Open-Weight Models Under Apache License 2.0

Model · Any to any

gemma-4-E2B-it-qat-q4_0-unquantized

Google

Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages. Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-suited for tasks like text generation, coding, and reasoning. The models are available in five distinct sizes: E2B, E4B, 12B, 26B A4B, and 31B. Their diverse sizes make them deployable in environments ranging from…

Model · Image and text to text

gemma-4-Ortenzya-The-Creative-Wordsmith-31B-it-uncensored-heretic-GGUF

LLMfan46

Creating these models takes significant time, work and compute. If you find them useful consider supporting me: Your help will motivate me and would go into further improving my workflow and coverings fees for storage, compute and may even help uncensoring bigger model with rental Cloud GPUs. GGUF quantizations of llmfan46/gemma-4-Ortenzya-The-Creative-Wordsmith-31B-it-uncensored-heretic. attn.oproj Lower refusals indicate fewer content restrictions, while lower KL divergence indicates more closeness to the original model's baseline. Higher refusals cause more rejections, objections, pushbacks, lecturing, censorship, softening and deflections. The aim of this finetune was to improve this…

Model · Text to video

wan-gguf

Cαlcμ

drag gguf to >./ComfyUI/models/diffusionmodels - drag t5xxl-um to >./ComfyUI/models/textencoders - drag vae to >./ComfyUI/models/vae - for i2v model, drag clip-vision-h to >./ComfyUI/models/clipvision - run the.bat file in the main directory (assume you are using gguf pack below) - if you opt to use fp8 scaled umt5xxl encoder (if applies to any fp8 scale t5 actually), please use cpu offload (switch from default to cpu under device in gguf clip loader; won't affect speed); btw, it works fine for both gguf umt5xxl and gguf vae - drag any demo video (below) to > your browser for workflow - pig is a lazy architecture for gguf node; it applies to all model, encoder and vae gguf file(s); if you…

Model · Any to any

Huihui-gemma-4-12B-it-qat-q4_0-unquantized-abliterated-GGUF

Huihui.ai

This is an uncensored version of google/gemma-4-12B-it-qat-q40-unquantized created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. Note: For this model, both the thinking mode and the non-thinking mode have been completely abliterated. Only layers 23-36 have been abliterated. Please use the latest version of ollama You can use huihuiai/gemma-4-abliterated:12b-qat directly, Please use the latest version of ggml-org/llama.cpp - Risk of Sensitive or Controversial Outputs: This model’s safety filtering has been significantly reduced, potentially…

Model · Image segmentation

beit-base-finetuned-ade-640-640

Microsoft

BEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on ADE20k (an important benchmark for semantic segmentation of images) at resolution 640x640. It was introduced in the paper BEIT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong and Furu Wei and first released in this repository. Disclaimer: The team releasing BEiT did not write a model card for this model so this model card has been written by the Hugging Face team. The BEiT model is a Vision Transformer (ViT), which is a transformer encoder model (BERT-like). In contrast to the original ViT model, BEiT is pretrained on a large…

Model · Time series forecasting

granite-timeseries-flowstate-r1

IBM Granite

FlowState is the first time-scale adjustable Time Series Foundation Model (TSFM), open-sourced by IBM Research. Combining a State Space Model (SSM) Encoder with a Functional Basis Decoder allows FlowState to transition into a timescale invariant coefficient space and make a continuous forecast from this space. This allows FlowState to seamlessly adjust to all possible sampling rates. Therefore, training in one time-scale helps for inference at all scales, allowing for drastically improved utilization of training data across time-scales. This innovation leads to a significant improvement in performance, making FlowState the new state-of-the art in zero-shot time series forecasting.…

Model · Audio classification

wav2vec2-lg-xlsr-en-speech-emotion-recognition

Enrique Hernández Calabrés

The model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-english for a Speech Emotion Recognition (SER) task. The dataset used to fine-tune the original pre-trained model is the RAVDESS dataset. This dataset provides 1440 samples of recordings from actors performing on 8 different emotions in English, which are: It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 4 - evalbatchsize: 4 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 8 - lrschedulertype: linear - numepochs: 3 - mixedprecisiontraining: Native AMP Any doubt, contact me on Twitter. - Transformers 4.8.2…

Model · Text to video

minimax-h3-spatial-physics-lora

Hpmg

空间思维与物理逻辑 LoRA，基于 MiniMax-H3（Comfy-Org/MiniMax-H3）训练，让模型学会纯物体的空间关系与物理运动（碰撞、堆叠、掉落、遮挡等）。 目前还是训练和测试阶段，一些素材片段以及打标问题导致LORA还不是特别稳定。下一阶段准备修复后重新训练。目前LORA也是可以使用，强度建议0.3 ~ 0.5。纯属是在原模型基础上稍微增强一点物理反馈。 最新是重新训练到了wushuspatialphysicsclean3000pruned.safetensors 版本。 实测用了这个LORA，视频整体提升真实感物理的逻辑，比如物体碰撞的真实反馈。也可以用于一些打斗场景，人物真实碰撞的效果。 用了空间物理LORA的武打片段（强度 0.3） 没有用空间物理LORA的武打片段（同样提示词） 1. 下载.safetensors，放入 ComfyUI models/loras/ 2. LoraLoader 加载，strength 建议 0.8~1.0 3. 用空间/物理语言 prompt 描述物体运动 - several colored objects 多个彩色物体（CLEVRER 风格） - rigid objects 刚体 / elastic objects 弹性物体 - metal objects 金属物体 - a ball / balls 球 - billiard balls 台球 - objects 通用物体 - on a table 桌面上（PhyCo 台球场景） - in a synthetic scene 合成场景（CLEVRER 风格）…

Model · Time series forecasting

Toto-2.0-313m

Datadog

Toto (Time Series Optimized Transformer for Observability) is a family of time series foundation models for multivariate forecasting developed by Datadog. Toto 2.0 is the current generation, featuring u-μP-scaled transformers ranging from 4m to 2.5B parameters, all trained from a single recipe. Forecast quality improves reliably with parameter count across the family. The family sets a new state of the art on three forecasting benchmarks: BOOM, our observability benchmark; GIFT-Eval, the standard general-purpose benchmark; and the recent contamination-resistant TIME benchmark. Inference code is available on GitHub. For more examples, see the Quick Start notebook and GluonTS integration…

Model · Time series forecasting

granite-timeseries-ttm-r1

IBM Granite

TinyTimeMixers (TTMs) are compact pre-trained models for Multivariate Time-Series Forecasting, open-sourced by IBM Research. With less than 1 Million parameters, TTM (accepted in NeurIPS 24) introduces the notion of the first-ever “tiny” pre-trained models for Time-Series Forecasting. TTM outperforms several popular benchmarks demanding billions of parameters in zero-shot and few-shot forecasting. TTMs are lightweight forecasters, pre-trained on publicly available time series data with various augmentations. TTM provides state-of-the-art zero-shot forecasts and can easily be fine-tuned for multi-variate forecasts with just 5% of the training data to be competitive. Refer to our paper for…

Model · Image segmentation

PP-DocLayoutV3

PaddlePaddle

Unified Layout Module for PaddleOCR-VL 1.5/1.6 & GLM-OCR. This is the PP-Doclayoutv3 model weights for the PaddlePaddle framework. Get safetensors weights at PP-DocLayoutV3safetensors PP-DocLayoutV3 is specifically engineered to handle non-planar document images. It can directly predict multi-point bounding boxes for layout elements—as opposed to standard two-point boxes—and determine logical reading orders for skewed and curved surfaces within a single forward pass, significantly reducing cascading errors. This model is an essential component of PaddleOCR-VL-1.5, providing crucial layout analysis for the high-precision parsing of various real-world documents in PaddleOCR-VL. This work has…

Model · Audio classification

wav2vec-vm-finetune

Jake Downie

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m for voicemail detection. It is trained on a dataset of call recordings to distinguish between voicemail greetings and live human responses. This model builds on wav2vec2-xls-r-300m, a self-supervised speech model trained on large-scale multilingual data. We fine-tuned it on the first two seconds of a call. - Automated voicemail detection in AI-powered call assistants. - Filtering voicemail responses in customer service and sales call automation. - Only trianed on the English language. - Assumes the voicemail track is isolated and contains no audio from the caller. - Designed for the first two seconds of audio when calling a…

Model · Any to any

gemma-4-E2B-it-AWQ-INT4

Cyankiwi

Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on small models) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages. Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-suited for tasks like text generation, coding, and reasoning. The models are available in four distinct sizes: E2B, E4B, 26B A4B, and 31B. Their diverse sizes make them deployable in environments ranging from high-end phones…

Model · Object detection

deformable-detr

SenseTime

Deformable DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper Deformable DETR: Deformable Transformers for End-to-End Object Detection by Zhu et al. and first released in this repository. Disclaimer: The team releasing Deformable 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 · Translation

Sugoi-14B-Ultra-GGUF

Sugoi Toolkit

Unleashing the full potential of the previous sugoi 14B model, Sugoi 14B Ultra delivers near-double translation accuracy compared to its quantized predecessor—achieving a BLEU score of 21.38 vs 13.67. Its prompt-following skills rival those of Qwen 2.5 Base, especially when handling the bracket-heavy text commonly found in RPG Maker projects. - Key Improvements Nearly 2× BLEU score boost over previous quantized version (21.38 vs 13.67). Stronger prompt adherence, especially with RPGM-style bracketed text. - Ideal Use Cases Japanese → English translation—especially for game dialogue or RPG text. Interactive environments—works well with chat UIs like LM Studio. Must include a system prompt…

Model · Text to video

Wan2.2-Fun-Reward-LoRAs

Alibaba-PAI

We explore the Reward Backpropagation technique 1 2 to optimized the generated videos by Wan2.2-Fun for better alignment with human preferences. We provide the following pre-trained models (i.e. LoRAs) along with the training script. You can use these LoRAs to enhance the corresponding base model as a plug-in or train your own reward LoRA. For more details, please refer to our GitHub repo. A panda eats bamboo while a monkey swings from branch to branch A dog runs through a field while a cat climbs a tree A penguin waddles on the ice, a camel treks by Pig with wings flying above a diamond mountain Set lorapath along with loraweight for the low noise reward LoRA, while specifying lorahighpath…

Model · Translation

Hy-MT2-30B-A3B

Tencent

English ｜ 中文 Hy-MT2 is a family of “fast-thinking” multilingual translation models designed for complex real-world scenarios. It includes three model sizes: 1.8B, 7B, and 30B-A3B (MoE), all of which support translation among 33 languages and effectively follow translation instructions in multiple languages. For on-device deployment, AngelSlim 1.25-bit extreme quantization reduces the storage requirement of the 1.8B model to only 440 MB and improves inference speed by 1.5x. Multi-dimensional evaluations show that Hy-MT2 delivers outstanding performance across general, real-world business, domain-specific, and instruction-following translation tasks. The 7B and 30B-A3B models outperform…

Model · Audio classification

ced-base

Speech Team, Xiaomi MiLM Plus

CED are simple ViT-Transformer-based models for audio tagging, achieving sota performance on Audioset. Notable differences from other available models include: 1. Simplification for finetuning: Batchnormalization of Mel-Spectrograms. During finetuning one does not need to first compute mean/variance over the dataset, which is common for AST. 1. Support for variable length inputs. Most other models use a static time-frequency position embedding, which hinders the model's generalization to segments shorter than 10s. Many previous transformers simply pad their input to 10s in order to avoid the performance impact, which in turn slows down training/inference drastically. 1. Training/Inference…

Model · Object detection

conditional-detr-resnet-50

Microsoft

Conditional DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper Conditional DETR for Fast Training Convergence by Meng et al. and first released in this repository. The recently-developed DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the critical issue, slow training convergence, and present a conditional cross-attention mechanism for fast DETR training. Our approach is motivated by that the cross-attention in DETR relies highly on the content embeddings for localizing the four extremities and…

Model · Image to text

PP-OCRv5_mobile_det_onnx

PaddlePaddle

PP-OCRv5mobiledet is one of the PP-OCRv5det series, the latest generation of text detection models developed by the PaddleOCR team. It aims to efficiently and accurately supports the detection of text in diverse scenarios—including handwriting, vertical, rotated, and curved text—across multiple languages such as Simplified Chinese, Traditional Chinese, English, and Japanese. Key features include robust handling of complex layouts, varying text sizes, and challenging backgrounds, making it suitable for practical applications like document analysis, license plate recognition, and scene text detection. The key accuracy metrics are as follow

Model · Image to text

en_PP-OCRv4_mobile_rec

PaddlePaddle

enPP-OCRv4mobilerec is a text line recognition model within the PP-OCRv4rec series, developed by the PaddleOCR team. The enPP-OCRv4mobilerec model is an English-specific model trained based on PP-OCRv4mobilerec, 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 · Image to text

PP-OCRv4_server_rec

PaddlePaddle

PP-OCRv4serverrec is a text line recognition model within the PP-OCRv4rec series, developed by the PaddleOCR team. PP-OCRv4 is an upgrade over PP-OCRv3. The overall framework retains the same pipeline as PP-OCRv3, with optimizations made to several modules such as data, network structure, and training strategy for both detection and recognition models. It supports text line recognition in general Chinese and English scenarios, but mainly focuses on Chinese. 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…

Model · Zero-shot classification

ModernBERT-large-nli

Tasksource

This model is ModernBERT multi-task fine-tuned on tasksource NLI tasks, including MNLI, ANLI, SICK, WANLI, doc-nli, LingNLI, FOLIO, FOL-NLI, LogicNLI, Label-NLI and all datasets in the below table). This is the equivalent of an "instruct" version. The model was trained for 200k steps on an Nvidia A30 GPU. It is very good at reasoning tasks (better than llama 3.1 8B Instruct on ANLI and FOLIO), long context reasoning, sentiment analysis and zero-shot classification with new labels. The following table shows model test accuracy. These are the scores for the same single transformer with different classification heads on top. Further gains can be obtained by fine-tuning on a single-task, e.g.…

Model · Image to text

pix2struct-base

Google

This model is the pretrained version of Pix2Struct, use this model for fine-tuning purposes only. Pix2Struct is an image encoder - text decoder model that is trained on image-text pairs for various tasks, including image captionning and visual question answering. The full list of available models can be found on the Table 1 of the paper: The abstract of the model states that: forms. Perhaps due to this diversity, previous work has typically relied on domainspecific recipes with limited sharing of the underlying data, model architectures, and objectives. We present Pix2Struct, a pretrained image-to-text model for purely visual language understanding, which can be finetuned on tasks…

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 · 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 · 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 · 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 · 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…

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 · 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…

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 · 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 · 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 · Image segmentation

ormbg-ONNX

ONNX Community

If you haven't already, you can install the Transformers.js JavaScript library from NPM using

Model · Image segmentation

clipseg-rd64

CAMPUS INSTITUTE DATA SCIENCE (CIDAS)

CLIPSeg model with reduce dimension 64. It was introduced in the paper Image Segmentation Using Text and Image Prompts by Lüddecke et al. and first released in this repository. This model is intended for zero-shot and one-shot image segmentation. Refer to the documentation.

Model · Zero-shot classification

deberta-v3-base-tasksource-nli

Damien Sileo

NOTE Deprecated: use https://huggingface.co/tasksource/deberta-small-long-nli for longer context and better accuracy. This is DeBERTa-v3-base fine-tuned with multi-task learning on 600+ tasks of the tasksource collection. 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] - Hundreds of previous tasks with tasksource-adapters [TA]. - Further fine-tuning on a new task or tasksource task (classification, token classification or multiple-choice) [FT]. NLI training data of this model includes label-nli, a NLI dataset specially…

This is a ported version of The base model is wav2vec2-base, which is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. For more information refer to SUPERB: Speech processing Universal PERformance Benchmark Keyword Spotting (KS) detects preregistered keywords by classifying utterances into a predefined set of words. The task is usually performed on-device for the fast response time. Thus, accuracy, model size, and inference time are all crucial. SUPERB uses the widely used Speech Commands dataset v1.0 for the task. The dataset consists of ten classes of keywords, a class for silence, and an unknown class to include the…

Model · Summarization

distilbart-cnn-6-6

Sam Shleifer

This checkpoint should be loaded into BartForConditionalGeneration.frompretrained. See the BART docs for more information.

Model · Image and text to text

Qwen3.8-Flash-Next-Uncensored-NVFP4

OrcaRouter

This model has had its safety alignment substantially removed via abliteration (orthogonalizing the refusal direction out of the residual stream). It will comply with harmful, unethical, or illegal requests the original Qwen3.8-Flash-Next would refuse. Released strictly for legitimate research — interpretability, AI-safety / refusal-mechanism study, red-teaming, and robustness evaluation. You assume full responsibility for how you use it and everything it generates; add your own safety and moderation layers before any deployment. Use must comply with the Apache 2.0 License inherited from the base model and all applicable law. The authors accept no liability for misuse. - A Blackwell GPU…

Model · Audio classification

speech-emotion-recognition-with-openai-whisper-large-v3

Muhammad Firdho

This project leverages the Whisper model to recognize emotions in speech. The goal is to classify audio recordings into different emotional categories, such as Happy, Sad, Surprised, and etc. The dataset used for training and evaluation is sourced from multiple datasets, including: The dataset contains recordings labeled with various emotions. Below is the distribution of the emotions in the dataset: This distribution reflects the balance of emotions in the dataset, with some emotions having more samples than others. Excluded the "calm" emotion during training due to its underrepresentation. The model used is the Whisper Large V3 model, fine-tuned for audio classification tasks: I map the…

Model · Audio classification

Deepfake-audio-detection

Mohammed Abdeldayem

This model is a fine-tuned version of mo-thecreator/wav2vec2-base-finetuned on the None dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 3e-05 - trainbatchsize: 8 - evalbatchsize: 8 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 32 - lrschedulertype: linear - lrschedulerwarmupratio: 0.1 - numepochs: 5 - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2 - mo-thecreator

Model · Audio classification

wav2vec2-deepfake-voice-detector

Gary Stafford

Fine-tuned Wav2Vec2 model for detecting AI-generated speech. Determines if audio was spoken by a human or created by AI text-to-speech/voice cloning software. Fine-tuned Wav2Vec2 transformer for binary audio classification (real vs AI-generated speech). Trained to distinguish authentic human speech from synthetic audio generated by AI text-to-speech and voice cloning services including: Note: This model uses transfer learning from a base model already trained for deepfake detection. Fast convergence is expected due to task similarity and TTS engine overlap with the base model's training data. The model outputs logits (raw, unnormalized scores) for two classes: Apply softmax to convert raw…

However, we observe that the speed and accuracy of YOLOs are negatively affected by the NMS. Recently, end-to-end Transformer-based detectors (DETRs) have provided an alternative to eliminating NMS. Nevertheless, the high computational cost limits their practicality and hinders them from fully exploiting the advantage of excluding NMS. In this paper, we propose the Real-Time DEtection TRansformer (RT-DETR), the first real-time end-to-end object detector to our best knowledge that addresses the above dilemma. We build RT-DETR in two steps, drawing on the advanced DETR: first we focus on maintaining accuracy while improving speed, followed by maintaining speed while improving accuracy.…

Paper: Zheng et al., 2025, “X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model” (arXiv:2510.10274) Successful generalist Vision-Language-Action (VLA) models rely on effective training across diverse robotic platforms with large-scale, cross-embodiment, heterogeneous datasets. To facilitate and leverage the heterogeneity in rich robotic data sources, X-VLA introduces a Soft Prompt approach with minimally added parameters: we infuse prompt-learning concepts into cross-embodiment robot learning, introducing separate sets of learnable embeddings for each distinct embodiment. These embodiment-specific prompts empower VLA models to exploit cross-embodiment…

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 · 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 · 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…

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 · 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