Open-Weight Models Under MIT License

Model

vocos-mel-24khz

Charactr Inc.

Paper [[abs]](https://arxiv.org/abs/2306.00814) [[pdf]](https://arxiv.org/pdf/2306.00814.pdf) Vocos is a fast neural vocoder designed to synthesize audio waveforms from acoustic features. Trained using a Generative Adversarial Network (GAN) objective, Vocos can generate waveforms in a single forward pass. Unlike other typical GAN-based vocoders, Vocos does not model audio samples in the time domain. Instead, it generates spectral coefficients, facilitating rapid audio reconstruction through inverse Fourier transform. To use Vocos only in inference mode, install it using: If you wish to train the model, install it with additional dependencies: If this code contributes to your research…

Model · Speech recognition

faster-whisper-small

Systran

This repository contains the conversion of openai/whisper-small to the CTranslate2 model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as faster-whisper. The original model was converted with the following command: Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the computetype option in CTranslate2. For more information about the original model, see its model card.

Model · Feature extraction

bge-base-en-v1.5

Joshua

https://huggingface.co/BAAI/bge-base-en-v1.5 with ONNX weights to be compatible with Transformers.js. If you haven't already, you can install the Transformers.js JavaScript library from NPM using: You can then use the model to compute embeddings, as follows: You can also use the model for retrieval. For example: 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).

Model · Speech recognition

faster-whisper-tiny

Systran

This repository contains the conversion of openai/whisper-tiny to the CTranslate2 model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as faster-whisper. The original model was converted with the following command: Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the computetype option in CTranslate2. For more information about the original model, see its model card.

Model · Fill mask

Bio_ClinicalBERT

Emily Alsentzer

The Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models: initialized with BERT-Base (casedL-12H-768A-12) or BioBERT (BioBERT-Base v1.0 + PubMed 200K + PMC 270K) & trained on either all MIMIC notes or only discharge summaries. This model card describes the Bio+Clinical BERT model, which was initialized from BioBERT & trained on all MIMIC notes. The BioClinicalBERT model was trained on all notes from MIMIC III, a database containing electronic health records from ICU patients at the Beth Israel Hospital in Boston, MA. For more details on MIMIC, see here. All notes from the NOTEEVENTS table were included (~880M words). Each note in MIMIC was first split…

Model

esmfold_v1

AI at Meta

ESMFold is a state-of-the-art end-to-end protein folding model based on an ESM-2 backbone. It does not require any lookup or MSA step, and therefore does not require any external databases to be present in order to make predictions. As a result, inference time is very significantly faster than AlphaFold2. For details on the model architecture and training, please refer to the accompanying paper. If you're interested in using ESMFold in practice, please check out the associated tutorial notebook.

Model · Text generation

Ornith-1.0-35B-GGUF

Ornith

Aloha! Today, we are releasing Ornith-1.0, a self-improving family of open-source models for agentic coding. This model card documents Ornith-1.0-35B, the lightweight member of the Ornith family, designed for efficient single-GPU deployment. The two recipes below stand up an OpenAI-compatible server on a single 8×80GB GPU node (tensor-parallel 8). Adjust --tensor-parallel-size / --tp to the number of GPUs you have. For a quick local test (or to script offline generation), load the model directly with Transformers. Make sure you have a recent release installed — see the Transformers installation guide; Ornith-1.0-35B requires transformers >= 5.8.1. To split the reasoning trace from the final…

Model · Image to 3d

TRELLIS.2-4B

Microsoft

TRELLIS.2 is a state-of-the-art large 3D generative model designed for high-fidelity image-to-3D generation. It leverages a novel "field-free" sparse voxel structure termed O-Voxel and a large-scale flow-matching transformer (4 Billion parameters). Unlike previous methods that rely on iso-surface fields (e.g., SDF, Flexicubes) which struggle with open surfaces or non-manifold geometry, TRELLIS can reconstruct and generate arbitrary 3D assets with complex topologies, sharp features, and full Physical-Based Rendering (PBR) materials—including transparency/translucency. - The CUDA Toolkit is needed to compile certain packages. Recommended version is 12.4. - Conda is recommended for managing…

This quants are specific for the DS4 inference engine. They may work with other inference engines or not (they should, but not the MTP model which requires a specific loader). https://github.com/antirez/ds4 Use q2 on 128 GB Mac machines, q4 on machines with ≥ 256 GB RAM, pair either with MTP for optional speculative decoding. The filename is the spec. In detail, for the q2 file: For the q4 file, only the three routed-expert classes change to Q4K. Everything else is byte-for-byte identical to the q2 recipe. The motivation behind the asymmetry: the routed experts are the majority of the parameter count but each individual expert handles only a fraction of tokens, so aggressive quantization on…

Model · Text to speech

chatterbox

Resemble AI

Chatterbox Multilingual V3 is the latest general-purpose multilingual TTS model in the Chatterbox family. It keeps the same 0.5B model size while improving speaker similarity, reducing hallucinations, and producing more natural, conversational speech across languages. V3 is designed for broad language coverage like V2, but with stronger stability and more expressive generation. It is the recommended multilingual model for users who want one voice cloning model that works across many languages. Try it in the Chatterbox Multilingual TTS V3 Space. Alongside V3, we are releasing the Single Language Pack: dedicated finetunes for priority languages where tighter quality control, stronger…

Model · Voice activity detection

segmentation

Pyannote

Using this open-source model in production? Consider switching to pyannoteAI for better and faster options. In order to reproduce the results of the paper "End-to-end speaker segmentation for overlap-aware resegmentation ", use pyannote/segmentation@Interspeech2021 with the following hyper-parameters: Expected outputs (and VBx baseline) are also provided in the /reproducibleresearch sub-directories.

Model · Speech recognition

faster-whisper-base

Systran

This repository contains the conversion of openai/whisper-base to the CTranslate2 model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as faster-whisper. The original model was converted with the following command: Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the computetype option in CTranslate2. For more information about the original model, see its model card.

BERTimbau Large is a pretrained BERT model for Brazilian Portuguese that achieves state-of-the-art performances on three downstream NLP tasks: Named Entity Recognition, Sentence Textual Similarity and Recognizing Textual Entailment. It is available in two sizes: Base and Large. For further information or requests, please go to BERTimbau repository. If you use our work, please cite

Model · Token classification

stanford-deidentifier-base

Stanford AIMI

Stanford de-identifier was trained on a variety of radiology and biomedical documents with the goal of automatising the de-identification process while reaching satisfactory accuracy for use in production. Manuscript in-proceedings. These model weights are the recommended ones among all available deidentifier weights. This work was supported in part by the Medical Imaging and Data Resource Center (MIDRC), which is funded by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health under contract 75N92020D00021 and through The Advanced Research Projects Agency for Health (ARPA-H)

Model

faster-whisper-large-v3-turbo

Dropbox Inc

This repository contains the conversion of openai/whisper-large-v3-turbo to the CTranslate2 model format. This model can be used in CTranslate2 or projects based on CTranslate2 models such as faster-whisper. It is called automatically for Mobius Labs fork of faster-whisper. Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the computetype option in CTranslate2. The openAI model was converted with the following command: For more information about the original model, see its model card.

Model · Speech recognition

vakyansh-wav2vec2-tamil-tam-250

Harveen Singh Chadha

Fine-tuned on Multilingual Pretrained Model CLSRIL-23. The original fairseq checkpoint is present here. When using this model, make sure that your speech input is sampled at 16kHz. Note: The result from this model is without a language model so you may witness a higher WER in some cases. This model was trained on 4200 hours of Hindi Labelled Data. The labelled data is not present in public domain as of now. Models were trained using experimental platform setup by Vakyansh team at Ekstep. Here is the training repository. In case you want to explore training logs on wandb they are here. The model can be used directly (without a language model) as follows: The model can be evaluated as follows…

Model · Text generation

Ornith-1.5-397B-GGUF

Ornith

Chirp Chirp! We are introducing Ornith-1.5, a major step toward building foundation models through end-to-end self-improvement. Ornith-1.5 extends Ornith-1.0 (which was developed on top of Qwen3.5 and Gemma4 with additional continued pretraining, mid-training, and post-training) by expanding the self-improvement loop from scaffold and rollout optimization to jointly optimizing task generation, scaffold construction, and solution rollouts. Rather than relying on a fixed set of human-curated tasks and manually designed harnesses, Ornith-1.5 continuously generates new training tasks, discovers effective strategies for solving them, and improves the policy through reinforcement learning. For…

Model · Audio to audio

bigvgan_v2_22khz_80band_256x

NVIDIA

[[Paper]](https://arxiv.org/abs/2206.04658) - [[Code]](https://github.com/NVIDIA/BigVGAN) - [[Showcase]](https://bigvgan-demo.github.io/) - [[Project Page]](https://research.nvidia.com/labs/adlr/projects/bigvgan/) - [[Weights]](https://huggingface.co/collections/nvidia/bigvgan-66959df3d97fd7d98d97dc9a) - [[Demo]](https://huggingface.co/spaces/nvidia/BigVGAN) - General refactor and code improvements for improved readability. - Fully fused CUDA kernel of anti-alised activation (upsampling + activation + downsampling) with inference speed benchmark. - We provide pretrained checkpoints of BigVGAN-v2 using diverse audio configurations, supporting up to 44 kHz sampling rate and 512x upsampling…

Model · Speech recognition

voice-activity-detection

Pyannote

Using this open-source model in production? Consider switching to pyannoteAI for better and faster options.

Model · Image and text to text

Unlimited-OCR-AWQ

Sahil Chachra

AWQ 4-bit (W4A16) quantization of baidu/Unlimited-OCR, a 3B vision-language OCR model that pushes DeepSeek-OCR one step further (one-shot, long-horizon document parsing). This repo quantizes the DeepSeek-V2 MoE text decoder with activation-aware scaling (AWQ) while keeping the vision tower in BF16, so it stays a drop-in transformers model. Unlimited-OCR uses the DeepSeek-OCR prompt vocabulary. The prompt must contain; prefix it with whenever you also want bounding boxes for what was read. - base — basesize=1024, imagesize=1024, cropmode=False. Good default for normal pages. - gundam — basesize=1024, imagesize=640, cropmode=True. Tiles the page; use for dense or large/high-resolution…

Model · Zero shot image classification

CLIP-convnext_base_w-laion2B-s13B-b82K-augreg

LAION eV

A series of CLIP ConvNeXt-Base (w/ wide embed dim) models trained on subsets LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/openclip). Explore an alternative to ViT and ResNet (w/ AttentionPooling) CLIP models that scales well with model size and image resolution First known ConvNeXt CLIP models trained at scale in the range of CLIP ViT-B/16 and RN50x4 models First released model weights exploring increase of augmentation + regularization for image tower via adding (greater scale range of RRC, random erasing, stochastic depth) The models utilize the timm ConvNeXt-Base model (convnextbase) as the image tower, and the same text tower as the RN50x4…

Model · Text generation

GLM-5.2-FP8

Z.ai

Join our WeChat or Discord community. Check out the GLM-5.2 blog and GLM-5 Technical report. Use GLM-5.2 API services on Z.ai API Platform. Try GLM-5.2 here. [ Paper ] [ GitHub ] We're introducing GLM-5.2, our latest flagship model for long-horizon tasks. It marks a substantial leap in long-horizon task capability over its predecessor GLM-5.1 and, for the first time, delivers that capability on a solid 1M-token context. GLM-5.2's new capabilities include: GLM-5.2 supports deployment with the following frameworks. Feel free to try them out: - SGLang (v0.5.13.post1+) — see cookbook - vLLM (v0.23.0+) — see recipes - Transformers (v0.5.12+) — see transformers docs - KTransformers (v0.5.12+)…

Model · Fill mask

BiomedNLP-BiomedBERT-base-uncased-abstract

Microsoft

This model was previously named "PubMedBERT (abstracts)". You can either adopt the new model name "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract" or update your transformers library to version 4.22+ if you need to refer to the old name. Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A prevailing assumption is that even domain-specific pretraining can benefit by starting from general-domain language models. Recent work shows that for domains with abundant unlabeled text, such as biomedicine, pretraining…

Model · Feature extraction

bge-large-zh-v1.5

Beijing Academy of Artificial Intelligence

For more details please refer to our Github: FlagEmbedding. If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using bge-m3. FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: - 1/30/2024: Release BGE-M3, a new member to BGE model series! M3 stands for Multi-linguality (100+ languages), Multi-granularities (input length up to 8192), Multi-Functionality (unification of dense, lexical, multi-vec/colbert retrieval). It is the first embedding model which supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks. Technical…

Model

bert-tiny

Prajjwal

The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the official Google BERT repository. This is one of the smaller pre-trained BERT variants, together with bert-mini bert-small and bert-medium. They were introduced in the study Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (arxiv), and ported to HF for the study Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics (arXiv). These models are supposed to be trained on a downstream task. If you use the model, please consider citing both the papers: - prajjwal1/bert-tiny (L=2, H=128) Model Link - prajjwal1/bert-mini (L=4, H=256) Model Link…

Model · Fill mask

deberta-v3-large

Microsoft

DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data. In DeBERTa V3, we further improved the efficiency of DeBERTa using ELECTRA-Style pre-training with Gradient Disentangled Embedding Sharing. Compared to DeBERTa, our V3 version significantly improves the model performance on downstream tasks. You can find more technique details about the new model from our paper. Please check the official repository for more implementation details and updates. The DeBERTa V3 large model comes with 24 layers and a hidden size of 1024. It has 304M…

Model · Speech recognition

faster-whisper-large-v3

Systran

This repository contains the conversion of openai/whisper-large-v3 to the CTranslate2 model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as faster-whisper. The original model was converted with the following command: Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the computetype option in CTranslate2. For more information about the original model, see its model card.

This model is a quantized version of the original model intfloat/e5-mistral-7b-instruct. It's quantized using the BitsAndBytes library to 4-bit using the bnb-my-repo space. - bnb4bitquanttype: nf4 - bnb4bitusedoublequant: True - bnb4bitcomputedtype: bfloat16 - bnb4bitquantstorage: uint8 Improving Text Embeddings with Large Language Models. Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024 This model has 32 layers and the embedding size is 4096. Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. Have a look at configsentencetransformers.json for the prompts that are pre-configured, such as websearchquery…

Model · Token classification

bert-portuguese-ner

Luís Filipe Cunha

This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased It achieves the following results on the evaluation set: This model was fine-tunned on token classification task (NER) on Portuguese archival documents. The annotated labels are: Date, Profession, Person, Place, Organization All the training and evaluation data is available at: http://ner.epl.di.uminho.pt/ The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 16 - evalbatchsize: 16 - lrschedulertype: linear - numepochs: 4 - Transformers 4.10.0.dev0 - Pytorch 1.9.0+cu111 - Datasets 1.10.2 - Tokenizers 0.10.3

Model

embedding

Pyannote

Using this open-source model in production? Consider switching to pyannoteAI for better and faster options. This model is based on the canonical x-vector TDNN-based architecture, but with filter banks replaced with trainable SincNet features. See XVectorSincNet architecture for implementation details. Using cosine distance directly, this model reaches 2.8% equal error rate (EER) on VoxCeleb 1 test set. This is without voice activity detection (VAD) nor probabilistic linear discriminant analysis (PLDA). Expect even better results when adding one of those.

Model · Token classification

punctuate-all

KREDOR

This is based on Oliver Guhr's work. The difference is that it is a finetuned xlm-roberta-base instead of an xlm-roberta-large and on twelve languages instead of four. The languages are: English, German, French, Spanish, Bulgarian, Italian, Polish, Dutch, Czech, Portugese, Slovak, Slovenian. precision recall f1-score support accuracy 0.98 84425503 macro avg 0.83 0.74 0.77 84425503 weighted avg 0.98 0.98 0.98 84425503

Model · Table question answering

tapex-base-finetuned-wikisql

Microsoft

TAPEX was proposed in TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found here. TAPEX (Table Pre-training via Execution) is a conceptually simple and empirically powerful pre-training approach to empower existing models with table reasoning skills. TAPEX realizes table pre-training by learning a neural SQL executor over a synthetic corpus, which is obtained by automatically synthesizing executable SQL queries. TAPEX is based on the BART architecture, the transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive…

Model · Feature extraction

bge-base-zh-v1.5

Beijing Academy of Artificial Intelligence

More details please refer to our Github: FlagEmbedding. FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search. And it also can be used in vector databases for LLMs. Updates - 10/12/2023: Release LLM-Embedder, a unified embedding model to support diverse retrieval augmentation needs for LLMs. Paper:fire: - 09/15/2023: The technical report of BGE has been released - 09/15/2023: The masive training data of BGE has been released - 09/12/2023: New models: - 09/07/2023: Update fine-tune code: Add script to mine hard negatives and support adding instruction during fine-tuning. - 08/09/2023: BGE…

Model · Audio to audio

bigvgan_v2_44khz_128band_512x

NVIDIA

[[Paper]](https://arxiv.org/abs/2206.04658) - [[Code]](https://github.com/NVIDIA/BigVGAN) - [[Showcase]](https://bigvgan-demo.github.io/) - [[Project Page]](https://research.nvidia.com/labs/adlr/projects/bigvgan/) - [[Weights]](https://huggingface.co/collections/nvidia/bigvgan-66959df3d97fd7d98d97dc9a) - [[Demo]](https://huggingface.co/spaces/nvidia/BigVGAN) - General refactor and code improvements for improved readability. - Fully fused CUDA kernel of anti-alised activation (upsampling + activation + downsampling) with inference speed benchmark. - We provide pretrained checkpoints of BigVGAN-v2 using diverse audio configurations, supporting up to 44 kHz sampling rate and 512x upsampling…

Model · Speech recognition

faster-whisper-medium

Systran

This repository contains the conversion of openai/whisper-medium to the CTranslate2 model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as faster-whisper. The original model was converted with the following command: Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the computetype option in CTranslate2. For more information about the original model, see its model card.

This repository hosts the official pretrained model checkpoints, vocabularies, and statistical datasets for the sinlib library—a comprehensive Sinhala NLP toolkit. This repository contains the following files loaded dynamically by sinlib.spellcheck.TypoDetector: bigrudetector.pt: A bidirectional GRU sequence labeling model (BiGRUSequenceLabeler) trained to detect spelling errors and character substitutions at the akshara level. bigrucorrector.pt: A sequence-to-sequence bidirectional GRU encoder-decoder model with Attention (BiGRUSeq2Seq) that performs generative character/akshara corrections. aksharavocab.json: Vocabulary mappings mapping Sinhala phonological units (aksharas) and basic…

Model · Speech recognition

faster-whisper-small.en

Systran

This repository contains the conversion of openai/whisper-small.en to the CTranslate2 model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as faster-whisper. The original model was converted with the following command: Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the computetype option in CTranslate2. For more information about the original model, see its model card.

Model · Image and text to text

Tiel-Coder-35B-A3B-GGUF-MTP

Saga

Tiel is the fast coder of the arsenal. At 4-bit quantization and 23 GB it fixes real codebase issues at the rate (and speed, with the right GPU) of Opus 4.6 medium, while holding the best multi-turn conversation of any local model we have measured. It is also cheerfully bad at trivia. Pick it for work. Pick something else for exams. Where it sits against the other local builds Multi-turn conversation Reasoning and knowledge Where it stands. On 25 SWE-bench-Live problems Tiel fixes 12 — the same as Opus 4.6 (medium), four more than Ornith-1.5 itself, three more than Nail, and four more than Sonnet 5 (medium). Among models of its own class it is first; the ones ahead are dense 27Bs and Opus…

Model · Fill mask

deberta-v3-small

Microsoft

DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data. In DeBERTa V3, we further improved the efficiency of DeBERTa using ELECTRA-Style pre-training with Gradient Disentangled Embedding Sharing. Compared to DeBERTa, our V3 version significantly improves the model performance on downstream tasks. You can find more technique details about the new model from our paper. Please check the official repository for more implementation details and updates. The DeBERTa V3 small model comes with 6 layers and a hidden size of 768. It has 44M backbone…

Model · Text generation

JiRackUltra_14b

Center Business Solutions inc

A fast and efficient 14B model optimized for CPU inference. The model was refactored with BitNet features and an updated tokenizer that includes new Routing, Media, Vision, Sound, Tool call, and Robotics tags. Built on a DeepSeek R1-14B architecture with native ternary (BitNet-style) support and ready-to-run GGUF quantizations. - JiRack is a cloud-ready model that helps save money on cloud infrastructure. It can be used as an expert model in RAG deployments, with the ONNX JiRack Java server as an alternative. - Benefits high quality CPU inference TQ2 on Llama.cpp and Ollama via QAT - Robotcs, Routing, Coding, Multimedia, Advanced tool calling via CMSManhattan/JiRackPrecisionTokenizer…

Model

m2m100_418M

AI at Meta

M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation. It was introduced in this paper and first released in this repository. The model that can directly translate between the 9,900 directions of 100 languages. To translate into a target language, the target language id is forced as the first generated token. To force the target language id as the first generated token, pass the forcedbostokenid parameter to the generate method. Note: M2M100Tokenizer depends on sentencepiece, so make sure to install it before running the example. To install sentencepiece run pip install sentencepiece See the model hub to look for more fine-tuned…

Model · Fill mask

juribert-base

DaSciM

JuriBERT is a set of BERT models (tiny, mini, small and base) pre-trained from scratch on French legal-domain specific corpora. JuriBERT models are pretrained on 6.3GB of legal french raw text from two different sources: the first dataset is crawled from Légifrance and the other one consists of anonymized court’s decisions and the pleadings from the Court of Cassation (mémoires ampliatifs). The latter contains more than 100k long documents from different court cases. It is now on Hugging Face in four different versions with varying number of parameters. JuriBERT was trained and evaluated at École Polytechnique in collaboration with HEC Paris by Stella Douka, Hadi Abdine, Mihcalis…

Model · Fill mask

Bio_Discharge_Summary_BERT

Emily Alsentzer

The Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models: initialized with BERT-Base (casedL-12H-768A-12) or BioBERT (BioBERT-Base v1.0 + PubMed 200K + PMC 270K) & trained on either all MIMIC notes or only discharge summaries. This model card describes the Bio+Discharge Summary BERT model, which was initialized from BioBERT & trained on only discharge summaries from MIMIC. The BioDischargeSummaryBERT model was trained on all discharge summaries from MIMIC III, a database containing electronic health records from ICU patients at the Beth Israel Hospital in Boston, MA. For more details on MIMIC, see here. All notes from the NOTEEVENTS table were included…

Model · Fill mask

mmBERT-base

Center for Language and Speech Processing @ JHU

mmBERT is a modern multilingual encoder that significantly outperforms previous generation models like XLM-R on classification, embedding, and retrieval tasks. Built on the ModernBERT architecture with novel multilingual training innovations, mmBERT demonstrates that low-resource languages can be effectively learned during the decay phase of training. It is also significantly faster than any previous multilingual encoder. mmBERT represents the first significant advancement over XLM-R for massively multilingual encoder models. Key features include: 1. Massive Language Coverage - Trained on over 1800 languages with progressive inclusion strategy 2. Modern Architecture - Built on ModernBERT…

Model · Fill mask

bertweet-base

VinAI Research

BERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is trained based on the RoBERTa pre-training procedure. The corpus used to pre-train BERTweet consists of 850M English Tweets (16B word tokens ~ 80GB), containing 845M Tweets streamed from 01/2012 to 08/2019 and 5M Tweets related to the COVID-19 pandemic. The general architecture and experimental results of BERTweet can be found in our paper: author = {Dat Quoc Nguyen and Thanh Vu and Anh Tuan Nguyen}, pages = {9--14}, year = {2020} Please CITE our paper when BERTweet is used to help produce published results or is incorporated into other software. For further information or requests, please go…

This repository contains the official trained policies for The files are organized by task environment and training seed: The play.py script in the JoSE repository downloads a pretrained model and executes it in the corresponding environment, allowing agent behaviour to be visualized. The script is called from the repo root as follows: Seeds vary by hand and task. See the HuggingFace model directory to see which seeds are available for each task-hand combination.

Model · Translation

sanskrit-en-custom-transformer

Praveen Kumar

A compact encoder–decoder Transformer trained from scratch on 10,000 Sanskrit–English pairs (NLU Assignment 2), with a joint SentencePiece BPE vocabulary. It is small and fast — meant to be efficient rather than to match large pretrained models. This is not a Transformers architecture, so it ships with a self-contained modeling.py. Files: pytorchmodel.bin (weights), spm.model (tokenizer), config.json (hyperparameters), modeling.py (model + load/translate helpers). pip install gradio first. To host it, create a Hugging Face Space (SDK: Gradio) with an app.py (the load + respond code) and a requirements.txt of torch sentencepiece huggingfacehub gradio. Trained only on the provided dataset…

Model · Fill mask

deberta-base

Microsoft

DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data. Please check the official repository for more details and updates. We present the dev results on SQuAD 1.1/2.0 and MNLI tasks. If you find DeBERTa useful for your work, please cite the following paper

Model · Fill mask

esm2_t36_3B_UR50D

AI at Meta

ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective. It is suitable for fine-tuning on a wide range of tasks that take protein sequences as input. For detailed information on the model architecture and training data, please refer to the accompanying paper. You may also be interested in some demo notebooks (PyTorch, TensorFlow) which demonstrate how to fine-tune ESM-2 models on your tasks of interest. Several ESM-2 checkpoints are available in the Hub with varying sizes. Larger sizes generally have somewhat better accuracy, but require much more memory and time to train

Model · Text to speech

MeloTTS-English

MyShell.ai

MeloTTS is a high-quality multi-lingual text-to-speech library by MIT and MyShell.ai. Supported languages include: - The Chinese speaker supports mixed Chinese and English. - Fast enough for CPU real-time inference. - Wenliang Zhao at Tsinghua University - Xumin Yu at Tsinghua University - Zengyi Qin (project lead) at MIT and MyShell Citation An unofficial live demo is hosted on Hugging Face Spaces. There are hundreds of TTS models on MyShell, much more than MeloTTS. See examples here. More can be found at the widget center of MyShell.ai. Follow the installation steps here before using the following snippet: Open Source AI Grant We are actively sponsoring open-source AI projects. The…

Model · Fill mask

BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext

Microsoft

This model was previously named "PubMedBERT (abstracts + full text)". You can either adopt the new model name "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext" or update your transformers library to version 4.22+ if you need to refer to the old name. Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A prevailing assumption is that even domain-specific pretraining can benefit by starting from general-domain language models. Recent work shows that for domains with abundant unlabeled text, such as…

Model · Fill mask

SaProt_650M_AF2

Westlake Repl

We provide two ways to use SaProt, including through huggingface class and through the same way as in esm github. Users can choose either one to use. The following code shows how to load the model. The esm version is also stored in the same folder, named SaProt650MAF2.pt. We provide a function to load the model. We provide a function to predict the mutational effect of a protein sequence. The example below shows how to predict the mutational effect at a specific position. If using the AF2 structure, we strongly recommend that you add pLDDT mask (see below). If you want to generate protein embeddings, you could refer to the following code. The embeddings are the average of the hidden states…

BERTimbau Base is a pretrained BERT model for Brazilian Portuguese that achieves state-of-the-art performances on three downstream NLP tasks: Named Entity Recognition, Sentence Textual Similarity and Recognizing Textual Entailment. It is available in two sizes: Base and Large. For further information or requests, please go to BERTimbau repository. If you use our work, please cite

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 · Audio classification

accent-id-commonaccent_ecapa

Juan Pablo Zuluaga

Abstract: The recognition of accented speech still remains a dominant problem in Automatic Speech Recognition (ASR) systems. We approach the classification of accented English speech through the Emphasized Channel Attention, Propagation and Aggregation Time Delay Neural Network (ECAPA-TDNN) architecture which has been shown to perform well on a variety of speech tasks. Three models are proposed: one trained from scratch, another two models (one using data augmentation and a baseline model) fine-tuned from the checkpoints of speechbrain/spkrec-ecapa-voxceleb (VoxCeleb). Our results show that the model fine-tuned with data augmentation yield the best results. Most of the misclassifications…

Model · Image classification

swin_tiny_patch4_window7_224.ms_in1k

PyTorch Image Models

A Swin Transformer image classification model. Pretrained on ImageNet-1k by paper authors. Explore the dataset and runtime metrics of this model in timm model results.

Model

m2m100_1.2B

AI at Meta

M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation. It was introduced in this paper and first released in this repository. The model that can directly translate between the 9,900 directions of 100 languages. To translate into a target language, the target language id is forced as the first generated token. To force the target language id as the first generated token, pass the forcedbostokenid parameter to the generate method. Note: M2M100Tokenizer depends on sentencepiece, so make sure to install it before running the example. To install sentencepiece run pip install sentencepiece See the model hub to look for more fine-tuned…