Open-Weight Models

Model · Zero-shot classification

DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary

Moritz Borrett-Laurer (formerly Laurer)

This model was trained on 782 357 hypothesis-premise pairs from 4 NLI datasets: MultiNLI, Fever-NLI, LingNLI and ANLI. Note that the model was trained on binary NLI to predict either "entailment" or "not-entailment". This is specifically designed for zero-shot classification, where the difference between "neutral" and "contradiction" is irrelevant. The base model is DeBERTa-v3-xsmall from Microsoft. The v3 variant of DeBERTa substantially outperforms previous versions of the model by including a different pre-training objective, see the DeBERTa-V3 paper. For highest performance (but less speed), I recommend using…

Model · Text to video

minimax-h3-nvfp4-convrot

rockerBOO

Quantized diffusion transformers for MiniMax H3, a 33B omni-modal video+audio generator, built from the ComfyUI repack at Comfy-Org/MiniMax-H3. Output is 768p / 24 fps / 4–15 s with synchronized 32 kHz stereo audio. (2K output requires the separate H3-Regenerate-2K module, which is not part of this or Comfy-Org's release.) These are ComfyUI single-file checkpoints, not diffusers models. Filenames follow minimaxh3.safetensors. - fl2va — first/last-frame mode. Zero images = text-to-video, one or two = frame-conditioned. - ref2va — omni-reference mode (up to 9 images / 3 video clips / 3 audio clips). Both get identical treatment; pick the one matching your workflow. The three INT4 variants…

Model · Image to text

PP-OCRv3_mobile_det

PaddlePaddle

PP-OCRv3mobiledet is one of the PP-OCRv3det series models, a set of text detection models developed by the PaddleOCR team. This mobile-optimized text detection model offers higher efficiency, making it ideal for deployment on edge devices. Please refer to the following commands to install PaddlePaddle using pip: For details about PaddlePaddle installation, please refer to the PaddlePaddle official website. Install the latest version of the PaddleOCR inference package from PyPI: You can quickly experience the functionality with a single command: You can also integrate the model inference of the text detection module into your project. Before running the following code, please download the…

Model · Object detection

detr-resnet-50

Joshua

https://huggingface.co/facebook/detr-resnet-50 with ONNX weights to be compatible with Transformers.js. If you haven't already, you can install the Transformers.js JavaScript library from NPM using: Test it out here, or create your own object-detection demo with 1 click! Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).

Model · Time series forecasting

moirai-1.0-R-small

Salesforce AI Research

Moirai, the Masked Encoder-based Universal Time Series Forecasting Transformer is a Large Time Series Model pre-trained on LOTSA data. For more details on the Moirai architecture, training, and results, please refer to the paper. Fig. 1: Overall architecture of Moirai. Visualized is a 3-variate time series, where variates 0 and 1 are target variables (i.e. to be forecasted, and variate 2 is a dynamic covariate (values in forecast horizon known). Based on a patch size of 64, each variate is patchified into 3 tokens. The patch embeddings along with sequence and variate id are fed into the Transformer. The shaded patches represent the forecast horizon to be forecasted, whose corresponding…

Model · Image segmentation

segformer-b2-finetuned-cityscapes-1024-1024

NVIDIA

SegFormer model fine-tuned on CityScapes at resolution 1024x1024. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository. Disclaimer: The team releasing SegFormer did not write a model card for this model so this model card has been written by the Hugging Face team. SegFormer consists of a hierarchical Transformer encoder and a lightweight all-MLP decode head to achieve great results on semantic segmentation benchmarks such as ADE20K and Cityscapes. The hierarchical Transformer is first pre-trained on ImageNet-1k, after which a decode head is added and fine-tuned altogether on a…

Model · Zero-shot classification

LLM2CLIP-Llama-3-8B-Instruct-CC-Finetuned

Microsoft

Weiquan Huang 1, Aoqi Wu 1, Yifan Yang 2†, Xufang Luo 2, Yuqing Yang 2, Liang Hu 1, Qi Dai 2, Xiyang Dai 2, Dongdong Chen 2, Chong Luo 2, Lili Qiu 2 In this paper, we propose LLM2CLIP, a novel approach that embraces the power of LLMs to unlock CLIP’s potential. By fine-tuning the LLM in the caption space with contrastive learning, we extract its textual capabilities into the output embeddings, significantly improving the output layer’s textual discriminability. We then design an efficient training process where the fine-tuned LLM acts as a powerful teacher for CLIP’s visual encoder. Thanks to the LLM’s presence, we can now incorporate longer and more complex captions without being…

Model · Image and text to text

PaddleOCR-VL-1.5

PaddlePaddle

PaddleOCR-VL-1.5: Towards a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing PaddleOCR-VL-1.5 is an advanced next-generation model of PaddleOCR-VL, achieving a new state-of-the-art accuracy of 94.5% on OmniDocBench v1.5. To rigorously evaluate robustness against real-world physical distortions—including scanning artifacts, skew, warping, screen photography, and illumination—we propose the Real5-OmniDocBench benchmark. Experimental results demonstrate that this enhanced model attains SOTA performance on the newly curated benchmark. Furthermore, we extend the model’s capabilities by incorporating seal recognition and text spotting tasks, while remaining a 0.9B ultra-compact VLM…

Model · Time series forecasting

moirai-1.1-R-large

Salesforce AI Research

This is new updated version of Moirai-1.0-R (https://huggingface.co/Salesforce/moirai-1.0-R-large). The Moirai-1.1-R model achieved significant improvements (~20%) for low-frequency cases like Yearly and Quarterly data in Normalised Mean Absolute Error (NMAE) for 40 datasets on the Monash repository. This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with…

Model · Object detection

YOLO11

Ultralytics

Ultralytics creates cutting-edge, state-of-the-art (SOTA) YOLO models built on years of foundational research in computer vision and AI. Constantly updated for performance and flexibility, our models are fast, accurate, and easy to use. They excel at object detection, tracking, instance segmentation, semantic segmentation, image classification, and pose estimation tasks. Find detailed documentation in the Ultralytics Docs. Get support via GitHub Issues. Join discussions on Discord, Reddit, and the Ultralytics Community Forums! Request an Enterprise License for commercial use at Ultralytics Licensing. See below for quickstart installation and usage examples. For comprehensive guidance on…

Model · Zero-shot classification

mobilebert-uncased-mnli

Joshua

https://huggingface.co/typeform/mobilebert-uncased-mnli with ONNX weights to be compatible with Transformers.js. If you haven't already, you can install the Transformers.js JavaScript library from NPM using: Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).

YOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection by Fang et al. and first released in this repository. Disclaimer: The team releasing YOLOS did not write a model card for this model so this model card has been written by the Hugging Face team. YOLOS is a Vision Transformer (ViT) trained using the DETR loss. Despite its simplicity, a base-sized YOLOS model is able to achieve 42 AP on COCO validation 2017 (similar to DETR and more complex frameworks such as Faster R-CNN). The model is trained using a "bipartite matching loss": one compares the…

Model · Object detection

detr-resnet-101

AI at Meta

DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repository. Disclaimer: The team releasing DETR did not write a model card for this model so this model card has been written by the Hugging Face team. The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect…

Model · Object detection

YOLO26

Ultralytics

Ultralytics creates cutting-edge, state-of-the-art (SOTA) YOLO models built on years of foundational research in computer vision and AI. Constantly updated for performance and flexibility, our models are fast, accurate, and easy to use. They excel at object detection, instance segmentation, semantic segmentation, image classification, depth estimation, pose estimation and oriented object detection tasks, and can track detected objects across video frames. Find detailed documentation in the Ultralytics Docs. Get support via GitHub Issues. Join discussions on Discord, Reddit, and the Ultralytics Community Forums!

Model · Image to text

GLM-OCR

Unsloth AI

Join our WeChat and Discord community Use GLM-OCR's API GLM-OCR is a multimodal OCR model for complex document understanding, built on the GLM-V encoder–decoder architecture. It introduces Multi-Token Prediction (MTP) loss and stable full-task reinforcement learning to improve training efficiency, recognition accuracy, and generalization. The model integrates the CogViT visual encoder pre-trained on large-scale image–text data, a lightweight cross-modal connector with efficient token downsampling, and a GLM-0.5B language decoder. Combined with a two-stage pipeline of layout analysis and parallel recognition based on PP-DocLayout-V3, GLM-OCR delivers robust and high-quality OCR performance…

Model · Audio classification

mms-lid-4017

AI at Meta

This checkpoint is a model fine-tuned for speech language identification (LID) and part of Facebook's Massive Multilingual Speech project. This checkpoint is based on the Wav2Vec2 architecture and classifies raw audio input to a probability distribution over 4017 output classes (each class representing a language). The checkpoint consists of 1 billion parameters and has been fine-tuned from facebook/mms-1b on 4017 languages. This MMS checkpoint can be used with Transformers to identify the spoken language of an audio. It can recognize the following 4017 languages. Let's look at a simple example. First, we install transformers and some other libraries Note: In order to use MMS you need to…

Model · Robotics

smolvla_libero

LeRobot

SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint.

Model · Image to text

en_PP-OCRv3_mobile_rec

PaddlePaddle

enPP-OCRv3mobilerec is a text line recognition model within the PP-OCRv3rec series, developed by the PaddleOCR team. The enPP-OCRv3mobilerec model is an English-specific model trained based on PP-OCRv3mobilerec, and it supports English recognition. The key accuracy metrics are as follow: Note: If any character (including punctuation) in a line was incorrect, the entire line was marked as wrong. This ensures higher accuracy in practical applications. Please refer to the following commands to install PaddlePaddle using pip: For details about PaddlePaddle installation, please refer to the PaddlePaddle official website. Install the latest version of the PaddleOCR inference package from PyPI…

Model · Object detection

stockmarket-pattern-detection-yolov8

FODUU

The YOLOv8s Stock Market Pattern Detection model is an object detection model based on the YOLO (You Only Look Once) framework. It is designed to detect various chart patterns in real-time from screen-captured stock market trading data. The model aids traders and investors by automating the analysis of chart patterns, providing timely insights for informed decision-making. The model has been fine-tuned on a diverse dataset and achieves high accuracy in detecting and classifying stock market patterns in live trading scenarios. The YOLOv8s Stock Market Pattern Detection model enables real-time detection of crucial chart patterns within stock market screen captures. As stock markets evolve…

Model · Audio classification

Silero-VAD-v6.2.1-MLX

Ivan

MLX port of snakers4/silero-vad tag v6.2.1 for voice activity detection on Apple Silicon. Measured with speech-swift release tests on Apple Silicon using a 20 s 16 kHz speech fixture, 625 streaming chunks. Parity against the matching CoreML v6.2.1 export: The exported safetensors were also checked tensor-by-tensor against the upstream v6.2.1 JIT state dict after conversion; the maximum absolute tensor difference was 0. Converted from snakers4/silero-vad tag v6.2.1. The upstream project is MIT licensed. - speech-swift - Apple SDK - Docs - install and CLI docs - soniqo.audio - website - blog - blog

Model · Zero-shot classification

multilingual-MiniLMv2-L6-mnli-xnli

Moritz Borrett-Laurer (formerly Laurer)

multilingual - zero-shot-classification - text-classification - nli - pytorch - accuracy - multinli - xnli pipelinetag: zero-shot-classification candidatelabels: "politics, economy, entertainment, environment" This multilingual model can perform natural language inference (NLI) on 100+ languages and is therefore also suitable for multilingual zero-shot classification. The underlying multilingual-MiniLM-L6 model was created by Microsoft and was distilled from XLM-RoBERTa-large (see details in the original paper and newer information in this repo). The model was then fine-tuned on the XNLI dataset, which contains hypothesis-premise pairs from 15 languages, as well as the English MNLI dataset.…

Model · Time series forecasting

chronos-t5-base

Autogluon

Update Feb 14, 2025: Chronos-Bolt & original Chronos models are now available on Amazon SageMaker JumpStart! Check out the tutorial notebook to learn how to deploy Chronos endpoints for production use in a few lines of code. Update Nov 27, 2024: We have released Chronos-Bolt models that are more accurate (5% lower error), up to 250 times faster and 20 times more memory-efficient than the original Chronos models of the same size. Check out the new models here. Chronos is a family of pretrained time series forecasting models based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these…

Model · Video classification

timesformer-base-finetuned-k600

AI at Meta

TimeSformer model pre-trained on Kinetics-600. It was introduced in the paper TimeSformer: Is Space-Time Attention All You Need for Video Understanding? by Tong et al. and first released in this repository. Disclaimer: The team releasing TimeSformer did not write a model card for this model so this model card has been written by fcakyon. You can use the raw model for video classification into one of the 600 possible Kinetics-600 labels. Here is how to use this model to classify a video: For more code examples, we refer to the documentation.

Model · Video classification

timesformer-base-finetuned-k400

AI at Meta

TimeSformer model pre-trained on Kinetics-400. It was introduced in the paper TimeSformer: Is Space-Time Attention All You Need for Video Understanding? by Tong et al. and first released in this repository. Disclaimer: The team releasing TimeSformer did not write a model card for this model so this model card has been written by fcakyon. You can use the raw model for video classification into one of the 400 possible Kinetics-400 labels. Here is how to use this model to classify a video: For more code examples, we refer to the documentation.