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A taxonomy-informed sparse DNA foundation model for microbial genomics.

An Ontix autoencoder with an explainable, 16-dimensional latent space, trained on single-cell RNA-seq data. Each latent dimension is constrained by a gene ontology term generated with Kimi K3, making the embedding directly interpretable in terms of biological processes.

An Ontix autoencoder with an explainable, 16-dimensional latent space, trained on single-cell RNA-seq data. Each latent dimension is constrained by a gene ontology term generated with GPT-5.6 TerraPro, making the embedding directly interpretable in terms of biological processes.

An Ontix autoencoder with an explainable, 16-dimensional latent space, trained on single-cell RNA-seq data. Each latent dimension is constrained by a gene ontology term generated with Claude Opus 5, making the embedding directly interpretable in terms of biological processes.

Contrastive LEarning with Soft Targets from TCRdist. Checkpoint SCEPTR6LACsoft800k20ep_bs1024.

Three genomic foundation models, packaged together for local inference on Apple silicon.

Iona is a transformer encoder foundation model for tandem mass spectra (MS/MS). It treats each centroided peak as a token and learns how peaks relate to each other through a per-head attention bias over the signed m/z difference (Δm/z) between every pair of peaks.

Iona is a transformer encoder foundation model for tandem mass spectra (MS/MS). It treats each centroided peak as a token and learns how peaks relate to each other through a per-head attention bias over the signed m/z difference (Δm/z) between every pair of peaks.

Iona is a transformer encoder foundation model for tandem mass spectra (MS/MS). It treats each centroided peak as a token and learns how peaks relate to each other through a per-head attention bias over the signed m/z difference (Δm/z) between every pair of peaks.

Iona is a transformer encoder foundation model for tandem mass spectra (MS/MS). It treats each centroided peak as a token and learns how peaks relate to each other through a per-head attention bias over the signed m/z difference (Δm/z) between every pair of peaks.

This model card provides an overview of the intended use of the ESMC SAE models and examples of how to access them, but it does not have a specific model or model weights. To access each SAE model collection, use the links below:

PhenoSeq is a Gaussian diffusion model that generates scGPT RNA-seq embeddings conditioned on ViT-L microscopy imaging features. Given fluorescence microscopy images of a cell or well, it predicts a 512-dimensional scGPT embedding representing the transcriptomic state of individual cells — enabling…

scRep is a PyTorch model for extracting cell embeddings from single-cell RNA-seq AnnData (.h5ad) inputs. This repository is a standalone Hugging Face release bundle: it includes checkpoint weights, the exact paired gene vocabulary, the inference implementation, and runnable examples.

In retrieval systems, embedding models determine the quality of your search.

MoLFormer is a class of models pretrained on SMILES string representations of up to 1.1B molecules from ZINC and PubChem. This repository is for the model pretrained on 10% of both datasets.

scUNVEIL is a pretrained foundation model for human single-cell RNA sequencing (scRNA-seq).

# Overview This is the CellHermes model, based on the LLaMA-3.1-8B-instruct architecture developed by Meta, fine-tuned using single-cell RNA sequencing (scRNA-seq) datasets from CellxGene and PPI network from BioGRID. CellHermes is an innovative framework for adapting existing large language models…

PlantGeneAnn is a plant genome foundation model that enables the prediction of various plant genomic elements at single-nucleotide resolution. The model is built upon the PlantBiMoE architecture with a 1D U-Net segmentation head, specifically designed for automated plant genome annotation.

Jolia is a 3D CT foundation model that encodes images into vector representations program. It encodes a whole 3D CT volume into:

JEPA-based spatial-transcriptomics foundation model (TERRA). Code & docs: https://github.com/Lotfollahi-lab/terra

This Hugging Face repository stores the official resources for RAMER (reaction-aware multimodal enzyme function representation model).

This model card provides an overview of the intended use of the ESMC SAE models and examples of how to access them, but it does not have a specific model or model weights. To access each SAE model collection, use the links below:

This model card provides an overview of the intended use of the ESMC SAE models and examples of how to access them, but it does not have a specific model or model weights. To access each SAE model collection, use the links below:

GENATATOR-PIPELINE is a Hugging Face pipeline for ab initio gene annotation from genomic DNA. It accepts a FASTA file, finds candidate transcript intervals, assigns transcript type, predicts exon and CDS structure, and writes a GFF3 annotation file.

MIST is a family of molecular foundation models for molecular property prediction. The models were pre-trained on SMILES strings from the Enamine REAL Space dataset using the Masked Language Modeling (MLM) objective, then fine-tuned for downstream prediction tasks.

A patient-level disease classification model trained on single-cell RNA-seq data. Given a matrix of gene expression profiles (one row per cell), the model produces a disease-category prediction for the patient.

A 50% magnitude-pruned version of facebook/esm2t1235MUR50D optimized for efficient drug discovery inference on Apple Silicon.

DrugCLIP is a dual-encoder multimodal model (SchNet 3D Graph Neural Network + DistilBERT Text Encoder) mapped to a shared 128-dimensional latent space. It is designed to evaluate and retrieve novel 3D molecular structures by aligning them with natural language therapeutic intents and clinical…

Github | Cite

Github | Cite

RNAElectra is a nucleotide-resolution RNA language model trained using an ELECTRA-style objective for efficient and discriminative representation learning. The model produces contextualized embeddings for RNA sequences and is designed for downstream transcriptomic and regulatory modeling tasks.

BulkRNABert is a transformer-based, encoder-only language model pre-trained on bulk RNA-seq profiles from the TCGA dataset using self-supervised masked language modeling, following the original BERT framework. The model is trained to reconstruct randomly masked gene expression values from their…

GeneJEPA is a Joint-Embedding Predictive Architecture (JEPA) trained for self-supervised representation learning on scRNA-seq. It uses a Perceiver-style encoder to handle sparse, high-dimensional gene count vectors and a Fourier-feature tokenizer for numerical tokenization.

> A CMR-report contrastive model combining Vision Transformers and pretrained text encoders.

This repository contains pre-trained models from RadImageNet, a large-scale radiologic image dataset designed to facilitate transfer learning for medical imaging applications.

Welcome to IBM's series of large foundation models for sustainable materials. Our models span a variety of representations and modalities, including SMILES, SELFIES, 3D atom positions, 3D density grids, molecular graphs, and other formats.

> [!IMPORTANT] > 🎉 Check out the latest version of Phikon here: Phikon-v2 > > Phikon is a self-supervised learning model for histopathology trained with iBOT.

Model Size: 7B

Model Size: 8B (English)

한국어 모델을 이용한 SapBERT(Self-alignment pretraining for BERT)입니다. 한·영 의료 용어 사전인 KOSTOM을 사용해 한국어 용어와 영어 용어를 정렬했습니다. 참고: SapBERT, Original Code

## Quick Start ```Python from transformers import AutoTokenizer, AutoModel

A Vision Transformer (ViT) image classification model. \ Trained on 15M histology patches from PAIP and TCGA. \ Used the MoCo v3 self supervised learning method.