Open-Weight Models

Model · Time series forecasting

moirai-moe-1.0-R-base

Salesforce AI Research

This model has been pushed to the Hub using the PytorchModelHubMixin integration: 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 applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use…

Model · Text generation

JiRackUltra_7b

Center Business Solutions inc

A fast and efficient 7B model optimized for CPU inference. The model was refactored with BitNet features and an updated tokenizer that includes new Routing, Media, Vision, Sound,Tool call, and Robotics tags. Built on a DeepSeek R1 -7B architecture with native ternary (BitNet-style) support and ready-to-run GGUF quantizations. - JiRack is a cloud-ready model that helps save money on cloud infrastructure. It can be used as an expert model in RAG deployments, with the ONNX JiRack Java server as an alternative. - Benefits high quality CPU inference TQ2 on Llama.cpp and Ollama via QAT - Robotcs, Routing, Coding, Multimedia, Advanced tool calling via CMSManhattan/JiRackPrecisionTokenizer…

Model · Tabular classification

Nori-100M

Synthefy

Nori-100M is the ~98.3M-parameter variant of Nori, a tabular foundation model for regression via in-context learning (ICL). Given a few labeled rows as context, it predicts on new query rows in a single forward pass, with no task-specific training or fine-tuning. The model is trained entirely on synthetic data. It uses a GPU when one is available and falls back to CPU. A one-shot helper skips the object predict follows the TabPFNRegressor.predict contract: pass outputtype="mean" (default), "median", or "mode" to choose the point estimate drawn from the model's predictive distribution. To run from a local checkpoint instead of the Hub, pass a path: NoriRegressor(modelpath="path/to/nori.pt").…

Model · Zero-shot classification

deberta-small-long-nli

Tasksource

DeBERTa-v3-small with context length of 1680 tokens fine-tuned on tasksource for 250k steps. I oversampled long NLI tasks (ConTRoL, doc-nli). Training data include HelpSteer v1/v2, logical reasoning tasks (FOLIO, FOL-nli, LogicNLI...), OASST, hh/rlhf, linguistics oriented NLI tasks, tasksource-dpo, fact verification tasks. This model is suitable for long context NLI or as a backbone for reward models or classifiers fine-tuning. This checkpoint has strong zero-shot validation performance on many tasks (e.g. 70% on WNLI), and can be used for: - Zero-shot entailment-based classification for arbitrary labels [ZS]. - Natural language inference [NLI] - Further fine-tuning on a new task or…

Model · Feature extraction

Qwen3-Voice-Embedding-12Hz-1.7B

Markus

Standalone ECAPA-TDNN voice encoder extracted from Qwen/Qwen3-TTS-12Hz-1.7B-Base. Produces 2048-dimensional x-vector speaker embeddings from audio. The encoder follows the ECAPA-TDNN architecture (Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification) and uses Res2Net blocks, squeeze-excitation attention, and attentive statistical pooling. Speaker embeddings can be stored and shared as SafeTensors files. These embeddings are designed to drive voice cloning in the Qwen3-TTS family. There are two main inference paths: the qwentts Python package and the vLLM-Omni serving API. The qwentts package wraps the TTS model and exposes generatevoiceclone. To…

Model · Time series forecasting

moirai-moe-1.0-R-small

Salesforce AI Research

This model has been pushed to the Hub using the PytorchModelHubMixin integration: 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 applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use…

Model · Object detection

YOLOv8

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 · Audio classification

Deepfake-audio-detection-V2

Melody Machine

This model is a fine-tuned version of motheecreator/Deepfake-audio-detection on the audiofolder dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 3e-05 - trainbatchsize: 32 - evalbatchsize: 32 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 128 - lrschedulertype: cosine - lrschedulerwarmupratio: 0.1 - numepochs: 5 - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.19.2 - Tokenizers 0.19.1

Model · Summarization

pegasus-large

Google

Original TF 1 code here Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019 The following is copied from the authors' README. We train a pegasus model with sampled gap sentence ratios on both C4 and HugeNews, and stochastically sample important sentences. The updated the results are reported in this table. The "Mixed & Stochastic" model has the following changes: - trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples). - trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity). - the model uniformly sample a gap sentence ratio between 15% and 45%. - importance sentences are sampled using a…

Model · Question answering

bert-base-uncased-squad-v1

Qingqing Cao

This model was fine-tuned from the HuggingFace BERT base uncased checkpoint on SQuAD1.1. CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz Memory: 32 GiB GPUs: 2 GeForce GTX 1070, each with 8GiB memory GPU driver: 418.87.01, CUDA: 10.1 It took about 2 hours to finish. Note that the above results didn't involve any hyperparameter search.

Model · Object detection

dfine-nano-coco

University of Science and Technology of China

The D-FINE model was proposed in D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement by Yansong Peng, Hebei Li, Peixi Wu, Yueyi Zhang, Xiaoyan Sun, Feng Wu This model was contributed by VladOS95-cyber with the help of @qubvel-hf This is the HF transformers implementation for D-FINE coco -> model trained on COCO obj365 -> model trained on Object365 obj2coco -> model trained on Object365 and then finetuned on COCO D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR models. D-FINE comprises two key components: Fine-grained Distribution Refinement (FDR) and Global…

Model · Summarization

bigbird-pegasus-large-arxiv

Google

BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle. BigBird was introduced in this paper and first released in this repository. Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team. BigBird relies on block sparse attention instead of normal attention (i.e. BERT's attention) and can handle sequences up to a length of 4096 at a much lower compute cost compared to BERT. It has achieved SOTA…

Model · Question answering

roberta-large-squad2

Deepset

This is the roberta-large model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering. Please note that we have also released a distilled version of this model called deepset/roberta-base-squad2-distilled. The distilled model has a comparable prediction quality and runs at twice the speed of the large model. Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack: For a complete example with an extractive question answering…

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 · Robotics

openvla-7b-oft-finetuned-libero-object

Moo Jin Kim

This repository contains the OpenVLA-OFT checkpoint for LIBERO-Object, as described in Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success. OpenVLA-OFT significantly improves upon the base OpenVLA model by incorporating optimized fine-tuning techniques. See here for other OpenVLA-OFT checkpoints: https://huggingface.co/moojink?searchmodels=oft This example demonstrates generating an action chunk using a pretrained OpenVLA-OFT checkpoint. Ensure you have set up the conda environment as described in the GitHub README.

Model · Time series forecasting

Aurora

DI DaSE ECNU

alt="Aurora Logo" src="https://cdn-uploads.huggingface.co/production/uploads/66276727368ec2a0b933772c/ytpsIAr98keUvNouoOVmb.png" width="30%" The official code repo of our ICLR 2026 paper: Aurora: Towards Universal Generative Multimodal Time Series Forecasting alt="ICLR 2026" src="https://img.shields.io/badge/ICLR%202026-Aurora-orange" alt="Python" src="https://img.shields.io/badge/Python-3.10%2B-blue" alt="PyTorch" src="https://img.shields.io/badge/PyTorch-2.4.1-blue" alt="GitHub Stars" src="https://img.shields.io/github/stars/decisionintelligence/Aurora?logo=github" alt="GitHub" src="https://img.shields.io/badge/GitHub-Aurora-black?logo=github" Aurora is a highly capable multimodal time…

Model · Robotics

openvla-7b-oft-finetuned-libero-goal

Moo Jin Kim

This repository contains the OpenVLA-OFT checkpoint for LIBERO-Goal, as described in Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success. OpenVLA-OFT significantly improves upon the base OpenVLA model by incorporating optimized fine-tuning techniques. See here for other OpenVLA-OFT checkpoints: https://huggingface.co/moojink?searchmodels=oft This example demonstrates generating an action chunk using a pretrained OpenVLA-OFT checkpoint. Ensure you have set up the conda environment as described in the GitHub README.

Model · Object detection

yolo11n-text

Oleksandr Rudnychenko

A fine-tuned YOLO11n model for detecting text regions in images. This model is optimized for detecting text bounding boxes in documents, screenshots, UI interfaces, and natural scene images. This model is based on Ultralytics YOLO11n (nano variant) and has been fine-tuned specifically for text detection tasks. It detects text regions as bounding boxes, which can be used as input for OCR pipelines or UI automation tasks. - Optimized for horizontal text; may have reduced accuracy on rotated text - Single class (text) - does not distinguish between text types This model is released under the Apache 2.0 License. - Ultralytics for the YOLO11 architecture - DonkeySmall for the training dataset

Model · Audio classification

distil-wav2vec2-adult-child-cls-37m

Bookbot

DistilWav2Vec2 Adult/Child Speech Classifier is an audio classification model based on the wav2vec 2.0 architecture. This model is a distilled version of wav2vec2-adult-child-cls on a private adult/child speech classification dataset. This model was trained using HuggingFace's PyTorch framework. All training was done on a Tesla P100, provided by Kaggle. Training metrics were logged via Tensorboard. The model achieves the following results on evaluation: The following hyperparameters were used during training: - learningrate: 3e-05 - trainbatchsize: 32 - evalbatchsize: 32 - seed: 42 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 128 - optimizer: Adam with betas=(0.9,0.999) and…

Model · Question answering

deberta-v3-large-squad2

Deepset

This is the deberta-v3-large model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Extractive Question Answering. Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack: For a complete example with an extractive question answering pipeline that scales over many documents, check out the corresponding Haystack tutorial. Evaluated on the SQuAD 2.0 dev set with the official eval script. deepset is the company behind the production-ready…

Model · Object detection

yolov5m-license-plate

Kerem

yolov5 - yolo - vision - object-detection - pytorch libraryname: yolov5 libraryversion: 7.0.6 - keremberke/license-plate-object-detection - Install yolov5: - Finetune the model on your custom dataset

Model · Text generation

mvp

AI Box

The MVP model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen. The detailed information and instructions can be found https://github.com/RUCAIBox/MVP. MVP is supervised pre-trained using a mixture of labeled datasets. It follows a standard Transformer encoder-decoder architecture. MVP is specially designed for natural language generation and can be adapted to a wide range of generation tasks, including but not limited to summarization, data-to-text generation, open-ended dialogue system, story generation, question answering, question generation, task-oriented dialogue system, commonsense…

Status: training in progress. No weights are published yet — this card describes the recipe and the pilot results that motivate it. A ~1B masked-diffusion language model decoded with confidence-targeted steps, then spend a few extra passes rewriting only the tokens the model is least sure about. The point is inference cost. An autoregressive model needs one sequential forward pass per token. This one needs ~20 passes for a whole sequence, regardless of its length. Cost is K + R forward passes. One refill pass fixes any number of positions at once, because the model processes the whole sequence in parallel — that is what makes targeted repair cheaper than more denoising. Draft and refill are…

Model · Robotics

Alpamayo-1.5-10B

Z Lab

Flash Vision-Language-Action Inference for Autonomous Driving FlashDrive accelerates Alpamayo 1.5 — one of NVIDIA's 10B-parameter vision-language-action models for autonomous driving — by 4.7× with no loss in accuracy, through streaming inference, DFlash speculative reasoning, ParoQuant W4A8 quantization, adaptive action caching, and torch.compile. This repository mirrors the weights of nvidia/Alpamayo-1.5-10B and is the base checkpoint of the FlashDrive stack. Loading it pulls the derived companions automatically: Install FlashDrive, then load this base checkpoint — the -PARO and -DFlash companions are fetched automatically: The first call per stream only prefills the KV cache and returns…