Find open-source science resources

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

# Model details ## Model description Nature Language Model (NatureLM) is a sequence-based science foundation model designed for scientific discovery. Pre-trained with data from multiple scientific domains, NatureLM offers a unified, versatile model that enables various applications including…

# Model details ## Model description Nature Language Model (NatureLM) is a sequence-based science foundation model designed for scientific discovery. Pre-trained with data from multiple scientific domains, NatureLM offers a unified, versatile model that enables various applications including…

An Evolutionary-scale Model (ESM) for protein function prediction from amino acid sequences using the Gene Ontology (GO). Based on the ESM2 Transformer architecture, pre-trained on UniRef50, and fine-tuned on the AmiGO dataset, this model predicts the GO subgraph for a particular protein sequence -…

Model documentation: MedGemma

An Evolutionary-scale Model (ESM) for protein function prediction from amino acid sequences using the Gene Ontology (GO). Based on the ESM2 Transformer architecture, pre-trained on UniRef50, and fine-tuned on the AmiGO dataset, this model predicts the GO subgraph for a particular protein sequence -…

An Evolutionary-scale Model (ESM) for protein function prediction from amino acid sequences using the Gene Ontology (GO). Based on the ESM2 Transformer architecture, pre-trained on UniRef50, and fine-tuned on the AmiGO dataset, this model predicts the GO subgraph for a particular protein sequence -…

This model mlx-community/medgemma-27b-text-it-bf16 was converted to MLX format from google/medgemma-27b-text-it using mlx-lm version 0.25.1.

An Evolutionary-scale Model (ESM) for protein function prediction from amino acid sequences using the Gene Ontology (GO). Based on the ESM2 Transformer architecture, pre-trained on UniRef50, and fine-tuned on the AmiGO dataset, this model predicts the GO subgraph for a particular protein sequence -…

An Evolutionary-scale Model (ESM) for protein function prediction from amino acid sequences using the Gene Ontology (GO). Based on the ESM2 Transformer architecture, pre-trained on UniRef50, and fine-tuned on the AmiGO dataset, this model predicts the GO subgraph for a particular protein sequence -…

This model had been created as part of joint research of HUMADEX research group (https://www.linkedin.com/company/101563689/) and has received funding by the European Union Horizon Europe Research and Innovation Program project SMILE (grant number 101080923) and Marie Skłodowska-Curie Actions…

This model had been created as part of joint research of HUMADEX research group (https://www.linkedin.com/company/101563689/) and has received funding by the European Union Horizon Europe Research and Innovation Program project SMILE (grant number 101080923) and Marie Skłodowska-Curie Actions…

This model had been created as part of joint research of HUMADEX research group (https://www.linkedin.com/company/101563689/) and has received funding by the European Union Horizon Europe Research and Innovation Program project SMILE (grant number 101080923) and Marie Skłodowska-Curie Actions…

An Evolutionary-scale Model (ESM) for protein function prediction from amino acid sequences using the Gene Ontology (GO). Based on the ESM2 Transformer architecture, pre-trained on UniRef50, and fine-tuned on the AmiGO dataset, this model predicts the GO subgraph for a particular protein sequence -…

CineMA is a foundation model for Cine cardiac magnetic resonance (CMR) imaging based on Masked-Autoencoder. CineMA has been pre-trained on UK Biobank data and fine-tuned on multiple clinically relevant tasks such as ventricle and myocaridum segmentation, ejection fraction (EF) regression,…

FloraSense is a fine-tuned Vision Transformer (ViT) model designed for accurate classification of plant species and flora-related imagery. It builds on top of the powerful google/vit-base-patch16-224 base model and is fine-tuned on the PlanterGARDENEDITION dataset curated by Sisigoks, which…

FineMedLM-o1

!467.png

Using llama.cpp release b5466 for quantization.

Using llama.cpp release b5466 for quantization.

Dans-PersonalityEngine-V1.3.0-24b Dans-PersonalityEngine-V1.3.0-24b ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠀⠄⠀⡂⠀⠁⡄⢀⠁⢀⣈⡄⠌⠐⠠⠤⠄⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⡄⠆⠀⢠⠀⠛⣸⣄⣶⣾⡷⡾⠘⠃⢀⠀⣴⠀⡄⠰⢆⣠⠘⠰⠀⡀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠃⠀⡋⢀⣤⡿⠟⠋⠁⠀⡠⠤⢇⠋⠀⠈⠃⢀⠀⠈⡡⠤⠀⠀⠁⢄⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠁⡂⠀⠀⣀⣔⣧⠟⠋⠀⢀⡄⠀⠪⣀⡂⢁⠛⢆⠀⠀⠀⢎⢀⠄⢡⠢⠛⠠⡀⠀⠄⠀⠀ ⠀⠀⡀⠡⢑⠌⠈⣧⣮⢾⢏⠁⠀⠀⡀⠠⠦⠈⠀⠞⠑⠁⠀⠀⢧⡄⠈⡜⠷⠒⢸⡇⠐⠇⠿⠈⣖⠂⠀…

Dans-PersonalityEngine-V1.3.0-12b Dans-PersonalityEngine-V1.3.0-12b ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠀⠄⠀⡂⠀⠁⡄⢀⠁⢀⣈⡄⠌⠐⠠⠤⠄⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⡄⠆⠀⢠⠀⠛⣸⣄⣶⣾⡷⡾⠘⠃⢀⠀⣴⠀⡄⠰⢆⣠⠘⠰⠀⡀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠃⠀⡋⢀⣤⡿⠟⠋⠁⠀⡠⠤⢇⠋⠀⠈⠃⢀⠀⠈⡡⠤⠀⠀⠁⢄⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠁⡂⠀⠀⣀⣔⣧⠟⠋⠀⢀⡄⠀⠪⣀⡂⢁⠛⢆⠀⠀⠀⢎⢀⠄⢡⠢⠛⠠⡀⠀⠄⠀⠀ ⠀⠀⡀⠡⢑⠌⠈⣧⣮⢾⢏⠁⠀⠀⡀⠠⠦⠈⠀⠞⠑⠁⠀⠀⢧⡄⠈⡜⠷⠒⢸⡇⠐⠇⠿⠈⣖⠂⠀…

Dans-PersonalityEngine-V1.1.0-12b This model series is intended to be multifarious in its capabilities and should be quite capable at both co-writing and roleplay as well as find itself quite at home performing sentiment analysis or summarization as part of a pipeline.

Dans-PersonalityEngine-V1.2.0-24b ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠀⠄⠀⡂⠀⠁⡄⢀⠁⢀⣈⡄⠌⠐⠠⠤⠄⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⡄⠆⠀⢠⠀⠛⣸⣄⣶⣾⡷⡾⠘⠃⢀⠀⣴⠀⡄⠰⢆⣠⠘⠰⠀⡀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠃⠀⡋⢀⣤⡿⠟⠋⠁⠀⡠⠤⢇⠋⠀⠈⠃⢀⠀⠈⡡⠤⠀⠀⠁⢄⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠁⡂⠀⠀⣀⣔⣧⠟⠋⠀⢀⡄⠀⠪⣀⡂⢁⠛⢆⠀⠀⠀⢎⢀⠄⢡⠢⠛⠠⡀⠀⠄⠀⠀ ⠀⠀⡀⠡⢑⠌⠈⣧⣮⢾⢏⠁⠀⠀⡀⠠⠦⠈⠀⠞⠑⠁⠀⠀⢧⡄⠈⡜⠷⠒⢸⡇⠐⠇⠿⠈⣖⠂⠀ ⠀⢌⠀⠤⠀⢠⣞⣾⡗⠁⠀⠈⠁⢨⡼⠀⠀⠀⢀⠀⣀⡤⣄⠄⠈⢻⡇⠀⠐⣠⠜⠑⠁⠀⣀⡔⡿⠨⡄…

# fernandoruiz/medgemma-4b-it-Q4_0-GGUF This model was converted to GGUF format from google/medgemma-4b-it using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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

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

This repository contains a fine-tuned Vision Transformer (ViT) model trained on a large collection of MRI scans for brain tumor classification. The model classifies MRI images into one of three categories:

# Mol-Llama-3.1-8B-Instruct [Project Page] [Paper] [GitHub]

This model has been pushed to the Hub using the PytorchModelHubMixin integration: - Hugging Face Space (available now): https://huggingface.co/spaces/Tournesol-Saturday/railNet-tooth-segmentation-in-CBCT-image - Code: https://github.com/Tournesol-Saturday/RAIL - Paper: RAIL: Region-Aware…

Pytorch Implementation of the paper: "MCP-MedSAM: A Powerful Lightweight Medical Segment Anything Model Trained with a Single GPU in Just One Day"

This project introduces a text-to-image diffusion model fine-tuned using LoRA (Low-Rank Adaptation) on top of CompVis/stable-diffusion-v1-4 for the task of medical image generation. The model generates X-ray, CT, or MRI scans based on natural language descriptions of patient symptoms, offering a…

🚀 Meerkat-8B is a new instruction-tuned medical AI system of the Meerkat model family. The model was based on the Meta's Llama-3-8B-Instruct model and fine-tuned using our new synthetic dataset consisting of high-quality chain-of-thought reasoning paths sourced from 18 medical textbooks, along…

🚀 Meerkat-7B-v1.0 is an instruction-tuned medical AI system that surpasses the passing threshold of 60% for the United States Medical Licensing Examination (USMLE) for the first time among all 7B-parameter models. The model was trained using our new synthetic dataset consisting of high-quality…

This is https://huggingface.co/kingabzpro/Qwen-3-32B-Medical-Reasoning applied to https://huggingface.co/Qwen/Qwen3-32B Original model card created by @kingabzpro

GP-MoLFormer is a class of models pretrained on SMILES string representations of 0.65-1.1B molecules from ZINC and PubChem. This repository is for the model pretrained on all the unique molecules from both datasets.

Model Repo: xformai/qwen-0.6b-mentalhealth-support Base Model: Qwen/Qwen-0.5B Task: Empathetic Conversational AI for mental health & emotional support Fine-Tuned By: XformAI

### Note: This model is copied version of DNABERT-2 which removes the FlashAttention integration with Trition. This allows the model to be installed off HuggingFace without having to uninstall Triton. Running the below example code yields identical output compared to the original verison.

## Model Description EYE-Llama_gqa is a large language model specifically designed for ophthalmic question-answering (QA). It is built upon the Llama 2 architecture and fine-tuned on a the EYE-lit and EYE-QA+ dataset.

## Overview This project focuses on curating and modeling bioactivity data of small molecules targeting immune receptors. Using datasets from ImmtorLig_DB, we applied machine learning techniques to predict interactions between small molecules and immune receptors or cytokines, aiding drug discovery…

本模型基于 trueto/medbert-base-chinese 预训练模型，结合 BiLSTM 和 CRF 构建而成，用于中文医疗命名实体识别（NER）任务。

Model Size: 7B

Model Size: 8B (English)

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 constructed. We then utilized a large-scale corpus of EHRs from over 3 million patient records to fine tune the base language model.

This is the full precision (f16) GGUF version of a model trained for medical chatbot and dental implant assistant tasks. It combines general doctor–patient dialogue understanding with domain-specific Q&A derived from Straumann® dental implant system manuals.

This project fine-tunes the meta-llama/Llama-4-Scout-17B-16E-Instruct model using a medical reasoning dataset (FreedomIntelligence/medical-o1-reasoning-SFT) with 4-bit quantization for memory-efficient training.