Open-Weight Models Under Apache License 2.0

Model · Tabular classification

sap-rpt-1-oss

SAP

Go to SAP-RPT Playground ↗ Note: This model and repository were formerly known as ConTextTab. While the code and repository have now been updated in line with the new name sap-rpt-1-oss, the model checkpoint and functionality remain identical. Implementation of the deep learning model with the inference pipeline described in the paper "ConTextTab: A Semantics-Aware Tabular In-Context Learner". Tabular in-context learning (ICL) has recently achieved state-of-the-art (SOTA) performance on several tabular prediction tasks. Previously restricted to classification problems on small tables, recent advances such as TabPFN and TabICL have extended its use to larger datasets. While being…

Model · Image classification

tf_mobilenetv3_large_minimal_100.in1k

PyTorch Image Models

A MobileNet-v3 image classification model. Trained on ImageNet-1k in Tensorflow by paper authors, ported to PyTorch by Ross Wightman. Explore the dataset and runtime metrics of this model in timm model results.

Use bucket training like novelai, can generate high resolutions images of any aspect ratio - Use large amount of high quality data(over 10000000 images), the dataset covers a diversity of situation - Use re-captioned prompt like DALLE.3, use CogVLM to generate detailed description, good prompt following ability - Use many useful tricks during training. Including but not limited to date augmentation, mutiple loss, multi resolution - Use almost the same parameter compared with original ControlNet. No obvious increase in network parameter or computation. - Support 10+ control conditions, no obvious performance drop on any single condition compared with training independently - Support multi…

Model · Image classification

resnet50_gn.a1h_in1k

PyTorch Image Models

A ResNet-B image classification model. single layer 7x7 convolution with pooling 1x1 convolution shortcut downsample Trained on ImageNet-1k in timm using recipe template described below. Based on ResNet Strikes Back A1 recipe Stronger dropout, stochastic depth, and RandAugment than paper A1 recipe - Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385 Explore the dataset and runtime metrics of this model in timm model results.

Model · Image classification

beitv2_base_patch16_224.in1k_ft_in22k

PyTorch Image Models

A BEiT-v2 image classification model. Trained on ImageNet-1k with self-supervised masked image modelling (MIM) using a VQ-KD encoder as a visual tokenizer (via OpenAI CLIP B/16 teacher). Fine-tuned on ImageNet-22k. - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 Explore the dataset and runtime metrics of this model in timm model results.

Model · Image classification

densenet121.ra_in1k

PyTorch Image Models

A DenseNet image classification model. Pretrained on ImageNet-1k in timm by Ross Wightman using RandAugment RA recipe. Related to B recipe in ResNet Strikes Back.

Model · Image classification

convnextv2-tiny-22k-224

AI at Meta

ConvNeXt V2 model pretrained using the FCMAE framework and fine-tuned on the ImageNet-22K dataset at resolution 224x224. It was introduced in the paper ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders by Woo et al. and first released in this repository. Disclaimer: The team releasing ConvNeXT V2 did not write a model card for this model so this model card has been written by the Hugging Face team. ConvNeXt V2 is a pure convolutional model (ConvNet) that introduces a fully convolutional masked autoencoder framework (FCMAE) and a new Global Response Normalization (GRN) layer to ConvNeXt. ConvNeXt V2 significantly improves the performance of pure ConvNets on various…

Model · Image classification

convnext_small.fb_in22k_ft_in1k_384

PyTorch Image Models

A ConvNeXt image classification model. Pretrained on ImageNet-22k and fine-tuned on ImageNet-1k by paper authors. Explore the dataset and runtime metrics of this model in timm model results. All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP.

This model combines the SpeechBrain ECAPA-TDNN speaker embedding model with an SVM classifier to predict speaker gender from audio input. The model was trained and evaluated on the VoxCeleb2, Mozilla Common Voice v10.0, and TIMIT datasets - Mozilla Common Voice v10.0 English validated test set: 92.3% accuracy The model was trained on VoxCeleb2 dataset: - Converted to WAV format, single channel, 16kHz sampling rate, 256 kp/s bitrate - Applied SileroVAD for voice activity detection, taking the first voiced segment You can install the package directly from GitHub: - Model was trained on celebrity voices from YouTube interviews - Performance may vary on different audio qualities or recording…

Model · Image classification

mobilenetv4_conv_small.e2400_r224_in1k

PyTorch Image Models

A MobileNet-V4 image classification model. Trained on ImageNet-1k by Ross Wightman. Trained with timm scripts using hyper-parameters inspired by the MobileNet-V4 paper with timm enhancements. NOTE: So far, these are the only known MNV4 weights. Official weights for Tensorflow models are unreleased. - MobileNetV4 -- Universal Models for the Mobile Ecosystem: https://arxiv.org/abs/2404.10518

Model · Image classification

convnext_base.clip_laion2b_augreg_ft_in12k_in1k

PyTorch Image Models

A ConvNeXt image classification model. CLIP image tower weights pretrained in OpenCLIP on LAION and fine-tuned on ImageNet-12k followed by ImageNet-1k in timm bby Ross Wightman. Please see related OpenCLIP model cards for more details on pretrain: https://huggingface.co/laion/CLIP-convnextxxlarge-laion2B-s34B-b82K-augreg-soup https://huggingface.co/laion/CLIP-convnextlarged.laion2B-s26B-b102K-augreg https://huggingface.co/laion/CLIP-convnextbasew-laion2B-s13B-b82K-augreg https://huggingface.co/laion/CLIP-convnextbasew320-laionaesthetic-s13B-b82K - Learning Transferable Visual Models From Natural Language Supervision: https://arxiv.org/abs/2103.00020 Explore the dataset and runtime metrics…

Model

blenderbot_small-90M

AI at Meta

Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training…

Model · Image classification

convnext_tiny.fb_in22k_ft_in1k

PyTorch Image Models

A ConvNeXt image classification model. Pretrained on ImageNet-22k and fine-tuned on ImageNet-1k by paper authors. Explore the dataset and runtime metrics of this model in timm model results. All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP.

Model

blenderbot-3B

AI at Meta

Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training…

Model · Question answering

splinter-base

Tel Aviv University

Splinter-base is the pretrained model discussed in the paper Few-Shot Question Answering by Pretraining Span Selection (at ACL 2021). Its original repository can be found here. The model is case-sensitive. Note: This model doesn't contain the pretrained weights for the QASS layer (see paper for details), and therefore the QASS layer is randomly initialized upon loading it. For the model with those weights, see tau/splinter-base-qass. Splinter is a model that is pretrained in a self-supervised fashion for few-shot question answering. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an…

Model · Text to speech

react-native-executorch-kokoro

Software Mansion

This repository hosts the kokoro models exported for the library as ExecuTorch.pte programs, ready to run on device. These files are published for the ExecuTorch v1.4.1 runtime. ExecuTorch gives no forward compatibility guarantee, so an older runtime may fail to load them. To use them in React Native ExecuTorch, pass the model constant shipped in the library's model registry to the corresponding task pipeline. See the To load these files in your own ExecuTorch runtime, read the first. Kokoro synthesizes speech in 8 languages here, including fine-tuned Polish and German voices. The synthesizer and duration predictor support input shape dynamism over a range of 1 to 128 tokens. Synthesis…

Model · Image segmentation

modnet

Joshua

For more information, check out the official repository and example colab. If you haven't already, you can install the Transformers.js JavaScript library from NPM using: You can then use the model for portrait matting, as follows: Or with the AutoModel and AutoProcessor APIs: Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).

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 Emotion Recognition (ER) predicts an emotion class for each utterance. The most widely used ER dataset IEMOCAP is adopted, and we follow the conventional evaluation protocol: we drop the unbalanced emotion classes to leave the final four classes with a similar amount of data points and cross-validate on five folds of the standard splits. For the original model's training and evaluation instructions refer to the You…

Model · Image classification

vit_base_patch16_224.augreg_in21k_ft_in1k

PyTorch Image Models

A Vision Transformer (ViT) image classification model. Trained on ImageNet-21k and fine-tuned on ImageNet-1k (with additional augmentation and regularization) in JAX by paper authors, ported to PyTorch by Ross Wightman. - How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers: https://arxiv.org/abs/2106.10270 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 Explore the dataset and runtime metrics of this model in timm model results.

+ Always use full LoRa name with "vrtlxxxx" trigger in prompt like "Alba Baptista (vrtlalbabaptista) in a swimming pool". "Woman" or "1girl" will NOT work due to my way i do captions. + Add the gender to the prompt for confusing names like "Alex Jones". + Remove the name when internal model knowledge is bad or censored or is confusing to model like "Sandy Cheeks" or "Kate Middleton". + When using a Lora with multiple triggers (vrtlxx,vrtlyy) do not use the real character name but only trigger or a combination of it. "vrtlMain" always combines all trigger-words. Angourie Rice, January Jones, Julianna Guill, Ursula Corbero, Judith Rakers, Alina Merkau, Kiernan Shipka, Leslie Bibb, Marie…

Model · Image classification

resnet50d.ra2_in1k

PyTorch Image Models

A ResNet-D image classification model. 3-layer stem of 3x3 convolutions with pooling 2x2 average pool + 1x1 convolution shortcut downsample Trained on ImageNet-1k in timm using recipe template described below. RandAugment RA2 recipe. Inspired by and evolved from EfficientNet RandAugment recipes. Published as B recipe in ResNet Strikes Back. RMSProp (TF 1.0 behaviour) optimizer, EMA weight averaging Step (exponential decay w/ staircase) LR schedule with warmup - Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385 - Bag of Tricks for Image Classification with Convolutional Neural Networks: https://arxiv.org/abs/1812.01187 Explore the dataset and runtime metrics of…

Model · Translation

opus-mt-en-zh

Helsinki-NLP Research Group

a sentence initial language token is required in the form of >>id<< (id = valid target language ID) - hfname: eng-zho - sourcelanguages: eng - targetlanguages: zho - opusreadmeurl: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-zho/README.md - originalrepo: Tatoeba-Challenge - srcconstituents: {'eng'} - tgtconstituents: {'cmnHans', 'nan', 'nanHani', 'gan', 'yue', 'cmnKana', 'yueHani', 'wuuBopo', 'cmnLatn', 'yueHira', 'cmnHani', 'cjyHans', 'cmn', 'lzhHang', 'lzhHira', 'cmnHant', 'lzhBopo', 'zho', 'zhoHans', 'zhoHant', 'lzhHani', 'yueHang', 'wuu', 'yueKana', 'wuuLatn', 'yueBopo', 'cjyHant', 'yueHans', 'lzh', 'cmnHira', 'lzhYiii', 'lzhHans', 'cmnBopo', 'cmnHang'…

Model · Zero-shot classification

MoritzLaurer-roberta-base-zeroshot-v2.0-c-onnx

Protect AI

This model is a conversion of MoritzLaurer/roberta-base-zeroshot-v2.0-c to ONNX format using the Optimum library.

Model · Image to image

FLUX.2-klein-base-4b-fp8

Black Forest Labs

FLUX.2 [klein] 4B Base is a 4 billion parameter rectified flow transformer capable of generating images from text descriptions and supports multi-reference editing capabilities. For more information, please read our blog post. This repository holds an FP8 version of FLUX.2 [klein] 4B Base. The main repository of this model (full BF16 weights) can be found here. Limitations - This model is not intended or able to provide factual information. - While the model can output text, text rendered may be inaccurate or subject to distortion. - As a statistical model, this checkpoint may represent or amplify biases observed in the training data. - The model may fail to generate output that matches the…

Model · Fill mask

mbarthez

Moussa Kamal Eddine

A french sequence to sequence pretrained model based on BART. BARThez is pretrained by learning to reconstruct a corrupted input sentence. A corpus of 66GB of french raw text is used to carry out the pretraining. Unlike already existing BERT-based French language models such as CamemBERT and FlauBERT, BARThez is particularly well-suited for generative tasks (such as abstractive summarization), since not only its encoder but also its decoder is pretrained. In addition to BARThez that is pretrained from scratch, we continue the pretraining of a multilingual BART mBART which boosted its performance in both discriminative and generative tasks. We call the french adapted version mBARThez.

This is an NVFP4 quantized version of Qwen3-VL-8B-Instruct, a powerful vision-language model for multimodal understanding and generation tasks. The following modules were excluded from quantization to maintain model quality: - lmhead (language model head) - Visual encoder modules (model.visual.) - MLP gate projections (.mlp.gate$) For faster inference, you can use this model with vLLM: This quantized model maintains high quality for vision-language tasks while significantly reducing memory usage. The SmoothQuant technique helps preserve model accuracy during quantization. Typical quality degradation is 2-5% compared to the full-precision model. 1. Calibration: Used 512 samples from the…

Model · Image classification

convnext_small.in12k_ft_in1k

PyTorch Image Models

A ConvNeXt image classification model. Pretrained in timm on ImageNet-12k (a 11821 class subset of full ImageNet-22k) and fine-tuned on ImageNet-1k by Ross Wightman. ImageNet-12k training done on TPUs thanks to support of the TRC program. Fine-tuning performed on 8x GPU Lambda Labs cloud instances. Explore the dataset and runtime metrics of this model in timm model results. All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP.

Model · Any to any

gemma-4-E2B-it-GGUF

GGML Org

Run with https://llama.app - https://huggingface.co/google/gemma-4-E2B-it - https://huggingface.co/google/gemma-4-E2B-it-assistant - https://huggingface.co/google/gemma-4-E2B-it-qat-q40-unquantized-assistant - https://huggingface.co/google/gemma-4-E2B-it-qat-q40-unquantized - add info - add dflash

Model · Image to text

trocr-base-handwritten-hist-swe-2

Swedish National Archives, AI-lab and Data Services

An HTR model for historical Swedish developed by the Swedish National Archives in collaboration with the Stockholm City Archives, the Finnish National Archives and Jämtlands Fornskriftsällskap. The model is trained on Swedish handwriting from the period 1600-1900. The model is trained on Swedish running-text handwriting dating from the start of the 17th century to the end of the 19th century. Like most current HTR models it operates on a text-line level, so its intended use is within an HTR pipeline that segments the text into text lines, which are transcribed by the model. The model can be used without fine-tuning on all handwriting but performs best on the type of handwriting it was…

Model · Time series forecasting

chronos-t5-mini

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…

This GGUF file is a direct conversion of Wan-AI/Wan2.2-TI2V-5B Since this is a quantized model, all original licensing terms and usage restrictions remain in effect. Usage The model can be used with the ComfyUI custom node ComfyUI-GGUF by city96 Place model files in ComfyUI/models/unet see the GitHub readme for further installation instructions.

Model · Translation

opus-mt-ROMANCE-en

Helsinki-NLP Research Group

source languages: fr,frBE,frCA,frFR,wa,frp,oc,ca,rm,lld,fur,lij,lmo,es,esAR,esCL,esCO,esCR,esDO,esEC,esES,esGT,esHN,esMX,esNI,esPA,esPE,esPR,esSV,esUY,esVE,pt,ptbr,ptBR,ptPT,gl,lad,an,mwl,it,itIT,co,nap,scn,vec,sc,ro,la; target languages: en; OPUS readme: fr+frBE+frCA+frFR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+esAR+esCL+esCO+esCR+esDO+esEC+esES+esGT+esHN+esMX+esNI+esPA+esPE+esPR+esSV+esUY+esVE+pt+ptbr+ptBR+ptPT+gl+lad+an+mwl+it+itIT+co+nap+scn+vec+sc+ro+la-en; dataset: opus; model: transformer; pre-processing: normalization + SentencePiece.

Model · Tabular classification

Nori

Synthefy

Nori is 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. Mean and median R² of the base model across 96 regression tasks from three public benchmark suites (single H200, up to 50K context rows per dataset): Large-N / long-context tables (common in TabArena) are the current focus of the large-table training stages. These numbers are reproducible end-to-end with one command — see Reproducing these numbers. Paste this into Claude Code, Cursor, or any AI coding assistant and it will…

Model · Text to video

Wan2.1-T2V-14B-Diffusers

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 features our T2V-14B model, which establishes a new SOTA performance benchmark among both open-source and closed-source models. It demonstrates exceptional capabilities in generating high-quality visuals with significant motion dynamics. It is also the only video model capable of producing both Chinese and English text and supports video generation at both 480P and 720P resolutions. Your browser does not support the video tag. - Wan2.1 Text-to-Video - [x] Multi-GPU Inference code of the 14B and 1.3B…

This is a quantization of Tongyi-MAI/Z-Image-Turbo to FP8 E5M2 and FP8 E4M3FN. This model strictly follows the original licensing terms and usage restrictions. Please refer to the original model card for details.

Model · Text to speech

Kokoro-82M-bf16

MLX Community

This model was converted to MLX format from hexagrad/Kokoro-82M using mlx-audio version 0.0.1. Refer to the original model card for more details on the model.

This is an image captioning model trained by @ydshieh in flax this is pytorch version of this. https://ankur3107.github.io/blogs/the-illustrated-image-captioning-using-transformers/ https://huggingface.co/ankur310794 https://twitter.com/ankur310794 http://github.com/ankur3107 https://www.linkedin.com/in/ankur310794

Model · Text to video

Wan2.2-TI2V-5B-GGUF

Unsloth AI

Every GGUF quantisation of Wan2.2-TI2V-5B that QuantStack/Wan2.2-TI2V-5B-GGUF publishes, plus the companion VAE, mirrored here. Unsloth Studio offers this repo as the curated one-click GGUF pick for Wan2.2 TI2V 5B, so its availability is Studio's problem rather than the repacker's: a rename or a takedown turns the pick into a 404 no client can work around. All 13 quants are mirrored, not a chosen few, because the picker lets you choose the precision. The weights are unmodified: byte for byte the files of the same name in the source repo. TI2V-5B is a 720P-only checkpoint: the supported sizes are 1280x704 and 704x1280, and its VAE has temporal compression 4, so valid frame counts are 4k+1.…

Model · Text to speech

MOSS-TTS-Nano-100M

OpenMOSS

MOSS-TTS-Nano is an open-source multilingual tiny speech generation model from MOSI.AI and the OpenMOSS team. With only 0.1B parameters, it is designed for realtime speech generation, can run directly on CPU without a GPU, and keeps the deployment stack simple enough for local demos, web serving, and lightweight product integration. 2026.4.10: We release MOSS-TTS-Nano. A demo Space is available at OpenMOSS-Team/MOSS-TTS-Nano. You can also view the demo and more details at openmoss.github.io/MOSS-TTS-Nano-Demo/. MOSS-TTS-Nano focuses on the part of TTS deployment that matters most in practice: small footprint, low latency, good enough quality for realtime products, and simple local setup. It…

Model · Text to speech

svara-tts-v1

Kenpath Labs

svara-TTS is a developer-first multilingual TTS model for 19 languages (18 Indic + Indian English). Built on an Orpheus-style discrete audio token approach, it targets clarity, expressiveness, and low-latency on commodity GPUs/CPUs. It supports light-weight emotion/style control (e.g.,,,, ) and simple speaker identities (Language (Gender)), with zero-shot adaptation paths. Try it live on the Demo Space, or on Colab Deployment scripts and inference repo will be available soon. Watch our Github for updates - Place style/emotion tags at the end of the sentence: आज... सच में अच्छी खबर है — शाम को मिलते हैं! - Use punctuation to hint prosody (ellipses, commas, exclamation). - For technical or…

Model · Text to image

nunchaku-qwen-image-edit

Nunchaku

This repository contains Nunchaku-quantized versions of Qwen-Image-Edit, an image-editing model based on Qwen-Image, advances in complex text rendering. It is optimized for efficient inference while maintaining minimal loss in performance. No recent news. Stay tuned for updates! Data Type: INT4 for non-Blackwell GPUs (pre-50-series), NVFP4 for Blackwell GPUs (50-series). Rank: r32 for faster inference, r128 for better quality but slower inference. Standard inference speed models for general use 4-step distilled models fused with Qwen-Image-Edit-Lightning-4steps-V1.0 LoRA using LoRA strength = 1.0 8-step distilled models fused with Qwen-Image-Edit-Lightning-8steps-V1.0 LoRA using LoRA…

Model · Text to image

FLUX.2-klein-4B-mflux-4bit

Runpod

4-bit quantized weights of [FLUX.2 [klein] 4B](https://huggingface.co/black-forest-labs/FLUX.2-klein-4B) by Black Forest Labs, optimized for mflux on Apple Silicon. FLUX.2 [klein] 4B is a 4 billion parameter rectified flow transformer by Black Forest Labs for fast image generation and editing. It delivers state-of-the-art quality with sub-second inference on consumer hardware. - Apache 2.0 — fully open for commercial use Apache 2.0, inherited from the original model.

Model · Image to text

PP-OCRv6_medium_det

PaddlePaddle

PP-OCRv6: From 1.5M to 34.5M Parameters, Surpassing Billion-Scale VLMs on OCR Tasks PP-OCRv6 is a lightweight OCR system that combines architectural innovation with data-centric optimization. It redesigns the backbone, detection neck, and recognition neck around a unified MetaFormer-style building block with structural reparameterization. Three model tiers (medium, small, tiny) share the same block primitives, covering deployment scenarios from server to edge. 1. Unified and Scalable Model Family: A three-tier OCR model family spanning 1.5M to 34.5M parameters. PP-OCRv6medium achieves 86.2% detection Hmean and 83.2% recognition accuracy, outperforming PP-OCRv5server by +4.6% and +5.1%…

Model · Text to image

girlslike-krea2

Ifmylove2011

用于测试基于krea2raw int8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。 理论上在krea2 raw模型与krea2 turbo模型上均可使用，使用强度在0.8~1.5之间，高于2.0面部会开始出现明显变形； （训练方案经过一段时间的测试，仍然采用了全秩lokr，泛化、细节、相似度三者相对均衡，在训练素材质量不高的情况下也更加有效） （存疑，某些中文概念确实可以准确理解，但也不要因此对krea2的中文理解能力抱有过高的期待，目前只能说比之前的flux系列有较大进步，实际效果有待商榷，比如flux系列的老问题——krea2仍然会把中文的"桃子"大概率画成苹果） krea2 turbo fp8； euler + beta|beta57； cfg=1； lora权重1.0； 无其他lora参与。 本系列lora训练中同样添加了人物本名作为触发词，生图时需输入触发词才能准确画出对应人物（名字本身就是触发词，无需其他前缀后缀）； 触发词一般为中文，部分人物将使用英文触发词，英文触发词会包含在示例图的水印中（水印通常以girlslikekrea2 for xxx形式出现，xxx即为触发词），有水印的示例图就代表需要使用英文触发词（因为krea2目前仍然无法准确生成中文，所以水印无法使用中文来表示触发词）； 示例图片一如既往包含工作流与提示词，lora强度为1.0，均由krea2turbo fp8模型生成； 示例图人名缩写可在 girlslikeloragalleryapp…

Model · Text to speech

VieNeu-TTS-v2

Pham Nguyen Ngoc Bao

VieNeu-TTS-v2 is the next generation of Vietnamese TTS, designed for Natural Communication, Podcasts, and Bilingual (En-Vi) Code-switching. This project features the flagship VieNeu-TTS-v2 architecture: Tác giả: Phạm Nguyễn Ngọc Bảo Training high-quality TTS models requires significant GPU resources. If you find this model useful, please consider supporting the development: Install the SDK to integrate VieNeu-TTS-0.3B into your research or applications: Deploy VieNeu-TTS as a high-performance API Server (powered by LMDeploy) with a single command. Start the Server with a Public Tunnel (No port forwarding needed): Once the server is running, you can connect from anywhere (Colab, Web Apps…

Model · Time series forecasting

flowstate

IBM Research

FlowState is the first time-scale adjustable Time Series Foundation Model (TSFM), open-sourced by IBM Research. Combining an 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 · Text to speech

orpheus-3b-0.1-ft

Unsloth AI

03/18/2025 – We are releasing our 3B Orpheus TTS model with additional finetunes. Code is available on GitHub: CanopyAI/Orpheus-TTS Orpheus TTS is a state-of-the-art, Llama-based Speech-LLM designed for high-quality, empathetic text-to-speech generation. This model has been finetuned to deliver human-level speech synthesis, achieving exceptional clarity, expressiveness, and real-time streaming performances. Check out our Colab (link to Colab) or GitHub (link to GitHub) on how to run easy inference on our finetuned models. Do not use our models for impersonation without consent, misinformation or deception (including fake news or fraudulent calls), or any illegal or harmful activity. By…

Model · Zero-shot classification

nli-deberta-v3-xsmall

Sentence Transformers - Cross-Encoders

This model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-xsmall 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-OCRv6_medium_det_onnx

PaddlePaddle

PP-OCRv6: From 1.5M to 34.5M Parameters, Surpassing Billion-Scale VLMs on OCR Tasks PP-OCRv6 is a lightweight OCR system that combines architectural innovation with data-centric optimization. It redesigns the backbone, detection neck, and recognition neck around a unified MetaFormer-style building block with structural reparameterization. Three model tiers (medium, small, tiny) share the same block primitives, covering deployment scenarios from server to edge. 1. Unified and Scalable Model Family: A three-tier OCR model family spanning 1.5M to 34.5M parameters. PP-OCRv6medium achieves 86.2% detection Hmean and 83.2% recognition accuracy, outperforming PP-OCRv5server by +4.6% and +5.1%…

Model · Translation

opus-mt-mul-en

Helsinki-NLP Research Group

hfname: mul-eng - sourcelanguages: mul - targetlanguages: eng - opusreadmeurl: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/mul-eng/README.md - originalrepo: Tatoeba-Challenge - srcconstituents: {'sjnLatn', 'cat', 'nan', 'spa', 'ileLatn', 'pap', 'mwl', 'uzbLatn', 'mww', 'hil', 'lij', 'avkLatn', 'ladLatn', 'latLatn', 'bosLatn', 'oss', 'epo', 'ron', 'fry', 'cym', 'toiLatn', 'awa', 'swg', 'zsmLatn', 'zhoHant', 'gcfLatn', 'uzbCyrl', 'isl', 'lfnLatn', 'shsLatn', 'novLatn', 'bho', 'ltz', 'lzh', 'kurLatn', 'sun', 'arg', 'pesThaa', 'sqi', 'uigArab', 'csbLatn', 'fra', 'hat', 'livLatn', 'nonLatn', 'sco', 'cmnHans', 'pnb', 'roh', 'chv', 'ibo', 'bulLatn', 'amh', 'lfnCyrl'…

convert TurboWan2.1-T2V-1.3B-480P(https://modelscope.cn/models/TurboDiffusion/TurboWan2.1-T2V-1.3B-480P/summary) to TurboWan2.1-T2V-1.3B-Diffusers convert script https://github.com/IPostYellow/TurboWantoDiffusers/blob/main/convertturbowantodiffusers.py To use in sglang

Model · Image to text

PP-OCRv6_medium_rec

PaddlePaddle

PP-OCRv6: From 1.5M to 34.5M Parameters, Surpassing Billion-Scale VLMs on OCR Tasks PP-OCRv6 is a lightweight OCR system that combines architectural innovation with data-centric optimization. It redesigns the backbone, detection neck, and recognition neck around a unified MetaFormer-style building block with structural reparameterization. Three model tiers (medium, small, tiny) share the same block primitives, covering deployment scenarios from server to edge. 1. Unified and Scalable Model Family: A three-tier OCR model family spanning 1.5M to 34.5M parameters. PP-OCRv6medium achieves 86.2% detection Hmean and 83.2% recognition accuracy, outperforming PP-OCRv5server by +4.6% and +5.1%…

Model · Image and text to text

VLX-Seek-1.5-10B

OmAI Lab

VLX-Seek-1.5-10B is the open-source 10B model in the VLX-Seek 1.5 family, designed for fine-grained perception and visual grounding in embodied scenarios. It targets practical settings such as drones, robots, robotic dogs, surveillance cameras, inspection systems, and other edge-side visual intelligence applications where a model must identify what is present, localize the right instance, and avoid grounding objects that are absent. Unlike coordinate-generation-based VLMs that directly decode bounding-box numbers, VLX-Seek reformulates localization as region retrieval and region reference. Candidate visual regions are represented as addressable entities, and the model answers by selecting…

Model · Translation

opus-mt-fr-es

Helsinki-NLP Research Group

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

Model · Text to video

Wan2.1-T2V-14B

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 features our T2V-14B model, which establishes a new SOTA performance benchmark among both open-source and closed-source models. It demonstrates exceptional capabilities in generating high-quality visuals with significant motion dynamics. It is also the only video model capable of producing both Chinese and English text and supports video generation at both 480P and 720P resolutions. Your browser does not support the video tag. - Wan2.1 Text-to-Video - [x] Multi-GPU Inference code of the 14B and 1.3B…

Model · Translation

opus-mt-it-es

Helsinki-NLP Research Group

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

Model · Text to speech

MOSS-VoiceGenerator

OpenMOSS

MOSS‑TTS Family is an open‑source speech and sound generation model family from MOSI.AI and the OpenMOSS team. It is designed for high‑fidelity, high‑expressiveness, and complex real‑world scenarios, covering stable long‑form speech, multi‑speaker dialogue, voice/character design, environmental sound effects, and real‑time streaming TTS. When a single piece of audio needs to sound like a real person, pronounce every word accurately, switch speaking styles across content, remain stable over tens of minutes, and support dialogue, role‑play, and real‑time interaction, a single TTS model is often not enough. The MOSS‑TTS Family breaks the workflow into five production‑ready models that can be…

Model · Translation

opus-mt-tr-en

Helsinki-NLP Research Group

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

Model · Translation

opus-mt-eu-es

Helsinki-NLP Research Group

hfname: eus-spa - sourcelanguages: eus - targetlanguages: spa - opusreadmeurl: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eus-spa/README.md - originalrepo: Tatoeba-Challenge - srcconstituents: {'eus'} - tgtconstituents: {'spa'} - srcmultilingual: False - tgtmultilingual: False - urlmodel: https://object.pouta.csc.fi/Tatoeba-MT-models/eus-spa/opus-2020-06-17.zip - urltestset: https://object.pouta.csc.fi/Tatoeba-MT-models/eus-spa/opus-2020-06-17.test.txt - srcalpha3: eus - tgtalpha3: spa - shortpair: eu-es - chrF2score: 0.6729999999999999 - brevitypenalty: 0.9640000000000001 - reflen: 12469.0 - srcname: Basque - tgtname: Spanish - traindate: 2020-06-17 - srcalpha2…

Model · Time series forecasting

TimeMoE-50M

Xiaoming Shi

This repository contains the weights of the TimeMoE-50M model of the paper Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts. For details on how to use this model, please visit our GitHub page.