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Palmyra-Med, a powerful LLM designed for healthcare

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Neeto-1.0-8b is an openly released biomedical large language model (LLM) created by BYOL Academy to assist learners and practitioners with medical exam study, literature understanding, and structured clinical reasoning.

This repository contains the official model of the paper A Unified Predictive and Generative Solution for Liquid Electrolyte Formulation.

This is a ReactionT5 pre-trained to predict the products of reactions. You can use the demo here.

This is a ReactionT5 pre-trained to predict the reactants of reactions. You can use the demo here.

This repos contains the biomedicine MLLM developed from Qwen2.5-VL-3B-Instruct in our paper: On Domain-Adaptive Post-Training for Multimodal Large Language Models. The correspoding training dataset is in biomed-visual-instructions.

# LAMAR LAMAR is a Foundation Language Model for RNA Regulation, which achieves better or comparable performance compared to baseline models in various RNA regulation tasks, helping to decipher the rules of RNA regulation.

# Position-based Equivariant Graph Neural Network (pos-egnn) This repository contains PyTorch model for loading and performing inference using the pos-egnn, a foundation model for Chemistry and Materials.

Specialized model for Chemical Entity Recognition - Identifies chemical compounds and substances in biomedical literature

Specialized model for Chemical Entity Recognition - Identifies chemical compounds and substances in biomedical literature

Specialized model for Chemical Entity Recognition - Chemical entities from the BC5CDR dataset

A specialized medical embedding model fine-tuned from Clinical ModernBERT using InfoNCE contrastive learning on PubMed title-abstract pairs.

This repository provides quantized GGUF versions of the google/medgemma-4b-it model. These 4-bit and 5-bit quantized variants retain the original model’s strengths in multimodal medical reasoning, while reducing memory and compute requirements—ideal for efficient inference on resource-constrained…

JAMUN is a novel approach for generating conformational ensembles of protein structures, presented in the paper JAMUN: Bridging Smoothed Molecular Dynamics and Score-Based Learning for Conformational Ensembles.

This project supports all types of 2D medical X-ray images, including chest, dental, and others. It performs super-resolution and denoising using a RealESRGAN-based model.

Highly focused on medical Training datasets ; + Upgraded inplace

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This model classifies facial skin images into 6 common dermatological conditions using a fine-tuned EfficientNetV2B0 architecture.

darkknight25/deepseek-16b-medical-GPT is a fine-tuned version of deepseek-ai/deepseek-l6b-moe-chat, optimized for medical question answering, reasoning, and clinical summarization using QLoRA and open-access healthcare datasets.

Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.

This is a merge of pre-trained language models created using mergekit, combining the specialty and general reasoning skills of Esper 3 8b and Shining Valiant 3 8b.

Using llama.cpp release b5868 for quantization.

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

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

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

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

Segment Anything in 3D Medical Images and Videos

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

Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.

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

RaDialog

!image # Compumacy-Experimental_MF ## A Specialized Language Model for Clinical Psychology & Psychiatry

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.

This is the official pre-trained model introduced in DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome .

Original code at (https://github.com/Edoar-do/HuBERT-ECG)

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