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!Protein-ligand interaction header

GGUF quantizations of HuggingFaceBio/Carbon-3B — a generative DNA foundation model — for efficient inference with llama.cpp.

A HuggingFace-compatible repackaging of PlasmidGPT (Shao, 2024) — a GPT-2-style decoder pretrained on 153k engineered plasmid sequences from Addgene. Loadable with standard AutoModelForCausalLM and AutoTokenizer. Used as the base for PlasmidGPT-SFT and PlasmidGPT-GRPO.

1. 概述 2. 数据处理流程 3. 模型架构 4. 损失函数设计 5. 训练流程 6. 配置参数

Hulu-Med: A Transparent Generalist Model towards Holistic Medical Vision-Language Understanding

Hulu-Med: A Transparent Generalist Model towards Holistic Medical Vision-Language Understanding

Hulu-Med: A Transparent Generalist Model towards Holistic Medical Vision-Language Understanding

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

A 53K-parameter weight-shared transformer that learns SMILES grammar by applying one small block 8 times. It reaches 95.3% validity on ZINC-250K — outperforming an unshared GPT 10× larger (87.6%).

This set of model weights was released with the GitHub-compatible esm package format. The models here are kept for backwards compatibility, but we recommend you use the HuggingFace-compatible model weights at biohub/ESMC-6B (or biohub/ESMC-300M / biohub/ESMC-600M) instead.

# Geneformer Geneformer is a foundational transformer model pretrained on a large-scale corpus of human single cell transcriptomes to enable context-aware predictions in settings with limited data in network biology.

Sparse Autoencoder (SAE) trained on residue-level embeddings from ESM-2 (650M, layer 33) for interpretability research on protein language models.

DISCO (DIffusion for Sequence-structure CO-design) is a multimodal generative model that simultaneously co-designs protein sequences and 3D structures, conditioned on and co-folded with arbitrary biomolecules — including small-molecule ligands, DNA, and RNA.

A PyTorch MLP that predicts odor descriptors from a molecule's SMILES string using Morgan (ECFP4) fingerprints. Given any molecule, the model outputs a smell profile across 50 odor categories.

COMO (Closed-loop Optical Molecule recOgnition) is a deep learning framework for Optical Chemical Structure Recognition (OCSR). It recognizes chemical structure diagrams from images and predicts SMILES strings with atom-level 2D coordinates and bond matrices.

Modelo fine-tunado com LoRA (MLX / Apple Silicon) para assistência clínica em saúde da mulher.

vitreg4so150mp14ls_dino-v2-bio is a Bio-DINO image encoder for natural photographs of living organisms. It uses a SoViT-150M/14 Vision Transformer with 4 register tokens and 133.6M backbone parameters, trained with a DINOv2-style self-supervised objective on approximately 31 million curated images…

vitreg1s14lsdino-v2-dist-bio is a compact Bio-DINO image encoder distilled from the larger Bio-DINO SoViT-150M/14 model. It keeps the same natural-photography biodiversity scope as the teacher model, but uses a much smaller ViT-S/14-style student with 21.7M backbone parameters and 384-dimensional…

This is the pretrained VetBERT model from the github repo: https://github.com/havocy28/VetBERT

This repository contains the full Bio-DINO DINOv2 training weights for a SoViT-150M/14 Vision Transformer trained on natural photographs of living organisms. It is the companion release to the Birder backbone checkpoints at .

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This model is a domain-specific large language model fine-tuned from QwQ-32B, specialized in the field of perovskite solar cells, particularly focusing on precursor additives. It is designed to assist researchers, engineers, and material scientists by providing knowledge, insights, and suggestions…

Target-Conditioned Molecular Ideation Model for Drug Discovery Research

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.

Data repository for SynPlanner — an open-source tool for retrosynthetic planning.

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.

Base model: google/gemma-4-26b-it Architecture: MoE — 26B total / ≈4B active parameters (1 shared expert + 8 routed from a pool of 128 per MoE layer, 30 MoE layers) Method: Activation-directed expert surgery — 128 → 64 experts per layer (50% reduction) Quantization: Q4KM (≈9.7 GB on disk) Tags:…

Base model: google/gemma-4-26b-it Architecture: MoE — 26B total / ≈4B active parameters (1 shared expert + 8 routed from a pool of 128 per MoE layer, 30 MoE layers) Method: Activation-directed expert surgery — 128 → 64 experts per layer (50% reduction) Quantization: Q4KM (≈9.7 GB on disk) Tags:…

Base model: google/gemma-4-26b-it Architecture: MoE — 26B total / ≈4B active parameters (1 shared expert + 8 routed from a pool of 128 per MoE layer, 30 MoE layers) Method: Activation-directed expert surgery — 128 → 64 experts per layer (50% reduction) Quantization: Q4KM (≈9.7 GB on disk) Tags:…

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…

This repository hosts JAX exports of MatterSim v1.0.0 for use with kUPS, a JAX-native molecular-simulation toolkit. Each artefact is a self-contained .zip containing the serialized JAX computation graph, the original model parameters, and the minimal metadata needed to run inference.

Meow-Omni 1 is the world’s first Multimodal Large Language Model (MLLM) specifically engineered for Computational Ethology. It natively co-embeds four distinct modalities—Text, Video, Audio, and Biological Time-Series—to decode the latent intentions of non-verbal species.

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.

This U‑Net model predicts tilt, z scanner drift, and other large‑scale imaging artifacts present in Atomic Force Microscopy (AFM) height maps. It outputs a background image, the same size and scale as the raw AFM image, which can be subtracted (via the accompanying afMLevel code) to produce a…

This U‑Net model masks features in Atomic Force Microscopy (AFM) height maps. It outputs a probability mask image, the same size as the raw AFM image; the accompanying python package, afMLevel code then applies a threshold (typically 0.5) to produce a binary mask.

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 +…

> [!WARNING] > This is a baseline model trained on publicly available data. While we've done our best to curate the data, the model performance is quite poor. Proceed with caution.