Open-Weight Models

Model · Any to any

kai-os_Grug-12B-GGUF

Bartowski

Using llama.cpp release b10068 for quantization. All quants made using imatrix option with dataset from here Run them in your choice of tools: Note: if it's a newly supported model, you may need to wait for an update from the developers. Some of these quants (Q3KXL, Q4KL etc) are the standard quantization method with the embeddings and output weights quantized to Q80 instead of what they would normally default to. First, make sure you have huggingface-cli installed: Then, you can target the specific file you want: If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: You can either specify a new local-dir…

Model · Any to any

gemma-4-12B-it-AWQ-INT4

Cyankiwi

Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on 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 · Question answering

deberta-v3-base-squad2

Deepset

This is the deberta-v3-base model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering. Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack: For a complete example with an extractive question answering pipeline that scales over many documents, check out the corresponding Haystack tutorial. Timo Möller: timo.moeller [at] deepset.ai deepset is the company behind the production-ready open-source AI framework Haystack. We also…

Model · Image to text

trocr-base-printed

Microsoft

TrOCR model fine-tuned on the SROIE dataset. It was introduced in the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Li et al. and first released in this repository. Disclaimer: The team releasing TrOCR did not write a model card for this model so this model card has been written by the Hugging Face team. The TrOCR model is an encoder-decoder model, consisting of an image Transformer as encoder, and a text Transformer as decoder. The image encoder was initialized from the weights of BEiT, while the text decoder was initialized from the weights of RoBERTa. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which…

Model · Token classification

piiranha-v1-detect-personal-information

The Internet Integrity Initiative

william (at) integrinet [dot] org Piiranha (cc-by-nc-nd-4.0 license) is trained to detect 17 types of Personally Identifiable Information (PII) across six languages. It successfully catches 98.27% of PII tokens, with an overall classification accuracy of 99.44%. Piiranha is especially accurate at detecting passwords, emails (100%), phone numbers, and usernames. Performance on PII vs. Non PII classification task: Piiranha was trained on H100 GPUs generously sponsored by the Akash Network Piiranha is a fine-tuned version of microsoft/mdeberta-v3-base. The context length is 256 Deberta tokens. If your text is longer than that, just split it up. Supported PII types: Account Number, Building…

Model · Text classification

bert-log-anomaly-detection

Aungruk Vanichanai

1. bert-log-anomaly-detection is a BERT-based NLP model fine-tuned for single SQL transaction log anomaly detection. 2. The model classifies each database transaction log as either Normal or Anomaly, with the goal of supporting AI-powered fraud detection and cybersecurity monitoring systems. 3. This model was developed as part of the Samsung × KBTG Digital Fraud Cybersecurity Hackathon (Thailand) under the AI-Powered Fraud Detection & Prevention track. This model analyzes individual SQL database transaction logs and detects abnormal patterns that may indicate fraudulent, malicious, or suspicious behavior. - Developed by Waris Sripatoomrak, this model integrates with an n8n workflow to…

Model · Token classification

span-marker-bert-base-uncased-acronyms

Tom Aarsen

This is a SpanMarker model trained on the Acronym Identification dataset that can be used for Named Entity Recognition. This SpanMarker model uses bert-base-uncased as the underlying encoder. See train.py for the training script. Is your data always capitalized correctly? Then consider using the cased variant of this model instead for better performance: tomaarsen/span-marker-bert-base-acronyms. You can finetune this model on your own dataset. - learningrate: 5e-05 - trainbatchsize: 32 - evalbatchsize: 32 - lrschedulertype: linear - lrschedulerwarmupratio: 0.1 - numepochs: 2 Carbon emissions were measured using CodeCarbon.

Model · Text classification

roberta-large-mnli

Facebook AI community

roberta-large-mnli is the RoBERTa large model fine-tuned on the Multi-Genre Natural Language Inference (MNLI) corpus. The model is a pretrained model on English language text using a masked language modeling (MLM) objective. Use the code below to get started with the model. The model can be loaded with the zero-shot-classification pipeline like so: You can then use this pipeline to classify sequences into any of the class names you specify. For example: This fine-tuned model can be used for zero-shot classification tasks, including zero-shot sentence-pair classification (see the GitHub repo for examples) and zero-shot sequence classification. The model should not be used to intentionally…

Model · Image classification

vit-age-classifier

Nate Raw

A vision transformer finetuned to classify the age of a given person's face.

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 · Token classification

ner-english

Flair

This is the standard 4-class NER model for English that ships with Flair. Based on Flair embeddings and LSTM-CRF. So, the entities "George Washington" (labeled as a person) and "Washington" (labeled as a location) are found in the sentence "George Washington went to Washington". The following Flair script was used to train this model: Please cite the following paper when using this model. The Flair issue tracker is available here.

Model · Text ranking

cross-encoder-russian-msmarco

Dmitry Tishencko

This is a sentence-transformers model based on a pre-trained DeepPavlov/rubert-base-cased and finetuned with MS-MARCO Russian passage ranking dataset. The model can be used for Information Retrieval in the Russian language: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See SBERT.net Retrieve & Re-rank for more details. Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you need to get the logits from the…

Model · Token classification

bert-small-pii-detection

Gravitee.io

Token-classification model for PII detection, fine-tuned from prajjwal1/bert-small on Detect personally identifiable information (PII) spans in english text. Suitable for privacy filtering, redaction pipelines, and data-leak prevention particularly on structured data (JSON, HTML, XML, SQL, Document) English-focused; other languages will degrade External-corpus evaluation (English only), seqeval. Last run: 2026-05-21. Data citation are present in the dataset card used for this model. If you use the model, please consider citing the papers

Model · Text classification

glotlid

CIS, LMU Munich

GlotLID is a Fasttext language identification (LID) model that supports more than 2000 labels. Latest: GlotLID is now updated to V3. V3 supports 2102 labels (three-letter ISO codes with script). For more details on the supported languages and performance, as well as significant changes from previous versions, please refer to https://github.com/cisnlp/GlotLID/blob/main/languages-v3.md. Here is how to use this model to detect the language of a given text: If you are not a fan of huggingfacehub, then download the model directyly: The model is distributed under the Apache License, Version 2.0 plus notices (see LICENSE file for full terms). We always maintain the previous version of GlotLID in…

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…

Model · Token classification

bert-base-multilingual-cased-ner-hrl

David Adelani

Hugging Face's logo - multilingual bert-base-multilingual-cased-ner-hrl is a Named Entity Recognition model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned mBERT base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). Specifically, this model is a bert-base-multilingual-cased model that was fine-tuned on an aggregation of 10 high-resourced languages You can use this model with Transformers pipeline for NER. This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may…

Model · Token classification

privacy-filter

OpenAI

OpenAI Privacy Filter is a bidirectional token-classification model for personally identifiable information (PII) detection and masking in text. It is intended for high-throughput data sanitization workflows where teams need a model that they can run on-premises that is fast, context-aware, and tunable. OpenAI Privacy Filter is pretrained autoregressively to arrive at a checkpoint with similar architecture to gpt-oss, albeit of a smaller size. We then converted that checkpoint into a bidirectional token classifier over a privacy label taxonomy, and post-trained with a supervised classification loss. (For architecture details about gpt-oss, please see the gpt-oss model card.) Instead of…

Model · Token classification

biomedical-ner-all

D 4 Data Community

An English Named Entity Recognition model, trained on Maccrobat to recognize the bio-medical entities (107 entities) from a given text corpus (case reports etc.). This model was built on top of distilbert-base-uncased Checkout the tutorial video for explanation of this model and corresponding python library: https://youtu.be/xpiDPdBpS18 The easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library. This model is part of the Research topic "AI in Biomedical field" conducted by Deepak John Reji, Shaina Raza. If you use this work (code, model or dataset), please star at

Model · Summarization

pegasus-xsum

Google

Original TF 1 code here Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019 The following is copied from the authors' README. We train a pegasus model with sampled gap sentence ratios on both C4 and HugeNews, and stochastically sample important sentences. The updated the results are reported in this table. The "Mixed & Stochastic" model has the following changes: - trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples). - trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity). - the model uniformly sample a gap sentence ratio between 15% and 45%. - importance sentences are sampled using a…

Model · Text to speech

orpheus-3b-0.1-ft

Canopy Labs

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 · Sentence similarity

Splade_PP_en_v1

Qdrant

ONNX port of prithivida/SpladePPenv1 for text classification and similarity searches. Here's an example of performing inference using the model with FastEmbed.

Model · Token classification

OpenMed-NER-ChemicalDetect-ModernMed-149M

OpenMed

Specialized model for Chemical Entity Recognition - Identifies chemical compounds and substances in biomedical literature This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for chemical entity recognition - identifies chemical compounds and substances in biomedical literature. 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…

Model · Token classification

OpenMed-NER-PharmaDetect-BigMed-278M

OpenMed

Specialized model for Chemical Entity Recognition - Chemical entities from the BC5CDR dataset This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for chemical entity recognition - chemical entities from the bc5cdr 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…