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Genos-m is a foundation model for human-associated microbial genomes. It is trained to model microbial DNA sequences at single-nucleotide resolution and supports ultra-long genomic contexts up to one million tokens.

Qwen3-8B-syco_med-gated-attention-FT is a plug-and-play gated attention weight released for AI safety research.

LoRA fine-tune of Qwen3.5-9B on synthetic clinical triage Q&A pairs generated from PubMed Central open-access papers. The model is specialized for emergency-medicine decision-making: triaging patients, applying clinical decision rules, and generating protocol-grounded triage recommendations.

Gemma 4 E2B fine-tuned on 225K drug–target pairs for novel small-molecule generation.

- 2025-05-15: We identified a bug in the Bacformer Large code on HuggingFace which resulted in a significant drop in the quality of the output embeddings. This is now fixed, but if you downloaded or cached the model before this date, re-download and use the latest model revision before running…

- 2025-05-15: We identified a bug in the Bacformer Large code on HuggingFace which resulted in a significant drop in the quality of the output embeddings. This is now fixed, but if you downloaded or cached the model before this date, re-download and use the latest model revision before running…

For a convenient overview and download list, visit our model page for this model.

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.

MedPsy-4B is a state-of-the-art, text-only medical and healthcare language model purpose-built for edge deployment. Built on top of Qwen3-4B-Thinking-2507 and post-trained with a multi-stage pipeline (supervised fine-tuning + reinforcement learning) on curated medical data, it surpasses models…

MedPsy-1.7B is a state-of-the-art, text-only medical and healthcare language model purpose-built for edge and smartphone deployment. Built on top of Qwen3-1.7B (operated in thinking mode, i.e. with enable_thinking=True) and post-trained with a multi-stage pipeline (supervised fine-tuning +…

## Introduction (简介) This model is a domain-specific expert fine-tuned from Qwen/Qwen2.5-7B-Instruct using LoRA (Low-Rank Adaptation). It is specifically designed for Fine-grained Information Extraction (IE) of technical indicator quintuples from highly complex lithium-ion battery patents.

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

Duchifat-2.3-Instruct is a state-of-the-art, instruction-tuned Large Language Model developed by TopAI. As the flagship of the Duchifat series, this model represents a fundamental breakthrough in how Hebrew is processed, reasoned, and generated in the LLM era.

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A generalist foundation model for healthcare capable of handling diverse medical data modalities.

Fine-tuned version of google/gemma-4-E4B-it across three professional domains — Medical, Legal, and Finance — using QLoRA (4-bit NF4) with Optuna-tuned hyperparameters, trained on Kaggle T4 GPU.

L1 (Learning Unit 1) is the first language model from Lunit and Lunit Consortium, purpose-built for the medical domain. Derived from Gravity-16B-A3B-Base, L1 is designed for clinical reasoning and decision support.

## Model Description This is a lightweight, high-performance image classification model built to diagnose histopathological scans of lung and colon tissues. This model was specifically designed for rapid web deployment without sacrificing clinical accuracy.

MarkushGrapher-2 is an end-to-end multimodal model for recognizing chemical structures from patent document images. It jointly encodes vision, text, and layout information to convert Markush structure images into machine-readable CXSMILES representations.

ChemicalOCR is a compact vision-language model fine-tuned specifically for optical character recognition (OCR) in chemical structure images. It extracts text and bounding boxes from molecular drawings, enabling the recognition of atom labels, abbreviations, and descriptive text within chemical…

# GigaHeart ## A Cardiac-specific CT Foundation Model for Heart Transplantation

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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.

Vortex Scientific is a from-scratch AI model family designed for deep scientific reasoning. Built from the ground up with a novel hybrid state-space + attention architecture, optimized for consumer laptop hardware (Apple Silicon MacBooks and Nvidia 4060 laptop GPUs).

In search enginers, rerankers are crucial for improving the accuracy of your retrieval system.

This repository contains the model presented in D3LM: A Discrete DNA Diffusion Language Model for Bidirectional DNA Understanding and Generation.

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Sahal Shaji Mullappilly\, Mohammed Irfan K\, Omair Mohamed, Mohamed Zidan, Fahad Khan, Salman Khan, Rao Muhammad Anwer, and Hisham Cholakkal

A compact protein language model distilled from ProtGPT2 using complementary-regularizer distillation---a method that combines uncertainty-aware position weighting with calibration-aware label smoothing to achieve 31% better perplexity than standard knowledge distillation at 3.8x compression.

A compact protein language model distilled from ProtGPT2 using complementary-regularizer distillation---a method that combines uncertainty-aware position weighting with calibration-aware label smoothing to achieve 54% better perplexity than standard knowledge distillation at 9.4x compression.

A compact protein language model distilled from ProtGPT2 using complementary-regularizer distillation---a method that combines uncertainty-aware position weighting with calibration-aware label smoothing to achieve 87% better perplexity than standard knowledge distillation at 20x compression.

Fine-tuned version of Google's MedGemma-4B-it model on the ECGInstruct dataset for automated ECG interpretation.

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