Models
rbio1 is a 3B biology reasoning model from the Chan Zuckerberg Initiative, post-trained from Qwen2.5-3B-Instruct by reinforcement learning (GRPO) with…
An Amharic-language variant of Gemma-3-27B-MeditronFO, from EPFL's LiGHT laboratory and the MeditronFO pipeline.
A medical specialisation of Gemma-3-27B-it, fine-tuned on the Fully Open Meditron corpus (about 601k clinician-vetted medical QA and clinical-vignette…
A medical specialisation of EuroLLM-9B-Instruct, fine-tuned on the Fully Open Meditron corpus (about 601k clinician-vetted medical QA and clinical-vignette…
A medical specialisation of EuroLLM-22B-Instruct, fine-tuned on the Fully Open Meditron corpus (about 601k clinician-vetted medical QA and clinical-vignette…
A medical specialisation of OLMo-2-32B, fine-tuned on the Fully Open Meditron corpus (about 601k clinician-vetted medical QA and clinical-vignette examples)…
A medical specialisation of Apertus-8B-Instruct, fine-tuned on the Fully Open Meditron corpus (about 601k clinician-vetted medical QA and clinical-vignette…
A medical specialisation of Apertus-70B-Instruct, fine-tuned on the Fully Open Meditron corpus (about 601k clinician-vetted medical QA and clinical-vignette…
Baichuan-M3 is a clinical large language model from Baichuan Inc., a 235B mixture-of-experts model built on Qwen3-235B-A22B.
MedGemma 1.5 is Google's 4B multimodal medical model, built on Gemma 3.
Cell2Sentence-Scale (C2S-Scale) model from the van Dijk lab (Yale) and Google, fine-tuned from Gemma 2 27B.
Cell2Sentence-Scale (C2S-Scale) model from the van Dijk lab (Yale) and Google, fine-tuned from Gemma 2 2B.
TxGemma is a family of Google models fine-tuned from Gemma 2 on therapeutic-development tasks from the Therapeutics Data Commons (TDC), covering small…
TxGemma is a family of Google models fine-tuned from Gemma 2 on therapeutic-development tasks from the Therapeutics Data Commons (TDC), covering small…
TxGemma is a family of Google models fine-tuned from Gemma 2 on therapeutic-development tasks from the Therapeutics Data Commons (TDC), covering small…
TxGemma is a family of Google models fine-tuned from Gemma 2 on therapeutic-development tasks from the Therapeutics Data Commons (TDC), covering small…
TxGemma is a family of Google models fine-tuned from Gemma 2 on therapeutic-development tasks from the Therapeutics Data Commons (TDC), covering small…
Biomni-R0-32B-Preview is a biomedical AI agent model from Stanford (Leskovec lab): Qwen3-32B trained by multi-turn reinforcement learning inside the Biomni…
Intern-S2-Preview-397B is the largest Intern-S2 preview from Shanghai AI Laboratory (InternLM): a 397B multimodal model for scientific reasoning and…
Intern-S2-Preview is a 35B multimodal scientific foundation model from Shanghai AI Laboratory (InternLM), trained on hundreds of professional scientific tasks…
Intern-S1-Pro is a trillion-parameter mixture-of-experts multimodal scientific reasoning model from Shanghai AI Laboratory (InternLM): 1T parameters in 512…
Intern-S1-mini is the lightweight Intern-S1 from Shanghai AI Laboratory (InternLM): an 8B Qwen3 language model with a 0.3B InternViT vision encoder, further…
Intern-S1 is an open multimodal scientific reasoning model from Shanghai AI Laboratory (InternLM): a Qwen3-235B mixture-of-experts language model with a 6B…
Source code repository mirrored from GitHub (bowang-lab/scGPT).
Geneformer Geneformer is a foundational transformer model pretrained on a large-scale corpus of human single cell transcriptomes to enable context-aware…
This is the official pre-trained model introduced in DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species
The Nucleotide Transformers are a collection of foundational language models that were pre-trained on DNA sequences from whole-genomes.
The Nucleotide Transformers are a collection of foundational language models that were pre-trained on DNA sequences from whole-genomes.
The Nucleotide Transformers are a collection of foundational language models that were pre-trained on DNA sequences from whole-genomes.
ESMFold is a state-of-the-art end-to-end protein folding model based on an ESM-2 backbone.
esm3-sm-open-v1 is trained on 2.78 billion natural proteins.
This set of model weights was released with the GitHub-compatible esm package format.
An encoder-only, half-precision version of the ProtT5-XL-UniRef50 model.
Pretrained model on protein sequences using a masked language modeling (MLM) objective.
ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective.
ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective.
ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective.
An Evolutionary-scale Model (ESM) for protein function prediction from amino acid sequences using the Gene Ontology (GO).
Model description - Intended uses and limitations - How to use - Limitations and bias - Training - Tokenization and model pretraining - Training corpora and…
This repository contains a fine-tuned Vision Transformer (ViT) model for classifying chest X-ray images, utilizing the CheXpert dataset.
Model Card for ChemBERTa-10M-MTR Model Details Model Description More information needed - Developed by: DeepChem - Shared by [Optional]:
This repository contains 82 Clan-level protein language model checkpoints generated for the study “Does domain-specific unsupervised fine-tuning improve…
This repository contains 171 Clan-level protein language model checkpoints generated for the study “Does domain-specific unsupervised fine-tuning improve…
This repository contains 254 Pfam-level protein language model checkpoints generated for the study “Does domain-specific unsupervised fine-tuning improve…
This repository contains 356 Pfam-level protein language model checkpoints generated for the study “Does domain-specific unsupervised fine-tuning improve…
An Evolutionary-scale Model (ESM) for protein function prediction from amino acid sequences using the Gene Ontology (GO).
ESM Protein Localization Model Model description The ESM Protein Localization Model is a deep learning model trained on protein sequences to predict their…
An Evolutionary-scale Model (ESM) for protein function prediction from amino acid sequences using the Gene Ontology (GO).
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or…
BiomedCLIP is a biomedical vision-language foundation model that is pretrained on PMC-15M, a dataset of 15 million figure-caption pairs extracted from…
This is a PubMedBERT-base model fined-tuned using sentence-transformers.
SapBERT-PubMedBERT SapBERT by Liu et al.
The Clinical Assertion and Negation Classification BERT is introduced in the paper Assertion Detection in Clinical Notes: Medical Language Models to the…
This model card describes the ClinicalBERT model, which was trained on a large multicenter dataset with a large corpus of 1.2B words of diverse diseases we…
A Named Entity Recognition model for clinical entities (problem, treatment,
Model description - Intended uses and limitations - How to use - Training - Evaluation - Additional information - Author - Licensing information - Citation…
text-generation artifact mirrored from the HuggingFace Hub.
About the Model An English Named Entity Recognition model, trained on Maccrobat to recognize the bio-medical entities (107 entities) from a given text corpus…
ChemBERTa: Training a BERT-like transformer model for masked language modelling of chemical SMILES
Pretrained model on protein sequences using a masked language modeling (MLM) objective.
ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective.
ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective.
ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective.
CXR-BERT is a chest X-ray (CXR) domain-specific language model that makes use of an improved vocabulary, novel pretraining procedure, weight regularization,…
Note: This model was previously known as PubMedGPT 2.7B, but we have changed it due to a request from the NIH which holds the trademark for
Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language…
The Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models: initialized with BERT-Base (casedL-12H-768A-12) or BioBERT…
fill-mask artifact mirrored from the HuggingFace Hub.
This is the pretrained model presented in SciBERT: A Pretrained Language Model for Scientific Text, which is a BERT model trained on scientific
This model was previously named "PubMedBERT (abstracts + full text)".