)\.ffn_.=CUDA1" \ -ot exps=CPU \ --threads 24 ``` </details> #### Quant Cookers Secret Recipe <details> <summary>Secret Recipe</summary> ```bash #!/usr/bin/env bash custom=" # Token embedding and output tensors (GPU) token_embd\.weight=q8_0 output\.weight=q8_0 output_norm\.weight=q8_0 # First 3 dense layers (0-3) (GPU) blk\.[0-2]\..=q8_0 # All attention, weights, and bias tensors for MoE layers (3-60) (GPU) blk\.[3-9]\.attn_.=q8_0 blk\.[1-5][0-9]\.attn_.=q8_0 blk\.60\.attn_.=q8_0 blk\.[3-9]\.ffn_norm\.weight=q8_0 blk\.[1-5][0-9]\.ffn_norm\.weight=q8_0 blk\.60\.ffn_norm\.weight=q8_0 blk\.[3-9]\.exp_probs_b\.bias=q8_0 blk\.[1-5][0-9]\.exp_probs_b\.bias=q8_0 blk\.60\.exp_probs_b\.bias=q8_0 # Shared Experts (3-60) (GPU) blk\.[3-9]\.ffn_down_shexp\.weight=q8_0 blk\.[1-5][0-9]\.ffn_down_shexp\.weight=q8_0 blk\.60\.ffn_down_shexp\.weight=q8_0 blk\.[3-9]\.ffn_(gate\|up)_shexp\.weight=q8_0 blk\.[1-5][0-9]\.ffn_(gate\|up)_shexp\.weight=q8_0 blk\.60\.ffn_(gate\|up)_shexp\.weight=q8_0 # MoE Experts (3-60) (CPU) blk\.[3-9]\.ffn_down_exps\.weight=iq4_ks_r4 blk\.[1-5][0-9]\.ffn_down_exps\.weight=iq4_ks_r4 blk\.60\.ffn_down_exps\.weight=iq4_ks_r4 blk\.[3-9]\.ffn_(gate\|up)_exps\.weight=iq3_k_r4 blk\.[1-5][0-9]\.ffn_(gate\|up)_exps\.weight=iq3_k_r4 blk\.60\.ffn_(gate\|up)_exps\.weight=iq3_k_r4 " custom=$( echo "$custom" \| grep -v '^#' \| \ sed -Ez 's:\n+:,:g;s:,$::;s:^,::' ) ./build/bin/llama-quantize \ --custom-q "$custom" \ --imatrix /mnt/raid/models/ubergarm/DeepSeek-R1-0528-GGUF/imatrix-DeepSeek-R1-0528-IQ4_KS_R4-mixed-calibration-corpus-2k-ctx-size.dat \ /mnt/raid/models/ubergarm/DeepSeek-R1-0528-GGUF/DeepSeek-R1-256x21B-0528-BF16-00001-of-00030.gguf \ /mnt/raid/models/ubergarm/DeepSeek-R1-0528-GGUF/DeepSeek-R1-0528-IQ3_K_R4.gguf \ IQ3_K_R4 \ 24 ``` </details> #### Perplexity <details> <summary>Perplexity Logs</summary> ```bash $ CUDA_VISIBLE_DEVICES="0," \ ./build/bin/llama-perplexity \ --model /mnt/raid/models/ubergarm/DeepSeek-R1-0528-GGUF/DeepSeek-R1-0528-IQ3_K_R4.gguf \ -ctk q8_0 \ -mla 3 -fa \ -amb 512 \ -fmoe \ --ctx-size 512 \ --ubatch-size 512 \ -f wiki.test.raw \ --seed 1337 \ --n-gpu-layers 63 \ --override-tensor exps=CPU \ --threads 24 echo TODO ``` </details> #### Split <details> <summary>Split GGUF</summary> TODO: Add key value metadata information before publishing. ```bash $ ./build/bin/llama-gguf-split \ --dry-run \ --split \ --split-max-size 50G \ /mnt/raid/models/ubergarm/DeepSeek-R1-0528-GGUF/DeepSeek-R1-0528-IQ3_K_R4.gguf /mnt/raid/hf/DeepSeek-R1-0528-GGUF/DeepSeek-R1-0528-IQ3_K_R4/DeepSeek-R1-0528-IQ3_K_R4 ``` </details> ## References * [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp/) * [ik_llama.cpp Getting Started Guide](https://github.com/ikawrakow/ik_llama.cpp/discussions/258) * [turboderp-org/exllamav3](https://github.com/turboderp-org/exllamav3/pull/26) * [imatrix calibration_data_v5_rc.txt](https://gist.github.com/tristandruyen/9e207a95c7d75ddf37525d353e00659c#file-calibration_data_v5_rc-txt)
Lasorco/botsu	Lasorco	2025-05-29T04:03:02Z	0	1	null	[ "stable-diffusion", "text-to-image", "safetensors", "ja", "license:other", "region:us" ]	text-to-image	2025-05-28T13:27:36Z	--- tags: - stable-diffusion - text-to-image - safetensors language: - ja license: other license_name: fair-ai-public-license-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ --- # ボツマージここにあるものは何らかの理由で没になったものです<br> 調整半ばで終わっているので粗も残っています<br> ## Nonnette_v071DF_k1 Twitterで作例をあげていた中で使ってみたいとの要望があったもの<br> 各種実験やvpredモデルを無理無理マージしているので色転びしやすい上にそれが安定しません<br> <br> レシピ<br> Nonnette_v071DF_k1 = nonnette_kiwi_002 + kittenTower_v20VPred<br> alpha 0.3,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.6,0.0,0.0,1.0,0.0,1.0,0.0,0.4,0.6,0.0<br> <br> nonnette_kiwi_002 = nonnette_kiwi000 + nonnette_kiwi_001_mat<br> alpha 0.2,0.0,0.0,0.0,0.0,0.2,0.2,0.1,0.4,0.3,0.5,0.999,0.9,0.9,0.999,0.4,0.4,0.999,0.0,0.0<br> <br> nonnette_kiwi000 = kiwimixXL_v3 + ntr40obs31lm20kai_nullpo<br> alpha 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8,0.0,0.0,0.0,0.0,0.0,0.0,0.0<br> <br> ntr40obs31lm20_nullpo = ntr40obs31lm20kai + noolpoAI_vpred_test0.4test3<br> alpha 0.5,0.0,0.7,0.75,1.0,0.75,1.0,1.0,1.0,0.25,0.5,0.1,0.75,0.5,0.5,0.1,0.0,0.0,0.75,0.0<br> <br> ntr40obs31lm20kai = ntr40obs31 + ntr40obs31lm20<br> alpha 0.0,0.5,0.15,0.75,0.5,0.7,0.65,0.5,0.4,0.3,0.3,0.4,0.9,0.7,0.4,0.6,0.8,0.7,0.2,0.7<br> <br> ntr40obs31lm20 = ntr40obs31 + lametta Lora<br> alpha 1.0<br> <br> ntr40obs31 = ntrMIXIllustriousXL_v40 + obsessionIllustrious_v31<br> alpha 0.542<br> <br> nonnette_kiwi_001_mat = nonnette_kiwi000 + (nonnette_v020L - matureRitualNoobai_v011)<br> alpha 1<br> <br> nonnette_v020L = nonnette_v020 + lametta Lora<br> alpha 1<br> <br> <br><br> ## Nonnette_v071DF_k2 上のNonnette_v071DF_k1のkittenTower_v20VPredをkittenTower_v10EpsPredに取り替えてマージし直したもの<br> マージ素材としてマージしたのでBASEとかは別のモデルと取り替えたほうが良いかもしれません<br> レシピ<br> Nonnette_v071DF_k2 = nonnette_kiwi_002.fp16 + kittenTower_v10EpsPred<br> alpha 0.0,0.9,0.6,0.0,0.9,0.0,0.0,0.9,0.4,0.6,0.2,0.1,0.25,0.25,0.25,0.25,0.25,0.25,0.25,0.3<br>
svjack/hakoniwa_anime_wan2_1_models	svjack	2025-05-29T03:58:35Z	0	3	null	[ "region:us" ]	null	2025-05-28T08:38:04Z	# hakoniwa_anime_wan2_1_models - drived from https://civitai.com/models/1626197 * anime_wanvideo_T2V_example_02.json - prompt ```txt anime style ,high quality nature video featuring a red panda balancing on a bamboo stem while a bird lands on it's head, on the background there is a waterfall ``` - output <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/634dffc49b777beec3bc6448/79SWnULz3splo2NchGnY-.mp4"></video> * anime_wanvideo_480p_I2V_example_02.json - Image ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/634dffc49b777beec3bc6448/K_rXTIWRDjtlXUlunLpZf.jpeg) - prompt ```txt anime style, portrays a serene anime-style scene with a tranquil yet slightly melancholic atmosphere. In the lower right corner, a young man with dark blue hair stands calmly, dressed in a long blue coat layered over a black turtleneck. His gaze is directed off to the side, adding a contemplative mood. ``` - out <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/634dffc49b777beec3bc6448/dlbXRGcPxXP0FaOT-cGQg.mp4"></video> ]()
HiveMindSystems/swarm-worker-v1	HiveMindSystems	2025-05-29T03:58:01Z	0	0	null	[ "region:us" ]	null	2025-05-29T03:56:22Z	# SwarmAI - Worker Agent v1 This is a minimal SwarmAI worker agent designed to process incoming signals and respond based on a primitive logic structure. ## Behavior - If the text contains "data" → processes data. - If it contains "order" → executes command. - Otherwise → idle. ## Vision Workers are modular, task-focused agents that can evolve, mutate, and specialize over time within the SwarmAI ecosystem. ## Usage ```bash python worker_agent.py ``` ## License MIT
mradermacher/Grand-Horror-25B-Godzillas-Wicked-Sister-GGUF	mradermacher	2025-05-29T03:57:42Z	38	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama3", "llama-3", "en", "base_model:DavidAU/Grand-Horror-25B-Godzillas-Wicked-Sister", "base_model:quantized:DavidAU/Grand-Horror-25B-Godzillas-Wicked-Sister", "endpoints_compatible", "region:us", "conversational" ]	null	2024-08-26T19:38:22Z	--- base_model: DavidAU/Grand-Horror-25B-Godzillas-Wicked-Sister language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge - llama3 - llama-3 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/DavidAU/Grand-Horror-25B-Godzillas-Wicked-Sister <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Grand-Horror-25B-Godzillas-Wicked-Sister-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Grand-Horror-25B-Godzillas-Wicked-Sister-GGUF/resolve/main/Grand-Horror-25B-Godzillas-Wicked-Sister.Q2_K.gguf) \| Q2_K \| 9.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Grand-Horror-25B-Godzillas-Wicked-Sister-GGUF/resolve/main/Grand-Horror-25B-Godzillas-Wicked-Sister.IQ3_XS.gguf) \| IQ3_XS \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Grand-Horror-25B-Godzillas-Wicked-Sister-GGUF/resolve/main/Grand-Horror-25B-Godzillas-Wicked-Sister.Q3_K_S.gguf) \| Q3_K_S \| 11.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Grand-Horror-25B-Godzillas-Wicked-Sister-GGUF/resolve/main/Grand-Horror-25B-Godzillas-Wicked-Sister.IQ3_S.gguf) \| IQ3_S \| 11.1 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Grand-Horror-25B-Godzillas-Wicked-Sister-GGUF/resolve/main/Grand-Horror-25B-Godzillas-Wicked-Sister.IQ3_M.gguf) \| IQ3_M \| 11.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Grand-Horror-25B-Godzillas-Wicked-Sister-GGUF/resolve/main/Grand-Horror-25B-Godzillas-Wicked-Sister.Q3_K_M.gguf) \| Q3_K_M \| 12.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Grand-Horror-25B-Godzillas-Wicked-Sister-GGUF/resolve/main/Grand-Horror-25B-Godzillas-Wicked-Sister.Q3_K_L.gguf) \| Q3_K_L \| 13.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Grand-Horror-25B-Godzillas-Wicked-Sister-GGUF/resolve/main/Grand-Horror-25B-Godzillas-Wicked-Sister.IQ4_XS.gguf) \| IQ4_XS \| 13.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Grand-Horror-25B-Godzillas-Wicked-Sister-GGUF/resolve/main/Grand-Horror-25B-Godzillas-Wicked-Sister.Q4_K_S.gguf) \| Q4_K_S \| 14.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Grand-Horror-25B-Godzillas-Wicked-Sister-GGUF/resolve/main/Grand-Horror-25B-Godzillas-Wicked-Sister.Q4_K_M.gguf) \| Q4_K_M \| 15.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Grand-Horror-25B-Godzillas-Wicked-Sister-GGUF/resolve/main/Grand-Horror-25B-Godzillas-Wicked-Sister.Q5_K_S.gguf) \| Q5_K_S \| 17.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Grand-Horror-25B-Godzillas-Wicked-Sister-GGUF/resolve/main/Grand-Horror-25B-Godzillas-Wicked-Sister.Q5_K_M.gguf) \| Q5_K_M \| 17.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Grand-Horror-25B-Godzillas-Wicked-Sister-GGUF/resolve/main/Grand-Horror-25B-Godzillas-Wicked-Sister.Q6_K.gguf) \| Q6_K \| 20.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Grand-Horror-25B-Godzillas-Wicked-Sister-GGUF/resolve/main/Grand-Horror-25B-Godzillas-Wicked-Sister.Q8_0.gguf) \| Q8_0 \| 26.7 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Cloudmaster/Llama-3.2-3B-HF-hqq-06	Cloudmaster	2025-05-29T03:51:24Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "hqq", "region:us" ]	text-generation	2025-05-29T03:47:51Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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second-state/SmolVLM2-256M-Video-Instruct-GGUF	second-state	2025-05-29T03:40:46Z	0	0	transformers	[ "transformers", "gguf", "smolvlm", "image-text-to-text", "en", "base_model:HuggingFaceTB/SmolVLM2-256M-Video-Instruct", "base_model:quantized:HuggingFaceTB/SmolVLM2-256M-Video-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	image-text-to-text	2025-05-29T03:31:48Z	--- base_model: HuggingFaceTB/SmolVLM2-256M-Video-Instruct library_name: transformers license: apache-2.0 model_creator: HuggingFaceTB model_name: SmolVLM2-256M-Video-Instruct quantized_by: Second State Inc. pipeline_tag: image-text-to-text language: - en --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # SmolVLM2-256M-Video-Instruct-GGUF ## Original Model [HuggingFaceTB/SmolVLM2-256M-Video-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM2-256M-Video-Instruct) ## Run with LlamaEdge - LlamaEdge version: [v0.21.0](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.21.0) and above - Prompt template - Prompt type: `smol-vision` - Prompt string ```text <\|im_start\|> User: {user_message_1}<image> Assistant: {assistant_message_1} User: {user_message_2}<image> Assistant: ``` - Context size: `2048` - Run as LlamaEdge service ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:SmolVLM2-256M-Video-Instruct-Q5_K_M.gguf \ llama-api-server.wasm \ --prompt-template smol-vision \ --llava-mmproj SmolVLM2-256M-Video-Instruct-mmproj-f16.gguf \ --model-name SmolVLM2-256M-Video-Instruct \ --ctx-size 2048 ``` ## Quantized GGUF Models \| Name \| Quant method \| Bits \| Size \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [SmolVLM2-256M-Video-Instruct-Q2_K.gguf](https://huggingface.co/second-state/SmolVLM2-256M-Video-Instruct-GGUF/blob/main/SmolVLM2-256M-Video-Instruct-Q2_K.gguf) \| Q2_K \| 2 \| 104 MB\| smallest, significant quality loss - not recommended for most purposes \| \| [SmolVLM2-256M-Video-Instruct-Q3_K_L.gguf](https://huggingface.co/second-state/SmolVLM2-256M-Video-Instruct-GGUF/blob/main/SmolVLM2-256M-Video-Instruct-Q3_K_L.gguf) \| Q3_K_L \| 3 \| 114 MB\| small, substantial quality loss \| \| [SmolVLM2-256M-Video-Instruct-Q3_K_M.gguf](https://huggingface.co/second-state/SmolVLM2-256M-Video-Instruct-GGUF/blob/main/SmolVLM2-256M-Video-Instruct-Q3_K_M.gguf) \| Q3_K_M \| 3 \| 110 MB\| very small, high quality loss \| \| [SmolVLM2-256M-Video-Instruct-Q3_K_S.gguf](https://huggingface.co/second-state/SmolVLM2-256M-Video-Instruct-GGUF/blob/main/SmolVLM2-256M-Video-Instruct-Q3_K_S.gguf) \| Q3_K_S \| 3 \| 104 MB\| very small, high quality loss \| \| [SmolVLM2-256M-Video-Instruct-Q4_0.gguf](https://huggingface.co/second-state/SmolVLM2-256M-Video-Instruct-GGUF/blob/main/SmolVLM2-256M-Video-Instruct-Q4_0.gguf) \| Q4_0 \| 4 \| 108 MB\| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [SmolVLM2-256M-Video-Instruct-Q4_K_M.gguf](https://huggingface.co/second-state/SmolVLM2-256M-Video-Instruct-GGUF/blob/main/SmolVLM2-256M-Video-Instruct-Q4_K_M.gguf) \| Q4_K_M \| 4 \| 125 MB\| medium, balanced quality - recommended \| \| [SmolVLM2-256M-Video-Instruct-Q4_K_S.gguf](https://huggingface.co/second-state/SmolVLM2-256M-Video-Instruct-GGUF/blob/main/SmolVLM2-256M-Video-Instruct-Q4_K_S.gguf) \| Q4_K_S \| 4 \| 122 MB\| small, greater quality loss \| \| [SmolVLM2-256M-Video-Instruct-Q5_0.gguf](https://huggingface.co/second-state/SmolVLM2-256M-Video-Instruct-GGUF/blob/main/SmolVLM2-256M-Video-Instruct-Q5_0.gguf) \| Q5_0 \| 5 \| 125 MB\| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [SmolVLM2-256M-Video-Instruct-Q5_K_M.gguf](https://huggingface.co/second-state/SmolVLM2-256M-Video-Instruct-GGUF/blob/main/SmolVLM2-256M-Video-Instruct-Q5_K_M.gguf) \| Q5_K_M \| 5 \| 133 MB\| large, very low quality loss - recommended \| \| [SmolVLM2-256M-Video-Instruct-Q5_K_S.gguf](https://huggingface.co/second-state/SmolVLM2-256M-Video-Instruct-GGUF/blob/main/SmolVLM2-256M-Video-Instruct-Q5_K_S.gguf) \| Q5_K_S \| 5 \| 131 MB\| large, low quality loss - recommended \| \| [SmolVLM2-256M-Video-Instruct-Q6_K.gguf](https://huggingface.co/second-state/SmolVLM2-256M-Video-Instruct-GGUF/blob/main/SmolVLM2-256M-Video-Instruct-Q6_K.gguf) \| Q6_K \| 6 \| 169 MB\| very large, extremely low quality loss \| \| [SmolVLM2-256M-Video-Instruct-Q8_0.gguf](https://huggingface.co/second-state/SmolVLM2-256M-Video-Instruct-GGUF/blob/main/SmolVLM2-256M-Video-Instruct-Q8_0.gguf) \| Q8_0 \| 8 \| 175 MB\| very large, extremely low quality loss - not recommended \| \| [SmolVLM2-256M-Video-Instruct-f16.gguf](https://huggingface.co/second-state/SmolVLM2-256M-Video-Instruct-GGUF/blob/main/SmolVLM2-256M-Video-Instruct-f16.gguf) \| f16 \| 16 \| 328 MB\| \| \| [SmolVLM2-256M-Video-Instruct-mmproj-f16.gguf](https://huggingface.co/second-state/SmolVLM2-256M-Video-Instruct-GGUF/blob/main/SmolVLM2-256M-Video-Instruct-mmproj-f16.gguf) \| f16 \| 16 \| 190 MB\| \| Quantized with llama.cpp b5501
2yunadaaa/llama3.1-8b-3kingdoms-instruct-16bit	2yunadaaa	2025-05-29T03:40:09Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/Meta-Llama-3.1-8B", "base_model:finetune:unsloth/Meta-Llama-3.1-8B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-29T03:33:35Z	--- base_model: unsloth/Meta-Llama-3.1-8B tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: 2yunadaaa - License: apache-2.0 - Finetuned from model : unsloth/Meta-Llama-3.1-8B 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)
salma-remyx/llava-1.5-7b-hf-instruct-trl-sft-spacellava_openspaces_3epoch_a256_r128	salma-remyx	2025-05-29T03:36:09Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:llava-hf/llava-1.5-7b-hf", "base_model:finetune:llava-hf/llava-1.5-7b-hf", "endpoints_compatible", "region:us" ]	null	2025-05-20T15:51:42Z	--- base_model: llava-hf/llava-1.5-7b-hf library_name: transformers model_name: llava-1.5-7b-hf-instruct-trl-sft-spacellava_openspaces_3epoch_a256_r128 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for llava-1.5-7b-hf-instruct-trl-sft-spacellava_openspaces_3epoch_a256_r128 This model is a fine-tuned version of [llava-hf/llava-1.5-7b-hf](https://huggingface.co/llava-hf/llava-1.5-7b-hf). 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="salma-remyx/llava-1.5-7b-hf-instruct-trl-sft-spacellava_openspaces_3epoch_a256_r128", 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/smellslikeml/llava-1.5-7b-hf-instruct-trl-sft-spacellava_openspaces/runs/y13tzuom) This model was trained with SFT. ### Framework versions - TRL: 0.13.0.dev0 - Transformers: 4.51.3 - Pytorch: 2.5.1+cu121 - Datasets: 3.1.0 - 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}} } ```
Novigrad/dqn-SpaceInvadersNoFrameskip-v4	Novigrad	2025-05-29T03:30:27Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-05-29T03:29:48Z	--- 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: 739.50 +/- 203.27 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 Novigrad -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 Novigrad -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 Novigrad ``` ## 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', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
mradermacher/Gemma-The-Writer-9B-GGUF	mradermacher	2025-05-29T03:20:51Z	64	2	transformers	[ "transformers", "gguf", "mergekit", "merge", "gemma2", "en", "base_model:DavidAU/Gemma-The-Writer-9B", "base_model:quantized:DavidAU/Gemma-The-Writer-9B", "endpoints_compatible", "region:us", "conversational" ]	null	2024-09-26T23:14:58Z	--- base_model: DavidAU/Gemma-The-Writer-9B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge - gemma2 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/DavidAU/Gemma-The-Writer-9B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Gemma-The-Writer-9B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-9B-GGUF/resolve/main/Gemma-The-Writer-9B.Q2_K.gguf) \| Q2_K \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-9B-GGUF/resolve/main/Gemma-The-Writer-9B.IQ3_XS.gguf) \| IQ3_XS \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-9B-GGUF/resolve/main/Gemma-The-Writer-9B.IQ3_S.gguf) \| IQ3_S \| 4.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-9B-GGUF/resolve/main/Gemma-The-Writer-9B.Q3_K_S.gguf) \| Q3_K_S \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-9B-GGUF/resolve/main/Gemma-The-Writer-9B.IQ3_M.gguf) \| IQ3_M \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-9B-GGUF/resolve/main/Gemma-The-Writer-9B.Q3_K_M.gguf) \| Q3_K_M \| 4.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-9B-GGUF/resolve/main/Gemma-The-Writer-9B.Q3_K_L.gguf) \| Q3_K_L \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-9B-GGUF/resolve/main/Gemma-The-Writer-9B.IQ4_XS.gguf) \| IQ4_XS \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-9B-GGUF/resolve/main/Gemma-The-Writer-9B.Q4_K_S.gguf) \| Q4_K_S \| 5.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-9B-GGUF/resolve/main/Gemma-The-Writer-9B.Q4_K_M.gguf) \| Q4_K_M \| 5.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-9B-GGUF/resolve/main/Gemma-The-Writer-9B.Q5_K_S.gguf) \| Q5_K_S \| 6.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-9B-GGUF/resolve/main/Gemma-The-Writer-9B.Q5_K_M.gguf) \| Q5_K_M \| 6.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-9B-GGUF/resolve/main/Gemma-The-Writer-9B.Q6_K.gguf) \| Q6_K \| 7.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-9B-GGUF/resolve/main/Gemma-The-Writer-9B.Q8_0.gguf) \| Q8_0 \| 9.9 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-9B-GGUF/resolve/main/Gemma-The-Writer-9B.f16.gguf) \| f16 \| 18.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Aidana2007/SportBot	Aidana2007	2025-05-29T03:20:16Z	6	0	transformers	[ "transformers", "safetensors", "gguf", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-23T19:30:05Z	--- 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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mradermacher/L3-Darker-Planet-12.15B-i1-GGUF	mradermacher	2025-05-29T03:19:06Z	1	2	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-3", "en", "base_model:DavidAU/L3-Darker-Planet-12.15B", "base_model:quantized:DavidAU/L3-Darker-Planet-12.15B", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-09-28T04:07:38Z	--- base_model: DavidAU/L3-Darker-Planet-12.15B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge - llama-3 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/DavidAU/L3-Darker-Planet-12.15B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/L3-Darker-Planet-12.15B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/L3-Darker-Planet-12.15B-i1-GGUF/resolve/main/L3-Darker-Planet-12.15B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 3.0 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/L3-Darker-Planet-12.15B-i1-GGUF/resolve/main/L3-Darker-Planet-12.15B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 3.2 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/L3-Darker-Planet-12.15B-i1-GGUF/resolve/main/L3-Darker-Planet-12.15B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-Darker-Planet-12.15B-i1-GGUF/resolve/main/L3-Darker-Planet-12.15B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-Darker-Planet-12.15B-i1-GGUF/resolve/main/L3-Darker-Planet-12.15B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-Darker-Planet-12.15B-i1-GGUF/resolve/main/L3-Darker-Planet-12.15B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-Darker-Planet-12.15B-i1-GGUF/resolve/main/L3-Darker-Planet-12.15B.i1-Q2_K.gguf) \| i1-Q2_K \| 4.8 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3-Darker-Planet-12.15B-i1-GGUF/resolve/main/L3-Darker-Planet-12.15B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 5.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-Darker-Planet-12.15B-i1-GGUF/resolve/main/L3-Darker-Planet-12.15B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-Darker-Planet-12.15B-i1-GGUF/resolve/main/L3-Darker-Planet-12.15B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 5.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3-Darker-Planet-12.15B-i1-GGUF/resolve/main/L3-Darker-Planet-12.15B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 5.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/L3-Darker-Planet-12.15B-i1-GGUF/resolve/main/L3-Darker-Planet-12.15B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-Darker-Planet-12.15B-i1-GGUF/resolve/main/L3-Darker-Planet-12.15B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 6.1 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3-Darker-Planet-12.15B-i1-GGUF/resolve/main/L3-Darker-Planet-12.15B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 6.6 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3-Darker-Planet-12.15B-i1-GGUF/resolve/main/L3-Darker-Planet-12.15B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-Darker-Planet-12.15B-i1-GGUF/resolve/main/L3-Darker-Planet-12.15B.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 7.1 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-Darker-Planet-12.15B-i1-GGUF/resolve/main/L3-Darker-Planet-12.15B.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 7.1 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-Darker-Planet-12.15B-i1-GGUF/resolve/main/L3-Darker-Planet-12.15B.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 7.1 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-Darker-Planet-12.15B-i1-GGUF/resolve/main/L3-Darker-Planet-12.15B.i1-Q4_0.gguf) \| i1-Q4_0 \| 7.1 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-Darker-Planet-12.15B-i1-GGUF/resolve/main/L3-Darker-Planet-12.15B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 7.1 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-Darker-Planet-12.15B-i1-GGUF/resolve/main/L3-Darker-Planet-12.15B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 7.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/L3-Darker-Planet-12.15B-i1-GGUF/resolve/main/L3-Darker-Planet-12.15B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 8.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-Darker-Planet-12.15B-i1-GGUF/resolve/main/L3-Darker-Planet-12.15B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 8.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-Darker-Planet-12.15B-i1-GGUF/resolve/main/L3-Darker-Planet-12.15B.i1-Q6_K.gguf) \| i1-Q6_K \| 10.1 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
zjrwtxtechstudio/yijian_next_full01model	zjrwtxtechstudio	2025-05-29T03:18:12Z	0	0	null	[ "pytorch", "qwen3", "unsloth", "license:apache-2.0", "region:us" ]	null	2025-05-29T03:13:47Z	--- license: apache-2.0 tags: - unsloth ---
moonshotai/Kimi-Audio-7B-Instruct	moonshotai	2025-05-29T03:01:58Z	3,744	315	kimi-audio	[ "kimi-audio", "safetensors", "audio", "audio-language-model", "speech-recognition", "audio-understanding", "text-to-speech", "audio-generation", "chat", "custom_code", "en", "zh", "license:mit", "region:us" ]	text-to-speech	2025-04-25T08:05:10Z	--- license: mit language: - en - zh tags: - audio - audio-language-model - speech-recognition - audio-understanding - text-to-speech - audio-generation - chat library_name: kimi-audio --- # Kimi-Audio <p align="center"> <img src="https://raw.githubusercontent.com/MoonshotAI/Kimi-Audio/master/assets/kimia_logo.png" width="400"/> <!-- TODO: Replace with actual raw image URL from your repo --> <p> <p align="center"> <a href="https://huggingface.co/moonshotai/Kimi-Audio-7B">🤗 Kimi-Audio-7B</a>  \| <a href="https://huggingface.co/moonshotai/Kimi-Audio-7B-Instruct">🤗 Kimi-Audio-7B-Instruct </a>  \| <a href="https://raw.githubusercontent.com/MoonshotAI/Kimi-Audio/master/assets/kimia_report.pdf">📑 Paper</a> </p> ## Introduction We present Kimi-Audio, an open-source audio foundation model excelling in audio understanding, generation, and conversation. This repository hosts the model checkpoints for Kimi-Audio-7B-Instruct. Kimi-Audio is designed as a universal audio foundation model capable of handling a wide variety of audio processing tasks within a single unified framework. Key features include: * Universal Capabilities: Handles diverse tasks like speech recognition (ASR), audio question answering (AQA), audio captioning (AAC), speech emotion recognition (SER), sound event/scene classification (SEC/ASC) and end-to-end speech conversation. * State-of-the-Art Performance: Achieves SOTA results on numerous audio benchmarks (see our [Technical Report](https://raw.githubusercontent.com/MoonshotAI/Kimi-Audio/master/assets/kimia_report.pdf)). * Large-Scale Pre-training: Pre-trained on over 13 million hours of diverse audio data (speech, music, sounds) and text data. * Novel Architecture: Employs a hybrid audio input (continuous acoustic + discrete semantic tokens) and an LLM core with parallel heads for text and audio token generation. * Efficient Inference: Features a chunk-wise streaming detokenizer based on flow matching for low-latency audio generation. For more details, please refer to our [GitHub Repository](https://github.com/MoonshotAI/Kimi-Audio) and [Technical Report](https://raw.githubusercontent.com/MoonshotAI/Kimi-Audio/master/assets/kimia_report.pdf). ## Requirements We recommend that you build a Docker image to run the inference. After cloning the inference code, you can construct the image using the `docker build` command. ```bash git clone https://github.com/MoonshotAI/Kimi-Audio git submodule update --init cd Kimi-Audio docker build -t kimi-audio:v0.1 . ``` Alternatively, You can also use our pre-built image: ```bash docker pull moonshotai/kimi-audio:v0.1 ``` Or, you can install requirments by: ```bash pip install -r requirements.txt ``` You may refer to the Dockerfile in case of any environment issues. ## Quickstart This example demonstrates basic usage for generating text from audio (ASR) and generating both text and speech in a conversational turn using the `Kimi-Audio-7B-Instruct` model. ```python import soundfile as sf # Assuming the KimiAudio class is available after installation from kimia_infer.api.kimia import KimiAudio import torch # Ensure torch is imported if needed for device placement # --- 1. Load Model --- # Load the model from Hugging Face Hub # Make sure you are logged in (`huggingface-cli login`) if the repo is private. model_id = "moonshotai/Kimi-Audio-7B-Instruct" # Or "Kimi/Kimi-Audio-7B" device = "cuda" if torch.cuda.is_available() else "cpu" # Example device placement # Note: The KimiAudio class might handle model loading differently. # You might need to pass the model_id directly or download checkpoints manually # and provide the local path as shown in the original readme_kimia.md. # Please refer to the main Kimi-Audio repository for precise loading instructions. # Example assuming KimiAudio takes the HF ID or a local path: try: model = KimiAudio(model_path=model_id, load_detokenizer=True) # May need device argument model.to(device) # Example device placement except Exception as e: print(f"Automatic loading from HF Hub might require specific setup.") print(f"Refer to Kimi-Audio docs. Trying local path example (update path!). Error: {e}") # Fallback example: # model_path = "/path/to/your/downloaded/kimia-hf-ckpt" # IMPORTANT: Update this path if loading locally # model = KimiAudio(model_path=model_path, load_detokenizer=True) # model.to(device) # Example device placement # --- 2. Define Sampling Parameters --- sampling_params = { "audio_temperature": 0.8, "audio_top_k": 10, "text_temperature": 0.0, "text_top_k": 5, "audio_repetition_penalty": 1.0, "audio_repetition_window_size": 64, "text_repetition_penalty": 1.0, "text_repetition_window_size": 16, } # --- 3. Example 1: Audio-to-Text (ASR) --- # TODO: Provide actual example audio files or URLs accessible to users # E.g., download sample files first or use URLs # wget https://path/to/your/asr_example.wav -O asr_example.wav # wget https://path/to/your/qa_example.wav -O qa_example.wav asr_audio_path = "asr_example.wav" # IMPORTANT: Make sure this file exists qa_audio_path = "qa_example.wav" # IMPORTANT: Make sure this file exists messages_asr = [ {"role": "user", "message_type": "text", "content": "Please transcribe the following audio:"}, {"role": "user", "message_type": "audio", "content": asr_audio_path} ] # Generate only text output # Note: Ensure the model object and generate method accept device placement if needed _, text_output = model.generate(messages_asr, sampling_params, output_type="text") print(">>> ASR Output Text: ", text_output) # Expected output: "这并不是告别，这是一个篇章的结束，也是新篇章的开始。" (Example) # --- 4. Example 2: Audio-to-Audio/Text Conversation --- messages_conversation = [ {"role": "user", "message_type": "audio", "content": qa_audio_path} ] # Generate both audio and text output wav_output, text_output = model.generate(messages_conversation, sampling_params, output_type="both") # Save the generated audio output_audio_path = "output_audio.wav" # Ensure wav_output is on CPU and flattened before saving sf.write(output_audio_path, wav_output.detach().cpu().view(-1).numpy(), 24000) # Assuming 24kHz output print(f">>> Conversational Output Audio saved to: {output_audio_path}") print(">>> Conversational Output Text: ", text_output) # Expected output: "A." (Example) print("Kimi-Audio inference examples complete.") ``` ## Citation If you find Kimi-Audio useful in your research or applications, please cite our technical report: ```bibtex @misc{kimi_audio_2024, title={Kimi-Audio Technical Report}, author={Kimi Team}, year={2024}, eprint={arXiv:placeholder}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## License The model is based and modified from [Qwen 2.5-7B](https://github.com/QwenLM/Qwen2.5). Code derived from Qwen2.5-7B is licensed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0). Other parts of the code are licensed under the [MIT License](https://opensource.org/licenses/MIT).
moonshotai/Kimi-Audio-7B	moonshotai	2025-05-29T03:01:50Z	372	41	kimi-audio	[ "kimi-audio", "safetensors", "audio", "audio-language-model", "speech-recognition", "audio-understanding", "text-to-speech", "audio-generation", "chat", "custom_code", "en", "zh", "license:mit", "region:us" ]	text-to-speech	2025-04-25T12:22:40Z	--- license: mit language: - en - zh tags: - audio - audio-language-model - speech-recognition - audio-understanding - text-to-speech - audio-generation - chat library_name: kimi-audio --- # Kimi-Audio <p align="center"> <img src="https://raw.githubusercontent.com/MoonshotAI/Kimi-Audio/master/assets/kimia_logo.png" width="400"/> <p> <p align="center"> <a href="https://huggingface.co/moonshotai/Kimi-Audio-7B">🤗 Kimi-Audio-7B</a> \| <a href="https://huggingface.co/moonshotai/Kimi-Audio-7B-Instruct">🤗 Kimi-Audio-7B-Instruct </a> \| <a href="https://raw.githubusercontent.com/MoonshotAI/Kimi-Audio/master/assets/kimia_report.pdf">📑 Paper</a> </p> ## Introduction We present Kimi-Audio, an open-source audio foundation model excelling in audio understanding, generation, and conversation. This repository hosts the model checkpoints for Kimi-Audio-7B. Kimi-Audio is designed as a universal audio foundation model capable of handling a wide variety of audio processing tasks within a single unified framework. Key features include: * Universal Capabilities: Handles diverse tasks like speech recognition (ASR), audio question answering (AQA), audio captioning (AAC), speech emotion recognition (SER), sound event/scene classification (SEC/ASC) and end-to-end speech conversation. * State-of-the-Art Performance: Achieves SOTA results on numerous audio benchmarks (see our [Technical Report](https://raw.githubusercontent.com/MoonshotAI/Kimi-Audio/master/assets/kimia_report.pdf)). * Large-Scale Pre-training: Pre-trained on over 13 million hours of diverse audio data (speech, music, sounds) and text data. * Novel Architecture: Employs a hybrid audio input (continuous acoustic + discrete semantic tokens) and an LLM core with parallel heads for text and audio token generation. * Efficient Inference: Features a chunk-wise streaming detokenizer based on flow matching for low-latency audio generation. For more details, please refer to our [GitHub Repository](https://github.com/MoonshotAI/Kimi-Audio) and [Technical Report](https://raw.githubusercontent.com/MoonshotAI/Kimi-Audio/master/assets/kimia_report.pdf). ## Note Kimi-Audio-7B is a base model without fine-tuning. So it cannot be used directly. The base model is quite flexible, you can fine-tune it on any possible downstream tasks. If you are looking for an out-of-the-box model, please refer to [Kimi-Audio-7B-Instruct](https://huggingface.co/moonshotai/Kimi-Audio-7B-Instruct). ## Citation If you find Kimi-Audio useful in your research or applications, please cite our technical report: ```bibtex @misc{kimi_audio_2024, title={Kimi-Audio Technical Report}, author={Kimi Team}, year={2024}, eprint={arXiv:placeholder}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## License The model is based and modified from [Qwen 2.5-7B](https://github.com/QwenLM/Qwen2.5). Code derived from Qwen2.5-7B is licensed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0). Other parts of the code are licensed under the [MIT License](https://opensource.org/licenses/MIT).
quatatak/kazroberta-finetuned-pos-halved	quatatak	2025-05-29T03:01:02Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "token-classification", "generated_from_trainer", "base_model:kz-transformers/kaz-roberta-conversational", "base_model:finetune:kz-transformers/kaz-roberta-conversational", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-05-28T20:49:30Z	--- library_name: transformers license: apache-2.0 base_model: kz-transformers/kaz-roberta-conversational tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: kazroberta-finetuned-pos-halved 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. --> # kazroberta-finetuned-pos-halved This model is a fine-tuned version of [kz-transformers/kaz-roberta-conversational](https://huggingface.co/kz-transformers/kaz-roberta-conversational) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0254 - Accuracy: 0.9961 - Precision: 0.9927 - Recall: 0.9921 - F1: 0.9924 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - 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 - lr_scheduler_warmup_steps: 1000 - num_epochs: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.0855 \| 1.0 \| 1238 \| 0.0586 \| 0.9809 \| 0.9600 \| 0.9647 \| 0.9622 \| \| 0.0435 \| 2.0 \| 2476 \| 0.0345 \| 0.9886 \| 0.9745 \| 0.9759 \| 0.9752 \| \| 0.0167 \| 3.0 \| 3714 \| 0.0234 \| 0.9932 \| 0.9863 \| 0.9878 \| 0.9870 \| \| 0.0075 \| 4.0 \| 4952 \| 0.0229 \| 0.9949 \| 0.9902 \| 0.9900 \| 0.9901 \| \| 0.0034 \| 5.0 \| 6190 \| 0.0241 \| 0.9954 \| 0.9910 \| 0.9910 \| 0.9910 \| \| 0.0019 \| 6.0 \| 7428 \| 0.0247 \| 0.9959 \| 0.9928 \| 0.9910 \| 0.9919 \| \| 0.0009 \| 7.0 \| 8666 \| 0.0254 \| 0.9961 \| 0.9927 \| 0.9921 \| 0.9924 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.7.0+cu126 - Datasets 3.5.1 - Tokenizers 0.21.1
fahd200581/AIDEEEBAI	fahd200581	2025-05-29T02:55:31Z	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	2025-05-29T02:10:56Z	--- 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: AIDEEEBAI --- # Aideeebai <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 `AIDEEEBAI` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "AIDEEEBAI", "lora_weights": "https://huggingface.co/fahd200581/AIDEEEBAI/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('fahd200581/AIDEEEBAI', weight_name='lora.safetensors') image = pipeline('AIDEEEBAI').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: 4000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/fahd200581/AIDEEEBAI/discussions) to add images that show off what you’ve made with this LoRA.
songkey/epicphotogasm_ultimateFidelity	songkey	2025-05-29T02:55:14Z	0	0	diffusers	[ "diffusers", "safetensors", "base_model:stable-diffusion-v1-5/stable-diffusion-v1-5", "base_model:finetune:stable-diffusion-v1-5/stable-diffusion-v1-5", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-05-28T12:05:54Z	--- library_name: diffusers license: apache-2.0 base_model: - stable-diffusion-v1-5/stable-diffusion-v1-5 --- Copied from: https://civitai.com/models/132632?modelVersionId=429454
thisisnotabot/my_awesome_IMDb_model	thisisnotabot	2025-05-29T02:40:41Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-29T01:21:04Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: my_awesome_IMDb_model 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. --> # my_awesome_IMDb_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2020 - Accuracy: 0.9300 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2852 \| 1.0 \| 782 \| 0.1936 \| 0.9236 \| \| 0.1412 \| 2.0 \| 1564 \| 0.2020 \| 0.9300 \| ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.0
Zhang199/TinyLLaVA-Qwen2.5-3B-SigLIP	Zhang199	2025-05-29T02:27:29Z	82	0	null	[ "safetensors", "tinyllava", "image-text-to-text", "conversational", "arxiv:2405.11788", "license:apache-2.0", "region:us" ]	image-text-to-text	2024-12-01T01:28:37Z	--- license: apache-2.0 pipeline_tag: image-text-to-text --- <center><span style="font-size:2em;">TinyLLaVA</span></center> [![arXiv](https://img.shields.io/badge/Arxiv-2405.11788-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2405.11788)[![Github](https://img.shields.io/badge/Github-Github-blue.svg)](https://github.com/TinyLLaVA/TinyLLaVA_Factory)[![Demo](https://img.shields.io/badge/Demo-Demo-red.svg)](http://8843843nmph5.vicp.fun/#/) Here, we introduce TinyLLaVA-Qwen2.5-3B-SigLIP , which is trained by the [TinyLLaVA Factory](https://github.com/TinyLLaVA/TinyLLaVA_Factory) codebase. For LLM and vision tower, we choose [Qwen2.5-3B](https://huggingface.co/Qwen/Qwen2.5-3B) and [siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384), respectively. ### Usage Execute the following test code: ```python from tinyllava.eval.run_tiny_llava import eval_model model_path = 'Zhang199/TinyLLaVA-Qwen2.5-3B-SigLIP' prompt = "What are the things I should be cautious about when I visit here?" image_file = "https://llava-vl.github.io/static/images/view.jpg" conv_mode = "phi" # or llama, gemma, etc args = type('Args', (), { "model_path": model_path, "model": None, "query": prompt, "conv_mode": conv_mode, "image_file": image_file, "sep": ",", "temperature": 0, "top_p": None, "num_beams": 1, "max_new_tokens": 512 })() eval_model(args) ``` ### Result \| model_name \| vqav2 \| gqa \| sqa \| textvqa \| MM-VET \| POPE \| MME \| MMMU \| \| :----------------------------------------------------------: \| ----- \| ------- \| ----- \| ----- \| ------- \| ----- \| ------ \| ------ \| \| [LLaVA-1.5-7B](https://huggingface.co/llava-hf/llava-1.5-7b-hf) \| 78.5 \| 62.0 \| 66.8 \| 58.2 \| 30.5 \| 85.9 \| 1510.7 \| - \| \| [bczhou/TinyLLaVA-3.1B](https://huggingface.co/bczhou/TinyLLaVA-3.1B) (our legacy model) \| 79.9 \| 62.0 \| 69.1 \| 59.1 \| 32.0 \| 86.4 \| 1464.9 \| - \| \| [tinyllava/TinyLLaVA-Gemma-SigLIP-2.4B](https://huggingface.co/tinyllava/TinyLLaVA-Gemma-SigLIP-2.4B) \| 78.4 \| 61.6 \| 64.4 \| 53.6 \| 26.9 \| 86.4 \| 1339.0 \| 31.7 \| \| [tinyllava/TinyLLaVA-Phi-2-SigLIP-3.1B](https://huggingface.co/tinyllava/TinyLLaVA-Phi-2-SigLIP-3.1B) \| 80.1 \| 62.1 \| 73.0 \| 60.3 \| 37.5 \| 87.2 \| 1466.4 \| 38.4 \| \| [Zhang199/TinyLLaVA-Qwen2-0.5B-SigLIP](https://huggingface.co/Zhang199/TinyLLaVA-Qwen2-0.5B-SigLIP) \| 72.33 \| 55.84 \| 60.14 \| 45.17 \| 19.5 \| 86.59 \| 1153 \| 29.7 \| \| [Zhang199/TinyLLaVA-Qwen2.5-3B-SigLIP](https://huggingface.co/Zhang199/TinyLLaVA-Qwen2.5-3B-SigLIP) \| 79.4 \| 62.5 \| 74.1 \| 58.3 \| 34.8 \| 87.4 \| 1438.7 \| 39.9 \| P.S. [TinyLLaVA Factory](https://github.com/TinyLLaVA/TinyLLaVA_Factory) is an open-source modular codebase for small-scale LMMs with a focus on simplicity of code implementations, extensibility of new features, and reproducibility of training results. This code repository provides standard training&evaluating pipelines, flexible data preprocessing&model configurations, and easily extensible architectures. Users can customize their own LMMs with minimal coding effort and less coding mistake. TinyLLaVA Factory integrates a suite of cutting-edge models and methods. - LLM currently supports OpenELM, TinyLlama, StableLM, Qwen, Gemma, Phi, and Qwen2. - Vision tower currently supports CLIP, SigLIP, Dino, and combination of CLIP and Dino. - Connector currently supports MLP, Qformer, and Resampler.
Skywork/Skywork-OR1-7B-Preview	Skywork	2025-05-29T02:26:55Z	8,184	13	null	[ "safetensors", "qwen2", "dataset:Skywork/Skywork-OR1-RL-Data", "arxiv:2505.22312", "base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "region:us" ]	null	2025-04-13T08:49:45Z	--- base_model: - deepseek-ai/DeepSeek-R1-Distill-Qwen-7B datasets: - Skywork/Skywork-OR1-RL-Data --- <div align="center"> # 🤔 Skywork-OR1 (Open Reasoner 1) </div> <div> <br> <div align="center"> [![Models](https://img.shields.io/badge/Models-4d5eff?style=for-the-badge&logo=huggingface&logoColor=ffffff&labelColor)](https://huggingface.co/collections/Skywork/skywork-or1-67fa1bcb41b436ef2def76b9) [![Data](https://img.shields.io/badge/Data-4d5eff?style=for-the-badge&logo=huggingface&logoColor=ffffff&labelColor)](https://huggingface.co/datasets/Skywork/Skywork-OR1-RL-Data) [![Github](https://img.shields.io/badge/Code-000000?style=for-the-badge&logo=github&logoColor=white)](https://github.com/SkyworkAI/Skywork-OR1) [![Notion](https://img.shields.io/badge/Notion_Blog-000000?style=for-the-badge&logo=notion&logoColor=white)](https://capricious-hydrogen-41c.notion.site/Skywork-Open-Reaonser-Series-1d0bc9ae823a80459b46c149e4f51680) [![GitHub Stars](https://img.shields.io/github/stars/SkyworkAI/Skywork-OR1?style=for-the-badge&logo=github&logoColor=white&label=Stars&color=000000)](https://github.com/SkyworkAI/Skywork-OR1/stargazers) [![GitHub Forks](https://img.shields.io/github/forks/SkyworkAI/Skywork-OR1?style=for-the-badge&logo=github&logoColor=white&label=Forks&color=000000)](https://github.com/SkyworkAI/Skywork-OR1/fork) </div> ## 🔥 News - April 13, 2025: We release the `Skywork-OR1` (Open Reasoner 1) series of models, including `Skywork-OR1-Math-7B`, `Skywork-OR1-32B-Preview`, and `Skywork-OR1-7B-Preview`. We open-source - 🤗 Model weights: [`Skywork-OR1-Math-7B`](https://huggingface.co/Skywork/Skywork-OR1-Math-7B), [`Skywork-OR1-32B-Preview`](https://huggingface.co/Skywork/Skywork-OR1-32B-Preview), [`Skywork-OR1-7B-Preview`](https://huggingface.co/Skywork/Skywork-OR1-7B-Preview) - 🤗 Training data: [`Skywork-OR1-RL-Data`](https://huggingface.co/datasets/Skywork/Skywork-OR1-RL-Data) - 🧑‍💻 Code: [`Skywork-OR1`](https://github.com/SkyworkAI/Skywork-OR1) - We also release a [Notion Blog](https://capricious-hydrogen-41c.notion.site/Skywork-Open-Reaonser-Series-1d0bc9ae823a80459b46c149e4f51680) to share detailed training recipes and extensive experimental results, analysis, and insights, dedicated to helping the community to better research, understand, and push the frontier of open reasoning models. ## 📖 Overview <div align="center"> <img src="./assets/skywork-or1-math-7b-multi-stage.png" width="60%"/> <sub>The AIME24 scores versus training steps of Skywork-OR1-Math-7B in our multi-stage training pipeline.</sub> </div> The `Skywork-OR1` (Open Reasoner 1) model series consists of powerful math and code reasoning models trained using large-scale rule-based reinforcement learning with carefully designed datasets and training recipes. This series includes two general-purpose reasoning modelsl, `Skywork-OR1-7B-Preview` and `Skywork-OR1-32B-Preview`, along with a math-specialized model, `Skywork-OR1-Math-7B`. - [`Skywork-OR1-Math-7B`](https://huggingface.co/Skywork/Skywork-OR1-Math-7B) is specifically optimized for mathematical reasoning, scoring 69.8 on AIME24 and 52.3 on AIME25 — well ahead of all models of similar size. - [`Skywork-OR1-32B-Preview`](https://huggingface.co/Skywork/Skywork-OR1-32B-Preview) delivers the 671B-parameter Deepseek-R1 performance on math tasks (AIME24 and AIME25) and coding tasks (LiveCodeBench). - [`Skywork-OR1-7B-Preview`](https://huggingface.co/Skywork/Skywork-OR1-7B-Preview) outperforms all similarly sized models in both math and coding scenarios. The final release version will be available in two weeks. ## 📊 Evaluation <div align="center"> <img src="./assets/32b_perf.png" width="75%"/> <img src="./assets/7b_perf.png" width="75%"/> </div> </div> We evaluate our models on AIME24, AIME25, and LiveCodeBench. Instead of using Pass@1, which is common in prior work, we introduce Avg@K as the primary metric. This metric robustly measures a model's average performance across K independent attempts, reducing the impact of randomness and enhancing the reliability of the results. We believe that Avg@K provides a better reflection of a model's stability and reasoning consistency. We include the detailed results in the following table. \| Model \| AIME24 (Avg@32) \| AIME25 (Avg@32) \| LiveCodeBench (8/1/24-2/1/25) (Avg@4) \| \|-------\|---------\|---------\|--------------\| \| DeepSeek-R1-Distill-Qwen-7B \| 55.5 \| 39.2\| 37.6 \| \| Light-R1-7B-DS \| 59.1 \| 44.3\| 39.5 \| \| DeepSeek-R1-Distill-Qwen-32B \| 72.9 \| 59.0\| 57.2 \| \| TinyR1-32B-Preview \| 78.1\| 65.3\| 61.6 \| \| QwQ-32B \| 79.5 \| 65.3\| 61.6 \| \| DeepSeek-R1 \| 79.8 \| 70.0\| 65.9 \| \| Skywork-OR1-Math-7B \| 69.8 \| 52.3 \| 43.6 \| \| Skywork-OR1-7B-Preview \| 63.6 \| 45.8 \| 43.9 \| \| Skywork-OR1-32B-Preview \| 79.7 \| 69.0 \| 63.9 \| ## ⚙️ Training Recipe We offer a brief overview of our data and training pipeline below. For more details, please refer to our Notion Blog [here](https://capricious-hydrogen-41c.notion.site/Skywork-Open-Reaonser-Series-1d0bc9ae823a80459b46c149e4f51680). ### Data - We select, clean, and curate a dataset of 110K verifiable, challenging, and diverse math problems and 14K coding questions from open-source datasets. - We perform model-aware difficulty estimation for each problem and model and conduct rigorous quality assessment prior to training to ensure training efficiency and effectiveness. ### Training We develop a customized version of GRPO that leverages both data-wise and training-wise improvements: - We perform both offline and online difficulty-based filtering and rejection sampling to improve training efficiency. - We incorporate a multi-stage training pipeline coupled with adaptive entropy control and other techniques to enhance exploration and stability. ## 📄 Technical Report Our technical report will be released soon. Stay tuned! ## 🙏 Acknowledgements - Both of our models are trained on top of [`DeepSeek-R1-Distill-Qwen-7B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) and [`DeepSeek-R1-Distill-Qwen-32B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B). - Both models are trained using [a custom fork](https://github.com/SkyworkAI/Skywork-OR1) of the wonderful [`verl`](https://github.com/volcengine/verl) project. ## 📚 Citation Please cite the following: ```bibtex @article{he2025skywork, title={Skywork Open Reasoner 1 Technical Report}, author={He, Jujie and Liu, Jiacai and Liu, Chris Yuhao and Yan, Rui and Wang, Chaojie and Cheng, Peng and Zhang, Xiaoyu and Zhang, Fuxiang and Xu, Jiacheng and Shen, Wei and Li, Siyuan and Zeng, Liang and Wei, Tianwen and Cheng, Cheng and An, Bo and Liu, Yang and Zhou, Yahui}, journal={arXiv preprint arXiv:2505.22312}, year={2025} } @misc{skywork-or1-2025, title={Skywork Open Reasoner Series}, author = {He, Jujie and Liu, Jiacai and Liu, Chris Yuhao and Yan, Rui and Wang, Chaojie and Cheng, Peng and Zhang, Xiaoyu and Zhang, Fuxiang and Xu, Jiacheng and Shen, Wei and Li, Siyuan and Zeng, Liang and Wei, Tianwen and Cheng, Cheng and Liu, Yang and Zhou, Yahui}, howpublished={\url{https://capricious-hydrogen-41c.notion.site/Skywork-Open-Reaonser-Series-1d0bc9ae823a80459b46c149e4f51680}}, note={Notion Blog}, year={2025} } ```
Skywork/Skywork-OR1-Math-7B	Skywork	2025-05-29T02:26:36Z	2,819	11	null	[ "safetensors", "qwen2", "dataset:Skywork/Skywork-OR1-RL-Data", "arxiv:2505.22312", "base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "region:us" ]	null	2025-04-12T09:00:27Z	--- base_model: - deepseek-ai/DeepSeek-R1-Distill-Qwen-7B datasets: - Skywork/Skywork-OR1-RL-Data --- <div align="center"> # 🤔 Skywork-OR1 (Open Reasoner 1) </div> <div> <br> <div align="center"> [![Models](https://img.shields.io/badge/Models-4d5eff?style=for-the-badge&logo=huggingface&logoColor=ffffff&labelColor)](https://huggingface.co/collections/Skywork/skywork-or1-67fa1bcb41b436ef2def76b9) [![Data](https://img.shields.io/badge/Data-4d5eff?style=for-the-badge&logo=huggingface&logoColor=ffffff&labelColor)](https://huggingface.co/datasets/Skywork/Skywork-OR1-RL-Data) [![Github](https://img.shields.io/badge/Code-000000?style=for-the-badge&logo=github&logoColor=white)](https://github.com/SkyworkAI/Skywork-OR1) [![Notion](https://img.shields.io/badge/Notion_Blog-000000?style=for-the-badge&logo=notion&logoColor=white)](https://capricious-hydrogen-41c.notion.site/Skywork-Open-Reaonser-Series-1d0bc9ae823a80459b46c149e4f51680) [![GitHub Stars](https://img.shields.io/github/stars/SkyworkAI/Skywork-OR1?style=for-the-badge&logo=github&logoColor=white&label=Stars&color=000000)](https://github.com/SkyworkAI/Skywork-OR1/stargazers) [![GitHub Forks](https://img.shields.io/github/forks/SkyworkAI/Skywork-OR1?style=for-the-badge&logo=github&logoColor=white&label=Forks&color=000000)](https://github.com/SkyworkAI/Skywork-OR1/fork) </div> ## 🔥 News - April 13, 2025: We release the `Skywork-OR1` (Open Reasoner 1) series of models, including `Skywork-OR1-Math-7B`, `Skywork-OR1-32B-Preview`, and `Skywork-OR1-7B-Preview`. We open-source - 🤗 Model weights: [`Skywork-OR1-Math-7B`](https://huggingface.co/Skywork/Skywork-OR1-Math-7B), [`Skywork-OR1-32B-Preview`](https://huggingface.co/Skywork/Skywork-OR1-32B-Preview), [`Skywork-OR1-7B-Preview`](https://huggingface.co/Skywork/Skywork-OR1-7B-Preview) - 🤗 Training data: [`Skywork-OR1-RL-Data`](https://huggingface.co/datasets/Skywork/Skywork-OR1-RL-Data) - 🧑‍💻 Code: [`Skywork-OR1`](https://github.com/SkyworkAI/Skywork-OR1) - We also release a [Notion Blog](https://capricious-hydrogen-41c.notion.site/Skywork-Open-Reaonser-Series-1d0bc9ae823a80459b46c149e4f51680) to share detailed training recipes and extensive experimental results, analysis, and insights, dedicated to helping the community to better research, understand, and push the frontier of open reasoning models. ## 📖 Overview <div align="center"> <img src="./assets/skywork-or1-math-7b-multi-stage.png" width="60%"/> <sub>The AIME24 scores versus training steps of Skywork-OR1-Math-7B in our multi-stage training pipeline.</sub> </div> The `Skywork-OR1` (Open Reasoner 1) model series consists of powerful math and code reasoning models trained using large-scale rule-based reinforcement learning with carefully designed datasets and training recipes. This series includes two general-purpose reasoning modelsl, `Skywork-OR1-7B-Preview` and `Skywork-OR1-32B-Preview`, along with a math-specialized model, `Skywork-OR1-Math-7B`. - [`Skywork-OR1-Math-7B`](https://huggingface.co/Skywork/Skywork-OR1-Math-7B) is specifically optimized for mathematical reasoning, scoring 69.8 on AIME24 and 52.3 on AIME25 — well ahead of all models of similar size. - [`Skywork-OR1-32B-Preview`](https://huggingface.co/Skywork/Skywork-OR1-32B-Preview) delivers the 671B-parameter Deepseek-R1 performance on math tasks (AIME24 and AIME25) and coding tasks (LiveCodeBench). - [`Skywork-OR1-7B-Preview`](https://huggingface.co/Skywork/Skywork-OR1-7B-Preview) outperforms all similarly sized models in both math and coding scenarios. The final release version will be available in two weeks. ## 📊 Evaluation <div align="center"> <img src="./assets/32b_perf.png" width="75%"/> <img src="./assets/7b_perf.png" width="75%"/> </div> </div> We evaluate our models on AIME24, AIME25, and LiveCodeBench. Instead of using Pass@1, which is common in prior work, we introduce Avg@K as the primary metric. This metric robustly measures a model's average performance across K independent attempts, reducing the impact of randomness and enhancing the reliability of the results. We believe that Avg@K provides a better reflection of a model's stability and reasoning consistency. We include the detailed results in the following table. \| Model \| AIME24 (Avg@32) \| AIME25 (Avg@32) \| LiveCodeBench (8/1/24-2/1/25) (Avg@4) \| \|-------\|---------\|---------\|--------------\| \| DeepSeek-R1-Distill-Qwen-7B \| 55.5 \| 39.2\| 37.6 \| \| Light-R1-7B-DS \| 59.1 \| 44.3\| 39.5 \| \| DeepSeek-R1-Distill-Qwen-32B \| 72.9 \| 59.0\| 57.2 \| \| TinyR1-32B-Preview \| 78.1\| 65.3\| 61.6 \| \| QwQ-32B \| 79.5 \| 65.3\| 61.6 \| \| DeepSeek-R1 \| 79.8 \| 70.0\| 65.9 \| \| Skywork-OR1-Math-7B \| 69.8 \| 52.3 \| 43.6 \| \| Skywork-OR1-7B-Preview \| 63.6 \| 45.8 \| 43.9 \| \| Skywork-OR1-32B-Preview \| 79.7 \| 69.0 \| 63.9 \| ## ⚙️ Training Recipe We offer a brief overview of our data and training pipeline below. For more details, please refer to our Notion Blog [here](https://capricious-hydrogen-41c.notion.site/Skywork-Open-Reaonser-Series-1d0bc9ae823a80459b46c149e4f51680). ### Data - We select, clean, and curate a dataset of 110K verifiable, challenging, and diverse math problems and 14K coding questions from open-source datasets. - We perform model-aware difficulty estimation for each problem and model and conduct rigorous quality assessment prior to training to ensure training efficiency and effectiveness. ### Training We develop a customized version of GRPO that leverages both data-wise and training-wise improvements: - We perform both offline and online difficulty-based filtering and rejection sampling to improve training efficiency. - We incorporate a multi-stage training pipeline coupled with adaptive entropy control and other techniques to enhance exploration and stability. ## 📄 Technical Report Our technical report will be released soon. Stay tuned! ## 🙏 Acknowledgements - Both of our models are trained on top of [`DeepSeek-R1-Distill-Qwen-7B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) and [`DeepSeek-R1-Distill-Qwen-32B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B). - Both models are trained using [a custom fork](https://github.com/SkyworkAI/Skywork-OR1) of the wonderful [`verl`](https://github.com/volcengine/verl) project. ## 📚 Citation Please cite the following: ```bibtex @article{he2025skywork, title={Skywork Open Reasoner 1 Technical Report}, author={He, Jujie and Liu, Jiacai and Liu, Chris Yuhao and Yan, Rui and Wang, Chaojie and Cheng, Peng and Zhang, Xiaoyu and Zhang, Fuxiang and Xu, Jiacheng and Shen, Wei and Li, Siyuan and Zeng, Liang and Wei, Tianwen and Cheng, Cheng and An, Bo and Liu, Yang and Zhou, Yahui}, journal={arXiv preprint arXiv:2505.22312}, year={2025} } @misc{skywork-or1-2025, title={Skywork Open Reasoner Series}, author = {He, Jujie and Liu, Jiacai and Liu, Chris Yuhao and Yan, Rui and Wang, Chaojie and Cheng, Peng and Zhang, Xiaoyu and Zhang, Fuxiang and Xu, Jiacheng and Shen, Wei and Li, Siyuan and Zeng, Liang and Wei, Tianwen and Cheng, Cheng and Liu, Yang and Zhou, Yahui}, howpublished={\url{https://capricious-hydrogen-41c.notion.site/Skywork-Open-Reaonser-Series-1d0bc9ae823a80459b46c149e4f51680}}, note={Notion Blog}, year={2025} } ```
ModSpecialization/distilbert-base-uncased-fraud-classifer	ModSpecialization	2025-05-29T02:26:34Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "dataset:ModSpecialization/Credit_Card_Transaction_Dataset", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-29T02:06:47Z	--- library_name: transformers license: apache-2.0 tags: - text-classification - transformers - distilbert datasets: - ModSpecialization/Credit_Card_Transaction_Dataset base_model: - distilbert/distilbert-base-uncased pipeline_tag: text-classification metrics: - accuracy - f1 --- # DistilBERT Fraud Detection This is the model card of a 🤗 transformers model that has been pushed on the Hub. A fine-tuned `distilbert-base-uncased` model for binary classification (fraud detection). - Developed by: Model Specialization Lab - Model type: Classification - Finetuned from model : distilbert/distilbert-base-uncased ## Evaluation Metrics See [`eval_metrics.json`](./eval_metrics.json) for detailed metrics.
aiivanoff1982/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-long_sharp_skunk	aiivanoff1982	2025-05-29T02:21:15Z	12	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am long sharp skunk", "trl", "conversational", "arxiv:2402.03300", "base_model:unsloth/Qwen2.5-0.5B-Instruct", "base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-06T08:40:02Z	--- base_model: unsloth/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-long_sharp_skunk tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am long sharp skunk - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-long_sharp_skunk This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="aiivanoff1982/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-long_sharp_skunk", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.7.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
tsubaki0x01/NER-finetuning-BETO-PRO	tsubaki0x01	2025-05-29T02:17:17Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:NazaGara/NER-fine-tuned-BETO", "base_model:finetune:NazaGara/NER-fine-tuned-BETO", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-05-06T02:33:06Z	--- library_name: transformers license: cc-by-4.0 base_model: NazaGara/NER-fine-tuned-BETO tags: - generated_from_trainer model-index: - name: NER-finetuning-BETO-PRO 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. --> # NER-finetuning-BETO-PRO This model is a fine-tuned version of [NazaGara/NER-fine-tuned-BETO](https://huggingface.co/NazaGara/NER-fine-tuned-BETO) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 4 ### Training results ### Framework versions - Transformers 4.52.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
mradermacher/L3.1-Dark-Planet-SpinFire-Uncensored-8B-i1-GGUF	mradermacher	2025-05-29T02:09:41Z	106	4	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama 3.1", "llama-3", "llama3", "llama-3.1", "en", "base_model:DavidAU/L3.1-Dark-Planet-SpinFire-Uncensored-8B", "base_model:quantized:DavidAU/L3.1-Dark-Planet-SpinFire-Uncensored-8B", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-11-10T21:54:43Z	--- base_model: DavidAU/L3.1-Dark-Planet-SpinFire-Uncensored-8B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge - llama 3.1 - llama-3 - llama3 - llama-3.1 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/DavidAU/L3.1-Dark-Planet-SpinFire-Uncensored-8B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/L3.1-Dark-Planet-SpinFire-Uncensored-8B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/L3.1-Dark-Planet-SpinFire-Uncensored-8B-i1-GGUF/resolve/main/L3.1-Dark-Planet-SpinFire-Uncensored-8B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-Dark-Planet-SpinFire-Uncensored-8B-i1-GGUF/resolve/main/L3.1-Dark-Planet-SpinFire-Uncensored-8B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-Dark-Planet-SpinFire-Uncensored-8B-i1-GGUF/resolve/main/L3.1-Dark-Planet-SpinFire-Uncensored-8B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-Dark-Planet-SpinFire-Uncensored-8B-i1-GGUF/resolve/main/L3.1-Dark-Planet-SpinFire-Uncensored-8B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-Dark-Planet-SpinFire-Uncensored-8B-i1-GGUF/resolve/main/L3.1-Dark-Planet-SpinFire-Uncensored-8B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-Dark-Planet-SpinFire-Uncensored-8B-i1-GGUF/resolve/main/L3.1-Dark-Planet-SpinFire-Uncensored-8B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-Dark-Planet-SpinFire-Uncensored-8B-i1-GGUF/resolve/main/L3.1-Dark-Planet-SpinFire-Uncensored-8B.i1-Q2_K.gguf) \| i1-Q2_K \| 3.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-Dark-Planet-SpinFire-Uncensored-8B-i1-GGUF/resolve/main/L3.1-Dark-Planet-SpinFire-Uncensored-8B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-Dark-Planet-SpinFire-Uncensored-8B-i1-GGUF/resolve/main/L3.1-Dark-Planet-SpinFire-Uncensored-8B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-Dark-Planet-SpinFire-Uncensored-8B-i1-GGUF/resolve/main/L3.1-Dark-Planet-SpinFire-Uncensored-8B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-Dark-Planet-SpinFire-Uncensored-8B-i1-GGUF/resolve/main/L3.1-Dark-Planet-SpinFire-Uncensored-8B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-Dark-Planet-SpinFire-Uncensored-8B-i1-GGUF/resolve/main/L3.1-Dark-Planet-SpinFire-Uncensored-8B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-Dark-Planet-SpinFire-Uncensored-8B-i1-GGUF/resolve/main/L3.1-Dark-Planet-SpinFire-Uncensored-8B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 4.1 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-Dark-Planet-SpinFire-Uncensored-8B-i1-GGUF/resolve/main/L3.1-Dark-Planet-SpinFire-Uncensored-8B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.4 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-Dark-Planet-SpinFire-Uncensored-8B-i1-GGUF/resolve/main/L3.1-Dark-Planet-SpinFire-Uncensored-8B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-Dark-Planet-SpinFire-Uncensored-8B-i1-GGUF/resolve/main/L3.1-Dark-Planet-SpinFire-Uncensored-8B.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-Dark-Planet-SpinFire-Uncensored-8B-i1-GGUF/resolve/main/L3.1-Dark-Planet-SpinFire-Uncensored-8B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-Dark-Planet-SpinFire-Uncensored-8B-i1-GGUF/resolve/main/L3.1-Dark-Planet-SpinFire-Uncensored-8B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-Dark-Planet-SpinFire-Uncensored-8B-i1-GGUF/resolve/main/L3.1-Dark-Planet-SpinFire-Uncensored-8B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-Dark-Planet-SpinFire-Uncensored-8B-i1-GGUF/resolve/main/L3.1-Dark-Planet-SpinFire-Uncensored-8B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-Dark-Planet-SpinFire-Uncensored-8B-i1-GGUF/resolve/main/L3.1-Dark-Planet-SpinFire-Uncensored-8B.i1-Q6_K.gguf) \| i1-Q6_K \| 6.7 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/MN-DARKEST-UNIVERSE-29B-GGUF	mradermacher	2025-05-29T02:09:33Z	8	0	transformers	[ "transformers", "gguf", "mistral nemo", "creative", "creative writing", "fiction writing", "plot generation", "sub-plot generation", "story generation", "scene continue", "storytelling", "fiction story", "science fiction", "romance", "all genres", "story", "writing", "vivid prosing", "vivid writing", "fiction", "roleplaying", "bfloat16", "brainstorm 40x", "swearing", "mistral", "mistral nemo instruct", "mn", "128k context", "rp", "horror", "mergekit", "merge", "en", "base_model:DavidAU/MN-DARKEST-UNIVERSE-29B", "base_model:quantized:DavidAU/MN-DARKEST-UNIVERSE-29B", "endpoints_compatible", "region:us" ]	null	2024-11-10T22:15:01Z	--- base_model: DavidAU/MN-DARKEST-UNIVERSE-29B language: - en library_name: transformers quantized_by: mradermacher tags: - mistral nemo - creative - creative writing - fiction writing - plot generation - sub-plot generation - fiction writing - story generation - scene continue - storytelling - fiction story - science fiction - romance - all genres - story - writing - vivid prosing - vivid writing - fiction - roleplaying - bfloat16 - brainstorm 40x - swearing - mistral nemo - mistral - mistral nemo instruct - mn - 128k context - rp - horror - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/DavidAU/MN-DARKEST-UNIVERSE-29B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/MN-DARKEST-UNIVERSE-29B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/MN-DARKEST-UNIVERSE-29B-GGUF/resolve/main/MN-DARKEST-UNIVERSE-29B.Q2_K.gguf) \| Q2_K \| 11.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-DARKEST-UNIVERSE-29B-GGUF/resolve/main/MN-DARKEST-UNIVERSE-29B.Q3_K_S.gguf) \| Q3_K_S \| 12.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-DARKEST-UNIVERSE-29B-GGUF/resolve/main/MN-DARKEST-UNIVERSE-29B.Q3_K_M.gguf) \| Q3_K_M \| 14.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MN-DARKEST-UNIVERSE-29B-GGUF/resolve/main/MN-DARKEST-UNIVERSE-29B.Q3_K_L.gguf) \| Q3_K_L \| 15.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-DARKEST-UNIVERSE-29B-GGUF/resolve/main/MN-DARKEST-UNIVERSE-29B.IQ4_XS.gguf) \| IQ4_XS \| 16.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-DARKEST-UNIVERSE-29B-GGUF/resolve/main/MN-DARKEST-UNIVERSE-29B.Q4_K_S.gguf) \| Q4_K_S \| 16.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MN-DARKEST-UNIVERSE-29B-GGUF/resolve/main/MN-DARKEST-UNIVERSE-29B.Q4_K_M.gguf) \| Q4_K_M \| 17.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MN-DARKEST-UNIVERSE-29B-GGUF/resolve/main/MN-DARKEST-UNIVERSE-29B.Q5_K_S.gguf) \| Q5_K_S \| 20.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-DARKEST-UNIVERSE-29B-GGUF/resolve/main/MN-DARKEST-UNIVERSE-29B.Q5_K_M.gguf) \| Q5_K_M \| 20.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MN-DARKEST-UNIVERSE-29B-GGUF/resolve/main/MN-DARKEST-UNIVERSE-29B.Q6_K.gguf) \| Q6_K \| 24.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MN-DARKEST-UNIVERSE-29B-GGUF/resolve/main/MN-DARKEST-UNIVERSE-29B.Q8_0.gguf) \| Q8_0 \| 31.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
anderslindstrom/q-Taxi-v3	anderslindstrom	2025-05-29T02:07:31Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-05-29T02:07:27Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.48 +/- 2.69 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="anderslindstrom/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

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