Open-Weight Models

Model · Zero-shot classification

deberta-v3-base-zeroshot-v1.1-all-33

Moritz Borrett-Laurer (formerly Laurer)

The model is designed for zero-shot classification with the Hugging Face pipeline. The model can do one universal classification task: determine whether a hypothesis is "true" or "not true" given a text (entailment vs. notentailment). This task format is based on the Natural Language Inference task (NLI). The task is so universal that any classification task can be reformulated into this task. A detailed description of how the model was trained and how it can be used is available in this paper. The model was trained on a mixture of 33 datasets and 387 classes that have been reformatted into this universal format. 1. Five NLI datasets with ~885k texts: "mnli", "anli", "fever", "wanli"…

Model · Text to video

Wan2.2-TI2V-5B

Wan-AI

We are excited to introduce Wan2.2, a major upgrade to our foundational video models. With Wan2.2, we have focused on incorporating the following innovations: This repository contains our TI2V-5B model, built with the advanced Wan2.2-VAE that achieves a compression ratio of 16×16×4. This model supports both text-to-video and image-to-video generation at 720P resolution with 24fps and can runs on single consumer-grade GPU such as the 4090. It is one of the fastest 720P@24fps models available, meeting the needs of both industrial applications and academic research. Your browser does not support the video tag. If your research or project builds upon Wan2.1 or Wan2.2, we welcome you to share it…

Model · Video classification

videomae-large-finetuned-kinetics

Multimedia Computing Group-Nanjing University

VideoMAE model pre-trained for 1600 epochs in a self-supervised way and fine-tuned in a supervised way on Kinetics-400. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et al. and first released in this repository. Disclaimer: The team releasing VideoMAE did not write a model card for this model so this model card has been written by the Hugging Face team. VideoMAE is an extension of Masked Autoencoders (MAE) to video. The architecture of the model is very similar to that of a standard Vision Transformer (ViT), with a decoder on top for predicting pixel values for masked patches. Videos are presented to…

Model · Zero-shot classification

scandi-nli-large

Alexandra Institute

This model is a fine-tuned version of NbAiLab/nb-bert-large for Natural Language Inference in Danish, Norwegian Bokmål and Swedish. We have released three models for Scandinavian NLI, of different sizes: - alexandrainst/scandi-nli-large (this) A demo of the large-v2 model can be found in this Hugging Face Space - check it out! The performance and model size of each of them can be found in the Performance section below. You can use this model in your scripts as follows: We assess the models both on their aggregate Scandinavian performance, as well as their language-specific Danish, Swedish and Norwegian Bokmål performance. In all cases, we report Matthew's Correlation Coefficient (MCC)…

Model · Time series forecasting

granite-timeseries-patchtst

IBM Granite

PatchTST is a transformer-based model for time series modeling tasks, including forecasting, regression, and classification. This repository contains a pre-trained PatchTST model encompassing all seven channels of the ETTh1 dataset. This particular pre-trained model produces a Mean Squared Error (MSE) of 0.3881 on the test split of the ETTh1 dataset when forecasting 96 hours into the future with a historical data window of 512 hours. For training and evaluating a PatchTST model, you can refer to this demo notebook. The PatchTST model was proposed in A Time Series is Worth 64 Words: Long-term Forecasting with Transformers by Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, Jayant Kalagnanam. At…

Model · Robotics

pi05_base

LeRobot

π₀.₅ is a Vision-Language-Action (VLA) model with open-world generalization from Physical Intelligence, co-trained on robot demonstrations and large-scale multimodal data to execute long-horizon tasks in unseen real-world environments. Note: This model currently supports only the flow-matching action head for π₀.₅ training and inference. Other components from the original work (e.g., subtask prediction, action tokenization, or RL) were not released upstream and are not included here, though the LeRobot team is actively working to support them. Original paper: π0.5: A Vision-Language-Action Model with Open-World Generalization For full installation details (including optional video…

Model · Text generation

idefics-9b

HuggingFaceM4

How do I pronounce the model's name? Watch a Youtube tutorial IDEFICS (Image-aware Decoder Enhanced à la Flamingo with Interleaved Cross-attentionS) is an open-access reproduction of Flamingo, a closed-source visual language model developed by Deepmind. Like GPT-4, the multimodal model accepts arbitrary sequences of image and text inputs and produces text outputs. IDEFICS is built solely on publicly available data and models. The model can answer questions about images, describe visual contents, create stories grounded on multiple images, or simply behave as a pure language model without visual inputs. IDEFICS is on par with the original closed-source model on various image-text benchmarks…

Model · Robotics

MolmoAct2

Ai2

MolmoAct2 is an open vision-language-action model for robot control. It builds on Molmo2-ER, an embodied-reasoning VLM backbone, and connects the autoregressive VLM to a flow-matching continuous action expert through per-layer KV (key-value) conditioning. This checkpoint is the post-trained, multi-embodiment MolmoAct2 model. It is intended as a foundation checkpoint for further robot fine-tuning rather than as a ready-to-run policy for a single deployment setting. Use this checkpoint for further fine-tuning on a target robot embodiment or benchmark. It contains the VLM and continuous action expert weights, plus normalization metadata for the post-training mixture in normstats.json. This…

Model · Tabular classification

tabpfn_2_5

Prior Labs

TabPFN-2.5 is a transformer-based foundation model that uses in-context-learning to solve tabular prediction problems in a forward pass. Inference code can be found at https://github.com/PriorLabs/tabPFN. Fitting a classifier and predicting looks like this: For more examples (e.g. how to train a regressor), see the github repo: https://github.com/PriorLabs/tabPFN! Developed by Prior Labs. Regression and classification tasks with ≤50 000 samples and ≤2000 features in structured tabular format. - Not suitable for unstructured data (text, images); use API version for textual features. - Not tested for >50 000 samples or > 2000 features. Transformer with TabPFNv2-like alternating attention with…

Model · Object detection

dab-detr-resnet-50

IDEA-Research

This is the model card of a transformers model that has been pushed on the Hub. Use the code below to get started with the model. This should output The DAB-DETR model was trained on COCO 2017 object detection, a dataset consisting of 118k/5k annotated images for training/validation respectively. Following Deformable DETR and Conditional DETR, we use 300 anchors as queries. We select 300 predicted boxes and labels with the largest classification logits for evaluation as well. We also use focal loss (Lin et al., 2020) with α = 0.25, γ = 2 for classification. The same loss terms are used in bipartite matching and final loss calculating, but with different coefficients. Classification loss…

BioLinkBERT-base model pretrained on PubMed abstracts along with citation link information. It is introduced in the paper LinkBERT: Pretraining Language Models with Document Links (ACL 2022). The code and data are available in this repository. This model achieves state-of-the-art performance on several biomedical NLP benchmarks such as BLURB and MedQA-USMLE. LinkBERT is a transformer encoder (BERT-like) model pretrained on a large corpus of documents. It is an improvement of BERT that newly captures document links such as hyperlinks and citation links to include knowledge that spans across multiple documents. Specifically, it was pretrained by feeding linked documents into the same language…

Model · Text to video

Vdn-Minimax-H3-Comfy

T8star-Aix

This repository is a ready-to-place model bundle for the OpenVDN workflows in It contains the exact local model set used for the project's corrected DMD8 validation. The Seedance and RunningHub URLs above contain referral or invite codes supplied by T8star. 1. Install or update the custom node: 2. Log in to Hugging Face after your access request is approved, then download this repository directly into ComfyUI/models: 3. Restart ComfyUI and load one of the OpenVDN workflows from examples/workflows/10-speed in the custom-node repository. The repository already uses ComfyUI's folder names, so no file rearrangement is needed. Exact byte sizes and SHA-256 hashes are listed in MODELMANIFEST.json.…

Model · Image segmentation

detr-resnet-50-panoptic

AI at Meta

DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 panoptic (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 objects in…

Model · Zero-shot classification

LLM2CLIP-Openai-L-14-336

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 to text

pix2text-mfr

Breezedeus

Mathematical Formula Recognition (MFR) model from Pix2Text (P2T). This MFR model utilizes the TrOCR architecture developed by Microsoft, starting with its initial values and retrained using a dataset of mathematical formula images. The resulting MFR model can be used to convert images of mathematical formulas into LaTeX text representation. More detailed can be found: Pix2Text V1.0 New Release: The Best Open-Source Formula Recognition Model | Breezedeus.com. 此 MFR 模型使用了微软的 TrOCR 架构，以其为初始值并利用数学公式图片数据集进行了重新训练。 获得的 MFR 模型可用于把数学公式图片转换为 LaTeX 文本表示。更多细节请见：Pix2Text V1.0 新版发布：最好的开源公式识别模型 | Breezedeus.com。 - 用途：此模型为数学公式识别模型，它可以把输入的数学公式图片转换为 LaTeX 文本表示。 This method doesn't need to install pix2text…

Model · Robotics

MolmoAct2-Think

Ai2

MolmoAct2-Think extends MolmoAct2 with depth-token reasoning. Before producing an action, the model can predict a compact 10 x 10 discrete depth representation and condition the action expert on the resulting depth-aware VLM cache. This checkpoint is the post-trained, multi-embodiment depth-reasoning model. It is intended as a foundation checkpoint for further robot fine-tuning rather than as a ready-to-run policy for a single deployment setting. Use this checkpoint for further fine-tuning when the downstream policy should use depth reasoning. It contains the VLM, action expert, and depth-token weights, plus normalization metadata for the post-training mixture in normstats.json. This model…

Model · Text generation

Crowd-v1

Vxtzq

CrowdGPT's first community-distributed language model architecture. Crowd-v1 is the first official model architecture released for CrowdGPT, a community-driven distributed AI project. Unlike a conventional pretrained model release, Crowd-v1 is distributed with randomly initialized weights. The purpose of this release is to provide a common model definition and weight format that CrowdGPT clients can download and collectively train. The model is designed to be consumed by the CrowdGPT distributed training infrastructure, where individual participants contribute compute toward training a shared model. Crowd-v1 contains approximately 1 billion parameters. Grouped-Query Attention (GQA) Crowd-v1…

Model · Text to video

CogVideoX-2b

Z.ai

Visit QingYing and API Platform to experience commercial video generation models. CogVideoX is an open-source version of the video generation model originating from QingYing. The table below displays the list of video generation models we currently offer, along with their foundational information. Data Explanation + When testing using the diffusers library, all optimizations provided by the diffusers library were enabled. This solution has not been tested for actual VRAM/memory usage on devices other than NVIDIA A100 / H100. Generally, this solution can be adapted to all devices with NVIDIA Ampere architecture and above. If the optimizations are disabled, VRAM usage will increase…

Model · Image to text

PP-OCRv5_mobile_rec

PaddlePaddle

PP-OCRv5mobilerec is one of the PP-OCRv5rec that are the latest generation text line recognition models developed by PaddleOCR team. It aims to efficiently and accurately support the recognition of four major languages—Simplified Chinese, Traditional Chinese, English, and Japanese—as well as complex text scenarios such as handwriting, vertical text, pinyin, and rare characters using a single model. The key accuracy metrics are as follow: Note: If any character (including punctuation) in a line is incorrect, the entire line is marked as wrong. This ensures higher accuracy in practical applications. Please refer to the following commands to install PaddlePaddle using pip: For details about…

A diffusers-directory-layout repackaging of Efficient-Large-Model/LongLive-2.0-5B so it loads directly in SGLang Diffusion (sglang.multimodalgen) without any runtime overlay/materialization. - transformer/ — the generator weights extracted from the original modelbf16.pt, kept in their original (model.) naming; SGLang's LongLive2Transformer3DModel.paramnamesmapping maps them to the diffusers module names at load (same convention as LingBot-World). - scheduler / textencoder / tokenizer / vae — taken from Wan-AI/Wan2.2-TI2V-5B-Diffusers. - modelindex.json classname = LongLive2Pipeline.

Model · Robotics

MolmoAct2-Pretrain

Ai2

MolmoAct2-Pretrain adapts the Molmo2-ER vision-language backbone into a discrete autoregressive robot policy while keeping the Molmo2 token interface. Robot state is represented with discrete state tokens, and future one-second actions are represented with OpenFAST action tokens. This checkpoint is the pre-trained VLA backbone before the continuous flow-matching action expert is attached. It is intended for further post-training or fine-tuning, not direct continuous-control inference. Use this checkpoint for further MolmoAct2 training stages. It was converted with addactionexpert=false, so predictaction(...) is intentionally unavailable. Standard Transformers generation can still be used…

Model · Audio classification

sonics-spectttra-alpha-120s

Awsaf

The recent surge in AI-generated songs presents exciting possibilities and challenges. These innovations necessitate the ability to distinguish between human-composed and synthetic songs to safeguard artistic integrity and protect human musical artistry. Existing research and datasets in fake song detection only focus on singing voice deepfake detection (SVDD), where the vocals are AI-generated but the instrumental music is sourced from real songs. However, these approaches are inadequate for detecting contemporary end-to-end artificial songs where all components (vocals, music, lyrics, and style) could be AI-generated. Additionally, existing datasets lack music-lyrics diversity…

Model · Zero-shot classification

rubert-base-cased-nli-threeway

David Dale

This is the DeepPavlov/rubert-base-cased fine-tuned to predict the logical relationship between two short texts: entailment, contradiction, or neutral. How to run the model for NLI: You can also use this model for zero-shot short text classification (by labels only), e.g. for sentiment analysis: Alternatively, you can use Huggingface pipelines for inference. The model has been trained on a series of NLI datasets automatically translated to Russian from English. Most datasets were taken from the repo of Felipe Salvatore: Some datasets obtained from the original sources: The table below shows ROC AUC (one class vs rest) for five models on the corresponding dev sets: - tiny: a small BERT…

Model · Audio classification

ast-finetuned-audioset-10-10-0.4593

Joshua

https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593 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: Example: Perform audio classification with Xenova/ast-finetuned-audioset-10-10-0.4593 and return top 4 results. 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).