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

An ontology of qualifications, distinctions, and certifications that uses the Phenotype And Trait Ontology term quality (PATO:0000001) as a root term.

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

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 -…

In silico directed evolution framework using few-shot active learning to optimize protein activities, enabling rapid protein engineering with minimal experimental data (352+ stars, 2023)

Extensible chemistry toolkit for MCP-enabled AI assistants, exposing molecule analysis, property prediction, and reaction synthesis tools through unified Python/MCP interfaces for chemistry agents and research workflows (Apache 2.0, 2025)

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 -…

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 -…

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

Multimodal LLM for scientific charts and diagrams understanding/generation

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Universal 3D molecular pretraining framework with 209M conformations, scaling to 1.1B parameters (Uni-Mol2) on 800M conformations for molecular property prediction, docking, and quantum chemistry (ICLR 2023, NeurIPS 2024)

End-to-end RNA 3D structure prediction using RNA language model pretrained on 23.7M sequences, outperforming existing methods and human expert groups on RNA-Puzzles and CASP15 (Nature Methods 2024)

Advanced paper search agent powered by large language models, autonomously invoking search tools, reading papers, and selecting references to deliver comprehensive and accurate results for complex scholarly queries (1.5K+ stars, Apache 2.0, 2024)

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.

General purpose tools for high-throughput catalysis.

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

Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation.

An open, community-driven registry of conference and event venues. EVR assigns persistent identifiers (PIDs) to make referencing venues FAIR. This is similar to how ORCID assigns PIDs to researchers and ROR assigns PIDs to research organizations. This benefits researchers assembling information about in-person conferences and events by enabling them to refer in an unambiguous way to the venue where it takes place. This repository follows the [Open Data, Open Code, Open Infrastructure (O3) principles](https://www.nature.com/articles/s41597-024-03406-w), meaning that the data and code are all in one repository that anyone can contribute to.

Whole-slide pathology foundation model trained on 1.3 billion image tiles from 171K slides using a LongNet-based architecture to encode gigapixel-scale WSIs for cancer subtyping and biomarker prediction (Microsoft Research & Providence, 601+ stars)

ValidSense is a toolbox for assessing agreement between two quantitative methods or devices measuring the same quantity using the Limits of Agreement (LoA) analysis, also known as the Bland-Altman analysis.

Automated hypothesis testing with agentic sequential falsifications

Bioinspired multi-agent intelligent graph reasoning system that autonomously traverses ontological knowledge graphs to generate, critique, and refine novel research hypotheses, demonstrated on bio-inspired materials discovery with cross-disciplinary connection mining (MIT Lamm Group, 2024)

Systematic medical RAG toolkit for question answering over PubMed, StatPearls, textbooks, and Wikipedia, supporting multiple retrievers, domain LLMs, and follow-up-query workflows for benchmarked clinical/biomedical QA (ACL Findings 2024)

🚀 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…

AI-powered tool that automatically converts academic papers (PDF) into presentation slides

Diffusion-based molecular docking achieving SOTA blind docking performance, treating ligand pose prediction as generative diffusion over SE(3), with DiffDock-L update for improved generalization (MIT CSAIL, ICLR 2023)

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.

Unified benchmarking framework for protein representation learning, providing standardized interfaces for pre-training and diverse downstream tasks including structure prediction, fitness prediction, and property prediction across multiple protein datasets and model architectures (ICLR 2024, 273+ stars, MIT License)

## 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.

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.

Efficient foundation models for PDEs with pretrained transformer-based neural operators and downstream task fine-tuning pipelines, HuggingFace integration for models and datasets (ETH Zurich CAMLab, arXiv 2024)

PyTorch implementation of neural ODEs

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LLM-based molecular optimization tool

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Vision-language pathology foundation model using contrastive learning on histopathology image-text pairs, enabling zero-shot classification, slide-level retrieval, and multimodal reasoning across diverse cancer types (Mahmood Lab, 494+ stars)

This deep learning model is designed for ECG image classification, fine-tuned using ResNet-50. It can classify ECG images into different categories to assist in heart disease detection.