Open-Weight Models

Model · Token classification

llmlingua-2-xlm-roberta-large-meetingbank

Microsoft

This model was introduced in the paper LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression (Pan et al, 2024). It is a XLM-RoBERTa (large-sized model) finetuned to perform token classification for task agnostic prompt compression. The probability $p{preserve}$ of each token $xi$ is used as the metric for compression. This model is trained on the extractive text compression dataset constructed with the methodology proposed in the LLMLingua-2, using training examples from MeetingBank (Hu et al, 2023) as the seed data. You can evaluate the model on downstream tasks such as question answering (QA) and summarization over compressed meeting transcripts using…

Model · Translation

nllb-200-3.3B

AI at Meta

This is the model card of NLLB-200's 3.3B variant. Here are the metrics for that particular checkpoint. - Information about training algorithms, parameters, fairness constraints or other applied approaches, and features. The exact training algorithm, data and the strategies to handle data imbalances for high and low resource languages that were used to train NLLB-200 is described in the paper. - Paper or other resource for more information NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022 - Where to send questions or comments about the model: https://github.com/facebookresearch/fairseq/issues • Model performance measures: NLLB-200 model was…

Model · Token classification

OpenMed-NER-GenomicDetect-PubMed-109M

OpenMed

Specialized model for Gene Entity Recognition - Gene-related entities This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for gene entity recognition - gene-related entities. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as drug interaction detection, medication extraction from patient records, adverse event monitoring, literature mining for drug discovery, and biomedical knowledge graph construction with production-ready reliability for clinical and research applications. This model can identify and classify the…

Model · Token classification

OpenMed-NER-OrganismDetect-TinyMed-82M

OpenMed

Specialized model for Species Entity Recognition - Species names from the Species-800 dataset This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for species entity recognition - species names from the species-800 dataset. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as drug interaction detection, medication extraction from patient records, adverse event monitoring, literature mining for drug discovery, and biomedical knowledge graph construction with production-ready reliability for clinical and research…

Model

flan-t5-xl

Google

alt="drawing" width="600"/> If you already know T5, FLAN-T5 is just better at everything. For the same number of parameters, these models have been fine-tuned on more than 1000 additional tasks covering also more languages. As mentioned in the first few lines of the abstract: Disclaimer: Content from this model card has been written by the Hugging Face team, and parts of it were copy pasted from the T5 model card. Find below some example scripts on how to use the model in transformers: The authors write in the original paper's model card that: See the research paper for further details. The information below in this section are copied from the model's official model card: The model was…

Model · Image to image

FLUX.2-klein-4b-fp8

Black Forest Labs

FLUX.2 [klein] 4B 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. 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 prompts.…

Model · Text to image

DreamShaper

Lykon

Read more about this model here: https://civitai.com/models/4384/dreamshaper Also please support by giving 5 stars and a heart, which will notify new updates. Please consider supporting me on Patreon or buy me a coffee - https://www.patreon.com/Lykon275 - https://snipfeed.co/lykon You can run this model on: - https://huggingface.co/spaces/Lykon/DreamShaper-webui - Mage.space, sinkin.ai and more

Model · Image to text

granite-vision-3.3-2b

IBM Granite

Granite-vision-3.3-2b is a compact and efficient vision-language model, specifically designed for visual document understanding, enabling automated content extraction from tables, charts, infographics, plots, diagrams, and more. Granite-vision-3.3-2b introduces several novel experimental features such as image segmentation, doctags generation, and multi-page support (see Experimental Capabilities for more details) and offers enhanced safety when compared to earlier Granite vision models. The model was trained on a meticulously curated instruction-following data, comprising diverse public and synthetic datasets tailored to support a wide range of document understanding and general image…

Model · Token classification

OpenMed-NER-GenomeDetect-ModernMed-149M

OpenMed

Specialized model for Gene/Protein Entity Recognition - Gene and protein mentions This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for gene/protein entity recognition - gene and protein mentions. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as drug interaction detection, medication extraction from patient records, adverse event monitoring, literature mining for drug discovery, and biomedical knowledge graph construction with production-ready reliability for clinical and research applications. This model can…

Model · Text to speech

tts-1.6b-en_fr

Kyutai

See also the pre-print research paper, the project page, the Colab example, the GitHub repository, and the repository of voices. This is a model for streaming text-to-speech (TTS). Unlike offline text-to-speech, where the model needs the entire text to produce the audio, our model starts to output audio as soon as the first few words from the text have been given as input. This model is actually 1.8B parameters, not 1.6B as the name might suggest. The model architecture is a hierarchical Transformer that consumes tokenized text and generateds audio tokenized by Mimi, see the Moshi paper. The frame rate is 12.5 Hz and each audio frame is represented by 32 audio tokens, although you can use…

Model · Text to speech

E2-TTS

Yushen CHEN

Download F5-TTS or E2 TTS and place under ckpts/ Paper: E2 TTS: Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS

Model · Question answering

tinyroberta-squad2

Deepset

This is the distilled version of the deepset/roberta-base-squad2 model. This model has a comparable prediction quality and runs at twice the speed of the base model. This model was distilled using the TinyBERT approach described in this paper and implemented in haystack. Firstly, we have performed intermediate layer distillation with roberta-base as the teacher which resulted in deepset/tinyroberta-6l-768d. Secondly, we have performed task-specific distillation with deepset/roberta-base-squad2 as the teacher for further intermediate layer distillation on an augmented version of SQuADv2 and then with deepset/roberta-large-squad2 as the teacher for prediction layer distillation. Haystack is…

Model · Text to speech

mms-tts-eng

AI at Meta

mms - vits pipelinetag: text-to-speech This repository contains the English (eng) language text-to-speech (TTS) model checkpoint. This model is part of Facebook's Massively Multilingual Speech project, aiming to provide speech technology across a diverse range of languages. You can find more details about the supported languages and their ISO 639-3 codes in the MMS Language Coverage Overview, and see all MMS-TTS checkpoints on the Hugging Face Hub: facebook/mms-tts. MMS-TTS is available in the Transformers library from version 4.33 onwards. VITS (Variational Inference with adversarial learning for end-to-end Text-to-Speech) is an end-to-end speech synthesis model that predicts a speech…

Model · Video classification

vjepa2-vitl-fpc64-256

AI at Meta

A frontier video understanding model developed by FAIR, Meta, which extends the pretraining objectives of VJEPA, resulting in state-of-the-art video understanding capabilities, leveraging data and model sizes at scale. The code is released in this repository. To run V-JEPA 2 model, ensure you have installed the latest transformers: V-JEPA 2 is intended to represent any video (and image) to perform video classification, retrieval, or as a video encoder for VLMs. To load a video, sample the number of frames according to the model. For this model, we use 64. To load an image, simply copy the image to the desired number of frames. For more code examples, please refer to the V-JEPA 2…

Model · Translation

opus-mt-en-fr

Helsinki-NLP Research Group

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

Model · Token classification

OpenMed-NER-GenomeDetect-ModernMed-395M

OpenMed

Specialized model for Gene/Protein Entity Recognition - Gene and protein mentions This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for gene/protein entity recognition - gene and protein mentions. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as drug interaction detection, medication extraction from patient records, adverse event monitoring, literature mining for drug discovery, and biomedical knowledge graph construction with production-ready reliability for clinical and research applications. This model can…

Model · Image to video

Minimax-h3_Singularity

AIGC Singularity

Minimax-h3Singularity is a comprehensive fine-tuned fusion model specialized in enhancing the capabilities of MiniMax-H3. Designed as a versatile multimodal video generation model, it natively supports Text-to-Video (T2V), Image-to-Video (I2V), Reference-to-Video (Ref2V), and Video-to-Video (V2V) workflows within ComfyUI. Built upon a strategic fusion of key checkpoints (including ref, fl, b25-49, etc.), this model underwent deep high-step fine-tuning. To preserve the original model's foundational strengths and broad generalization while solving artifacts introduced by high-step training, we spent 3 full days on precise model pruning and weight optimization. The result is a clean, sharp…

Model

prot_t5_xl_uniref50

Rostlab

Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is trained on uppercase amino acids: it only works with capital letter amino acids. ProtT5-XL-UniRef50 is based on the t5-3b model and was pretrained on a large corpus of protein sequences in a self-supervised fashion. This means it was pretrained on the raw protein sequences only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those protein sequences. One important difference between this T5 model and the…

Model · Zero-shot classification

DeBERTa-v3-large-mnli-fever-anli-ling-wanli

Moritz Borrett-Laurer (formerly Laurer)

This model was fine-tuned on the MultiNLI, Fever-NLI, Adversarial-NLI (ANLI), LingNLI and WANLI datasets, which comprise 885 242 NLI hypothesis-premise pairs. This model is the best performing NLI model on the Hugging Face Hub as of 06.06.22 and can be used for zero-shot classification. It significantly outperforms all other large models on the ANLI benchmark. The foundation model is DeBERTa-v3-large from Microsoft. DeBERTa-v3 combines several recent innovations compared to classical Masked Language Models like BERT, RoBERTa etc., see the paper DeBERTa-v3-large-mnli-fever-anli-ling-wanli was trained on the MultiNLI, Fever-NLI, Adversarial-NLI (ANLI), LingNLI and WANLI datasets, which…

Model · Translation

opus-mt-zh-en

Helsinki-NLP Research Group

This model can be used for translation and text-to-text generation. CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes. Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Further details about the dataset for this model can be found in the OPUS readme: zho-eng helsinkigitsha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 transformersgitsha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b portmachine: brutasse porttime: 2020-08-21-14:41 srcmultilingual: False tgtmultilingual: False reflen: 82826.0 brevitypenalty…

Model · Image to text

PP-OCRv5_server_rec

PaddlePaddle

PP-OCRv5serverrec 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 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…

LTX-2.5 is an open world model with open weights, built for local execution and fine-tuning. Its established use is generating synchronized, high-fidelity video and audio from text, image, and video inputs; applicability to emerging domains such as robotics and physical AI is developing. Full control and customization — self-host on your own infrastructure. No per-generation billing, no per-seat lock-in, no forced API dependency. Revenue is measured across the whole entity, including subsidiaries and affiliates under common control. The full, binding terms live in LICENSE. - Native multishot generation — generate connected scenes in a single pass: multiple shots that hold character…

Model · Token classification

OpenMed-NER-GenomicDetect-BigMed-560M

OpenMed

Specialized model for Gene Entity Recognition - Gene-related entities This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for gene entity recognition - gene-related entities. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as drug interaction detection, medication extraction from patient records, adverse event monitoring, literature mining for drug discovery, and biomedical knowledge graph construction with production-ready reliability for clinical and research applications. This model can identify and classify the…