Open-Weight Models Under Apache License 2.0

Model · Any to any

gemma-4-E4B-it-ultra-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-E4B-it-ultra-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. PIQA (Physical Intuition Question Answering) a ~1,800 questions tests…

Model · Time series forecasting

granite-timeseries-ttm-r3

IBM Granite

Building on top of TTM-R1 and TTM-R2, we introduce the next generation of TinyTimeMixer under the Granite time-series foundation model family — Granite-TTM-R3. This release incorporates several novel tiny-neural architectural innovations designed to push the limits of accuracy in high-speed forecasting, a critical requirement for real-world production deployments. Granite-TTM-R3 is a family of pretrained models supporting multiple real-world forecasting scenarios: - Zero-shot forecasting across unseen datasets - Few-shot adaptation effective with as few as ~1K samples - Full fine-tuning for domain-specific optimization - Multivariate time-series forecasting - Exogenous / control variable…

Model · Object detection

deformable-detr-DocLayNet

Aryn Inc.

Deformable DEtection TRansformer (DETR), trained on DocLayNet (including 80k annotated pages in 11 classes). You can use this model in the serverless Aryn Partitioning Service. You can get started here 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 an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100. The model is trained using a "bipartite…

Model · Any to any

MiniCPM-o-4_5-awq

OpenBMB

A Gemini 2.5 Flash Level MLLM for Vision, Speech, and Full-Duplex Mulitmodal Live Streaming on | CaseBook(Audio, Omni Full-Duplex) MiniCPM-o 4.5 is the latest and most capable model in the MiniCPM-o series. The model is built in an end-to-end fashion based on SigLip2, Whisper-medium, CosyVoice2, and Qwen3-8B with a total of 9B parameters. It exhibits a significant performance improvement, and introduces new features for full-duplex multimodal live streaming. Notable features of MiniCPM-o 4.5 include: - Leading Visual Capability. MiniCPM-o 4.5 achieves an average score of 77.6 on OpenCompass, a comprehensive evaluation of 8 popular benchmarks. With only 9B parameters, it surpasses widely…

Model · Text to speech

Kokoro-82M-ONNX

ONNX Community

Kokoro is a frontier TTS model for its size of 82 million parameters (text in/audio out). First, install the kokoro-js library from NPM using: You can then generate speech as follows: Optionally, save the audio to a file: The model is resilient to quantization, enabling efficient high-quality speech synthesis at a fraction of the original model size.

Model · Summarization

text_summarization

Falcons.ai

The Fine-Tuned T5 Small is a variant of the T5 transformer model, designed for the task of text summarization. It is adapted and fine-tuned to generate concise and coherent summaries of input text. The model, named "t5-small," is pre-trained on a diverse corpus of text data, enabling it to capture essential information and generate meaningful summaries. Fine-tuning is conducted with careful attention to hyperparameter settings, including batch size and learning rate, to ensure optimal performance for text summarization. During the fine-tuning process, a batch size of 8 is chosen for efficient computation and learning. Additionally, a learning rate of 2e-5 is selected to balance convergence…

Model · Any to any

gemma-4-12B-it-FP8-dynamic

Thor Lin

Self-quantized FP8 (dynamic) of google/gemma-4-12B-it — Google's encoder-free omni model (text + image + audio + video). Quantized and benchmarked on an NVIDIA DGX Spark (GB10, sm121a). TL;DR: 13 GB on disk (from 23 GB BF16), 15.9 tok/s on a GB10 via vLLM, all four modalities intact. Data-free — no calibration needed. If you want the smallest + fastest build, see the sibling NVFP4 weight-only repo. FP8 is the conservative choice (dynamic activations, no calibration, widest kernel support). I scored all three formats on MMLU (English, 57 subjects) and TMMLU+ (Traditional Chinese, 66 subjects) with lm-evaluation-harness, 5-shot, chat template applied, limit=30 (N ≈ 1,710 EN / 1,980 TC, ±~1.0…

Model · Text to speech

Soprano-1.1-80M

Eugene Kwek

2026.01.14 - Soprano-1.1-80M released! 95% fewer hallucinations and a 63% preference rate over Soprano-80M. 2026.01.13 - Soprano-Factory released! You can now train/fine-tune your own Soprano models. 2025.12.22 - Soprano-80M released! Code | Demo Soprano is an ultra‑lightweight, on-device text‑to‑speech (TTS) model designed for expressive, high‑fidelity speech synthesis at unprecedented speed. Soprano was designed with the following features: - Up to 2000x real-time generation on GPU and 20x real-time on CPU - Lossless streaming with one plus one, etc) If Soprano produces unsatisfactory results, you can easily regenerate it for a new, potentially better generation. You may also change the…

Model · Zero-shot classification

nli-deberta-v3-large

Sentence Transformers - Cross-Encoders

This model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-large The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral. For futher evaluation results, see SBERT.net - Pretrained Cross-Encoder. Pre-trained models can be used like this: You can use the model also directly with Transformers library (without SentenceTransformers library): This model can also be used for zero-shot-classification

Model · Image to text

PP-OCRv5_mobile_det

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: Please refer to the following commands to install…

Model · Translation

opus-mt-da-en

Helsinki-NLP Research Group

source languages: da; target languages: en; OPUS readme: da-en; dataset: opus; model: transformer-align; pre-processing: normalization + SentencePiece.

Model · Text to image

girlslike-zimage

Ifmylove2011

用于测试基于 Z-Image Base/Turbo BF16 训练的人物 LoKR / LoRA，仅代表个人审美与训练效果。实际生成结果仅供测试，请勿用于冒充、欺骗或其他不当用途。 后缀为zi代表基于z-image base训练，zit则代表基于turbo训练，zi在base与turbo模型中均可使用，zit则只能在turbo中使用。 z-image base训练尚无较为通用的可行方案，目前本zi系列选择的方案是全秩lokr，可在与turbo模型配合使用时以权重1.0出图，也能适当增加权重以增强相似性，最好不要超过1.5。 而在与base模型或相关微调模型配合使用时，可适当降低权重。 基于base训练的zi lokr在turbo模型中使用时，质感会更接近turbo本身（应该是base模型本身低噪部分难以被"污染"的缘故），中远景图中的人物相似度也会比zit好一些，近景则更"平淡"。 但总体来说兼容性存疑，比如解剖学问题仍然大量存在（在base模型上生图就存在类似问题，也影响到了训练，会让肢体问题重新变得不稳定又棘手），其他类似的问题都还在测试中，后续有更好的方案会继续更新。 z-image turbo bf16； ersde + sgmuniform | euler + flowmatch； cfg=1； lora权重0.5~1.5； 无其他lora参与。 鉴于z-image…

Model · Any to any

gemma-4-12B-it-qat-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 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 · Any to any

gemma-4-12B-it-GGUF

GGML Org

Run with https://llama.app - https://huggingface.co/google/gemma-4-12B-it - https://huggingface.co/google/gemma-4-12B-it-assistant - https://huggingface.co/google/gemma-4-12B-it-qat-q40-unquantized-assistant - https://huggingface.co/google/gemma-4-12B-it-qat-q40-unquantized - add info - add dflash

Model · Translation

opus-mt-pl-en

Helsinki-NLP Research Group

source languages: pl; target languages: en; OPUS readme: pl-en; dataset: opus; model: transformer-align; pre-processing: normalization + SentencePiece.

Model · Text to speech

Kokoro-Vietnamese

ContextBoxAI

Fine-tuned Vietnamese Kokoro TTS artifacts. - kokorovi.pth: PyTorch Kokoro KModel checkpoint for inference. - kokorovi.onnx: ONNX Runtime export of the acoustic model. - kokorovivoicepack.pt: default Vietnamese voicepack. - config.json: Kokoro config/vocab used by both PyTorch and ONNX inference. - voicepacks/.pt: additional Vietnamese voicepacks. The ONNX CLI downloads kokorovi.onnx, kokorovivoicepack.pt, and config.json from this repository when local paths are not provided. Install onnxruntime-gpu and pass --device cuda to use CUDAExecutionProvider when available. Vietnamese G2P is handled by vig2p, matching the GitHub inference and training code.

Model · Translation

opus-mt-fi-en

Helsinki-NLP Research Group

hfname: fin-eng - sourcelanguages: fin - targetlanguages: eng - opusreadmeurl: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fin-eng/README.md - originalrepo: Tatoeba-Challenge - srcconstituents: {'fin'} - tgtconstituents: {'eng'} - srcmultilingual: False - tgtmultilingual: False - urlmodel: https://object.pouta.csc.fi/Tatoeba-MT-models/fin-eng/opus-2020-08-05.zip - urltestset: https://object.pouta.csc.fi/Tatoeba-MT-models/fin-eng/opus-2020-08-05.test.txt - srcalpha3: fin - tgtalpha3: eng - shortpair: fi-en - chrF2score: 0.6970000000000001 - brevitypenalty: 0.99 - reflen: 74651.0 - srcname: Finnish - tgtname: English - traindate: 2020-08-05 - srcalpha2: fi…

Model · Summarization

distilbart-xsum-12-6

Sam Shleifer

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

Model · Any to any

MiniCPM-o-4_5-gguf

OpenBMB

A Gemini 2.5 Flash Level MLLM for Vision, Speech, and Full-Duplex Mulitmodal Live Streaming on | CaseBook(Audio, Omni Full-Duplex) MiniCPM-o 4.5 is the latest and most capable model in the MiniCPM-o series. The model is built in an end-to-end fashion based on SigLip2, Whisper-medium, CosyVoice2, and Qwen3-8B with a total of 9B parameters. It exhibits a significant performance improvement, and introduces new features for full-duplex multimodal live streaming. Notable features of MiniCPM-o 4.5 include: - Leading Visual Capability. MiniCPM-o 4.5 achieves an average score of 77.6 on OpenCompass, a comprehensive evaluation of 8 popular benchmarks. With only 9B parameters, it surpasses widely…

Model · Translation

opus-mt-ja-en

Helsinki-NLP Research Group

source languages: ja; target languages: en; OPUS readme: ja-en; dataset: opus; model: transformer-align; pre-processing: normalization + SentencePiece.

ComfyUI-key repackaging of the official 8-step PDD acceleration LoRAs for MiniMax-H3 — full audio+video generation in 8 (or 4) sampler steps, CFG-free. These are not plain LoRAs. Each file carries a rank-64 trunk LoRA plus a Parallel Decoding Distillation head bank (32 per-interval final-layer projections per modality, PDD — arXiv:2607.26004). Loading them requires the (also loads the original alibaba-pai files directly — this repo just saves you the in-memory conversion and gives you inspectable standard LoRA keys). Put the LoRA files in ComfyUI/models/pddacc/. Pair FL2VA with an fl2va UNET, Ref2VA with ref2va (bf16 or int8-convrot builds both work). The baked checkpoint goes in…

Model · Audio classification

emotion-recognition-wav2vec2-IEMOCAP

SpeechBrain

This repository provides all the necessary tools to perform emotion recognition with a fine-tuned wav2vec2 (base) model using SpeechBrain. It is trained on IEMOCAP training data. For a better experience, we encourage you to learn more about SpeechBrain. The model performance on IEMOCAP test set is: This system is composed of an wav2vec2 model. It is a combination of convolutional and residual blocks. The embeddings are extracted using attentive statistical pooling. The system is trained with Additive Margin Softmax Loss. Speaker Verification is performed using cosine distance between speaker embeddings. The system is trained with recordings sampled at 16kHz (single channel). The code will…

The RT-DETRv2 model was proposed in RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer by Wenyu Lv, Yian Zhao, Qinyao Chang, Kui Huang, Guanzhong Wang, Yi Liu. RT-DETRv2 refines RT-DETR by introducing selective multi-scale feature extraction, a discrete sampling operator for broader deployment compatibility, and improved training strategies like dynamic data augmentation and scale-adaptive hyperparameters. These changes enhance flexibility and practicality while maintaining real-time performance. This model was contributed by @jadechoghari with the help of @cyrilvallez and @qubvel-hf This is RT-DETRv2 consistently outperforms its predecessor across all…

Model · Translation

opus-mt-bg-en

Helsinki-NLP Research Group

source languages: bg; target languages: en; OPUS readme: bg-en; dataset: opus; model: transformer-align; pre-processing: normalization + SentencePiece.

Model · Zero-shot classification

nli-distilroberta-base

Sentence Transformers - Cross-Encoders

This model was trained using SentenceTransformers Cross-Encoder class. The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral. For evaluation results, see SBERT.net - Pretrained Cross-Encoder. Pre-trained models can be used like this: You can use the model also directly with Transformers library (without SentenceTransformers library): This model can also be used for zero-shot-classification

This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 32 - evalbatchsize: 32 - lrschedulertype: linear - numepochs: 2 - Transformers 4.28.0 - Pytorch 2.0.0 - Datasets 2.12.0 - Tokenizers 0.13.3

Model · Object detection

dfine-xlarge-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…

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.…

Model · Time series forecasting

granite-timeseries-patchtst-fm-r1

IBM Granite

PatchTST was originally released prior to the interest in creating pre-trained, zero-shot time series foundation models that were capable of state-of-the-art performance on out of sample datasets. PatchTST-FM (patched time-series transformer-based foundation model) essentially has the architectural simplicity of PatchTST, but differs in some crucial ways. Coupled with a revised training strategy and a significantly larger training corpus, we are able to train a model that achieves state-of-the-art results on GiftEval (see below for recommended filters to view this on the leaderboard). The architecture incorporates the following changes: - residual blocks in the input and output projections…

Model · Text to video

Wan2.2-I2V-A14B-Lightning-Diffusers

Mage.Space

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 T2V-A14B model, which supports generating 5s videos at both 480P and 720P resolutions. Built with a Mixture-of-Experts (MoE) architecture, it delivers outstanding video generation quality. On our new benchmark Wan-Bench 2.0, the model surpasses leading commercial models across most key evaluation dimensions. 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 with us so we can highlight it for the broader community. - Wan2.2…

Model · Any to any

gemma-4-E4B-it-assistant

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 · Object detection

rf-detr-base

Roboflow

RF-DETR is a real-time detection transformer family introduced in RF-DETR: Neural Architecture Search for Real-Time Detection Transformers by Robinson et al. and integrated in Transformers via PR #36895. RF-DETR is an end-to-end object detection model that combines ideas from LW-DETR and Deformable DETR: a DINOv2-with-registers style ViT backbone (with an RF-DETR windowing pattern for efficient attention), a multi-scale projector between encoder and decoder, and a multi-scale deformable DETR decoder for fast convergence and strong accuracy–latency tradeoffs. You can use the raw model for object detection. See the model hub to look for all available RF-DETR models. Here is how to use this…

Model · Tabular classification

mitra-classifier-2

Autogluon

Mitra-v2 classifier is a tabular foundation model that is pre-trained on purely synthetic datasets sampled from a mix of random classifiers, including the new Hybrid SCM prior. It is the second generation of the Mitra classifier (autogluon/mitra-classifier), pre-trained with a 10x longer context, three times as many features, and an improved optimizer. On the TabArena and TALENT benchmarks it delivers state-of-the-art accuracy at the level of TabFM and EXAONE Tabular, while surpassing TabPFN-3 by a wide margin. The regression model is at autogluon/mitra-regressor-2, and the inference and fine-tuning code with our evaluation results is at autogluon/mitra-finetune. Mitra-v2 is based on a…

LW-DETR, a Light-Weight DEtection TRansformer model, is designed to be a real-time object detection alternative that outperforms conventional convolutional (YOLO-style) and earlier transformer-based (DETR) methods in terms of speed and accuracy trade-off. It was introduced in the paper LW-DETR: A Transformer Replacement to YOLO for Real-Time Detection by Chen et al. and first released in this repository. Disclaimer: This model was originally contributed by stevenbucaille in transformers. LW-DETR is an end-to-end object detection model that uses a Vision Transformer (ViT) backbone as its encoder, a simple convolutional projector, and a shallow DETR decoder. The core philosophy is to leverage…

Model obtained by Fine Tuning 'facebook/bart-large-xsum' using AMI Meeting Corpus, SAMSUM Dataset, DIALOGSUM Dataset, XSUM Dataset!

Model · Time series forecasting

chronos-t5-mini

Amazon

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 · Time series forecasting

t0-alpha

The Forecasting Company

t0-alpha is an open-weights time-series forecasting foundation model from The Forecasting Company. t0 is a transformer-based model that produces probabilistic multi-horizon forecasts and natively operates on multiple covariates. t0-alpha is the first public iteration of the model. You can use t0 on Retrocast, The Forecasting Company's platform for forecasting on your own data and comparing forecasts across open-weight models. Model family: t0-alpha (PyTorch/MLX) · French national electricity demand in Retrocast. Data: Enedis open data. t0-alpha is an alpha release intended for research, experimentation, and applied forecasting evaluation. t0-alpha is intended for probabilistic time-series…

Model · Question answering

bert-large-uncased-whole-word-masking-finetuned-squad

BERT community

Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is uncased: it does not make a difference between english and English. Differently to other BERT models, this model was trained with a new technique: Whole Word Masking. In this case, all of the tokens corresponding to a word are masked at once. The overall masking rate remains the same. The training is identical -- each masked WordPiece token is predicted independently. After pre-training, this model was fine-tuned on the SQuAD dataset with one of our fine-tuning scripts. See below for more information regarding this…

Model · Any to any

gemma-4-E2B-it-qat-mobile-GGUF

Unsloth AI

This model ships a Multi-Token Prediction drafter at the repo root (mtp-gemma-4-E2B-it.gguf, a near-lossless smart Q40). A recent llama.cpp auto-discovers it from -hf, so you do not pass --model-draft: The drafter shares the target's KV cache and does not change the output (the target verifies every drafted token). See the MTP/ folder for the other precisions and explicit usage. 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…

Model · Question answering

distilbert-base-uncased-distilled-squad

DistilBERT community

The DistilBERT model was proposed in the blog post Smaller, faster, cheaper, lighter: Introducing DistilBERT, adistilled version of BERT, and the paper DistilBERT, adistilled version of BERT: smaller, faster, cheaper and lighter. DistilBERT is a small, fast, cheap and light Transformer model trained by distilling BERT base. It has 40% less parameters than bert-base-uncased, runs 60% faster while preserving over 95% of BERT's performances as measured on the GLUE language understanding benchmark. This model is a fine-tune checkpoint of DistilBERT-base-uncased, fine-tuned using (a second step of) knowledge distillation on SQuAD v1.1. - See this repository for more about Distil\ (a class of…

Model · Time series forecasting

chronos-t5-tiny

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 · Question answering

bert-large-cased-whole-word-masking-finetuned-squad

BERT community

Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is cased: it makes a difference between english and English. Differently to other BERT models, this model was trained with a new technique: Whole Word Masking. In this case, all of the tokens corresponding to a word are masked at once. The overall masking rate remains the same. The training is identical -- each masked WordPiece token is predicted independently. After pre-training, this model was fine-tuned on the SQuAD dataset with one of our fine-tuning scripts. See below for more information regarding this fine-tuning.…

Model · Translation

opus-mt-nl-fr

Helsinki-NLP Research Group

source languages: nl; target languages: fr; OPUS readme: nl-fr; dataset: opus; model: transformer-align; pre-processing: normalization + SentencePiece.

Model · Image to text

latin_PP-OCRv5_mobile_rec

PaddlePaddle

latinPP-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 Korean. 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: You can quickly experience the…

Model · Image segmentation

rf-detr-seg-nano

Roboflow

RF-DETR is a real-time detection transformer family introduced in RF-DETR: Neural Architecture Search for Real-Time Detection Transformers by Robinson et al. and integrated in Transformers via PR #36895. RF-DETR is an end-to-end instance segmentation model that combines ideas from LW-DETR and Deformable DETR: a DINOv2-with-registers style ViT backbone (with an RF-DETR windowing pattern for efficient attention), a multi-scale projector between encoder and decoder, and a multi-scale deformable DETR decoder extended with an instance-segmentation head. You can use the raw model for instance segmentation; it predicts per-instance masks together with bounding boxes and class scores. See the model…

Model · Translation

opus-mt-fr-de

Helsinki-NLP Research Group

source languages: fr; target languages: de; OPUS readme: fr-de; dataset: opus; model: transformer-align; pre-processing: normalization + SentencePiece.

Model · Translation

opus-mt-sv-en

Helsinki-NLP Research Group

source languages: sv; target languages: en; OPUS readme: sv-en; dataset: opus; model: transformer-align; pre-processing: normalization + SentencePiece.

Model · Image segmentation

beit-large-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 · Translation

opus-mt-de-fr

Helsinki-NLP Research Group

source languages: de; target languages: fr; OPUS readme: de-fr; dataset: opus; model: transformer-align; pre-processing: normalization + SentencePiece.

GGUF packages of Soprano-1.1-80M for audio.cpp. Original model: ekwek1/soprano — an ultra-lightweight (~80M) English-only TTS model by @ekwek1. ` ash python3 tools/modelmanagerv2.py install soprano1180mq80 This conversion is provided as a community package for use with audio.cpp. All rights to the model and its weights belong to the original authors.

BioLinkBERT-large 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…

This model is a fine tuning for the deepfake audio classification task. It achieves the following results on its evalutation data: It achieves the following results on ASVspoof2019 evaluation subset

Model · Text to video

Wan2.1-T2V-14B-gguf

City

This is a direct GGUF conversion of Wan-AI/Wan2.1-T2V-14B All quants are created from the FP32 base file, though I only uploaded FP16 due to it exceeding the 50GB max file limit and gguf-split loading not currently being supported in ComfyUI-GGUF. The model files can be used with the ComfyUI-GGUF custom node. Place model files in ComfyUI/models/unet - see the GitHub readme for further install instructions. The VAE can be downloaded from this repository by Kijai Please refer to this chart for a basic overview of quantization types.

Model · Image and text to text

Qari-OCR-v0.3-VL-2B-Instruct

Network for Advancing Modern ArabicNLP & AI

QARI-OCR v0.3 is a specialized vision-language model fine-tuned for Arabic Optical Character Recognition with a focus on structural document understanding. - Built on Qwen2-VL-2B-Instruct, this model excels at preserving document layouts, HTML tags, and formatting while transcribing Arabic text. - It is described in detail in the paper QARI-OCR: High-Fidelity Arabic Text Recognition through Multimodal Large Language Model Adaptation. While QARI v0.2 achieves better raw text accuracy (CER: 0.061), QARI v0.3 excels in: - HTML/Markdown structure preservation - Document layout understanding - Handwritten text recognition (initial capabilities) - 5x faster training than v0.2 You can load this…

Model · Time series forecasting

sundial-base-128m

THUML @ Tsinghua University

News (2025.08) Sundial has been integrated into Apache IoTDB, a native time-series database. News (2025.06) Sundial has been accepted as ICML 2025 Oral (Top 1%). News (2025.05) Get 1st MASE on the GIFT-Eval Benchmark. News (2025.02) Get 1st MSE/MAE zero-shot performance on Time-Series-Library datasets. Sundial is a family of generative time series foundation models. This version is pre-trained on 1 trillion time points with 128M parameters. For more information, please refer to this paper. [[Slides]](https://cloud.tsinghua.edu.cn/f/8d526337afde465e87c9/) [[Poster]](https://cloud.tsinghua.edu.cn/f/cc2a156315e9453f99b3/) [[Intro (CN)]](https://mp.weixin.qq.com/s/y3sc2e2lmW1sqfnoK-ZdDA). The…

Model · Robotics

smolvla_libero

Hugging Face Vision Language Action Models Research

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 · Text to video

Wan2.1-T2V-1.3B

Wan-AI

In this repository, we present Wan2.1, a comprehensive and open suite of video foundation models that pushes the boundaries of video generation. Wan2.1 offers these key features: This repository hosts our T2V-1.3B model, a versatile solution for video generation that is compatible with nearly all consumer-grade GPUs. In this way, we hope that Wan2.1 can serve as an easy-to-use tool for more creative teams in video creation, providing a high-quality foundational model for academic teams with limited computing resources. This will facilitate both the rapid development of the video creation community and the swift advancement of video technology. Your browser does not support the video tag.…

Model · Translation

t5-11b

T5 community

The developers of the Text-To-Text Transfer Transformer (T5) write: T5-11B is the checkpoint with 11 billion parameters. The developers write in a blog post that the model: See the blog post and research paper for further details. The model is pre-trained on the Colossal Clean Crawled Corpus (C4), which was developed and released in the context of the same research paper as T5. The model was pre-trained on a on a multi-task mixture of unsupervised (1.) and supervised tasks (2.). Thereby, the following datasets were being used for (1.) and (2.): 1. Datasets used for Unsupervised denoising objective: 2. Datasets used for Supervised text-to-text language modeling objective - CoLA Warstadt et…

Example workflow - based on the Comfyui example workflow This is a direct GGUF conversion of Wan-AI/Wan2.1-VACE-14B All quants are created from the FP32 base file, though I only uploaded the Q80 and less, if you want the F16 or BF16 one I would upload it per request. The model files can be used with the ComfyUI-GGUF custom node. Place model files in ComfyUI/models/unet - see the GitHub readme for further install instructions. The VAE can be downloaded from here Please refer to this chart for a basic overview of quantization types. For conversion I used the conversion scripts from city96

Model · Translation

opus-mt-en-ar

Helsinki-NLP Research Group

a sentence initial language token is required in the form of >>id<< (id = valid target language ID) - hfname: eng-ara - sourcelanguages: eng - targetlanguages: ara - opusreadmeurl: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-ara/README.md - originalrepo: Tatoeba-Challenge - srcconstituents: {'eng'} - tgtconstituents: {'apc', 'ara', 'arqLatn', 'arq', 'afb', 'araLatn', 'apcLatn', 'arz'} - srcmultilingual: False - tgtmultilingual: False - urlmodel: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-ara/opus-2020-07-03.zip - urltestset: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-ara/opus-2020-07-03.test.txt - srcalpha3: eng - tgtalpha3: ara - shortpair: en-ar…