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LoRA adapters fine-tuning Qwen2.5-7B-Instruct to emit a full CIF crystal structure from a prompt of reduced composition + target space-group number. Part of a controlled composition-sweep study (MP-20 : MPTS-52 training ratio at fixed volume/steps).

A DNA annotation model from the Carbon family.

Paper: RAGenome: Scaling Retrieval-Based Genomic Language Models to Long Contexts

MedDecider-27B is the most robust mid-size member of the MedDecider family. Given a clinical state (a note, a trial record or any JSON) and a question with 2 to 10 answer options, it returns a calibrated probability for every option in a single scoring pass, without generating text.

Vigyan-7B-BioMed-Chem is a domain-specialized LoRA adapter trained on OLMo-2-1124-7B dedicated to organic chemical synthesis, pharmacology, and molecular biology.

Typed-decision adapter for Qwen3.5-4B, tuned on clinical question answering, financial news sentiment and structured record-level workflow decisions.

Minimal HuggingFace port of Helix-mRNA -- a hybrid Mamba2 / attention language model for full-length mRNA, trained with next-token prediction on single-nucleotide tokens with a codon-start marker.

Longevity-LLM (L-LLM) is a family of compact, domain-adapted language models for interpreting heterogeneous aging biology data. This checkpoint, L-Qwen3-1.7B, was produced by full-parameter supervised fine-tuning of Qwen/Qwen3-1.7B on aging-related multi-omics and clinical data.

A domain-adapted Qwen3.5-9B for aging and longevity biology. L-LLM is the result of continued pretraining + supervised fine-tuning + a reasoning-augmented continuation pass on a multi-domain corpus spanning clinical aging, epigenomics, transcriptomics, proteomics, and genetics.

BioGravity-Inst is a biomedical instruction model from AIGEN Sciences, Inc., developed from the Gravity 30B-A5B family. It is intended for biomedical research in the Biomni A1 environment, including question answering, evidence gathering, computation, and tool-assisted analysis.

A System-1 decision model for biomedicine, pharma and clinical trials. It reads a source, a question and a set of possible answers, and returns a calibrated probability for each answer in one forward pass, with no generated text.

A taxonomy-informed sparse DNA foundation model for microbial genomics.

ESMC is a state-of-the-art protein language model that has learned the rules of protein biology from training on billions of protein sequences. ESMC provides representations of proteins enabling novel AI applications from therapeutic protein engineering to unlocking basic insights into protein…

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.

Byte-level BPE tokenizer for Chargaff, our DNA prediction model. No training, no merges: 1 token per UTF-8 byte.

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

💻 GitHub | 📘 E-SMILES 2.0 Spec | 📄 Report | 🚀 Demo

iona-denoise-50m scores every peak of a tandem mass spectrum (MS/MS) as signal or noise. It is the Iona 50m encoder with a per-peak classification head, fine-tuned for noise-peak detection.

iona-denoise-400m scores every peak of a tandem mass spectrum (MS/MS) as signal or noise. It is the Iona 400m encoder with a per-peak classification head, fine-tuned for noise-peak detection.

iona-denoise-200m scores every peak of a tandem mass spectrum (MS/MS) as signal or noise. It is the Iona 200m encoder with a per-peak classification head, fine-tuned for noise-peak detection.

iona-denoise-100m scores every peak of a tandem mass spectrum (MS/MS) as signal or noise. It is the Iona 100m encoder with a per-peak classification head, fine-tuned for noise-peak detection.

Three genomic foundation models, packaged together for local inference on Apple silicon.

Iona is a transformer encoder foundation model for tandem mass spectra (MS/MS). It treats each centroided peak as a token and learns how peaks relate to each other through a per-head attention bias over the signed m/z difference (Δm/z) between every pair of peaks.

Iona is a transformer encoder foundation model for tandem mass spectra (MS/MS). It treats each centroided peak as a token and learns how peaks relate to each other through a per-head attention bias over the signed m/z difference (Δm/z) between every pair of peaks.

Iona is a transformer encoder foundation model for tandem mass spectra (MS/MS). It treats each centroided peak as a token and learns how peaks relate to each other through a per-head attention bias over the signed m/z difference (Δm/z) between every pair of peaks.

Iona is a transformer encoder foundation model for tandem mass spectra (MS/MS). It treats each centroided peak as a token and learns how peaks relate to each other through a per-head attention bias over the signed m/z difference (Δm/z) between every pair of peaks.

🩺 HuatuoGPT-3-27B 🏠 GitHub | 📄 Paper

🩺 HuatuoGPT-3-9B 🏠 GitHub | 📄 Paper

HuatuoGPT-3-Grader-8B GitHub | Paper

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Ultra-fast extraction of predefined clinical variables from free-text clinical notes.

This model card provides an overview of the intended use of the ESMC SAE models and examples of how to access them, but it does not have a specific model or model weights. To access each SAE model collection, use the links below:

The ESMC scaling-study checkpoints are being released to support reproducibility of the findings in our paper, please refer to the paper and github for details. Please use the ESMC model for research work. ESMC is a state-of-the-art protein language model trained on billions of protein sequences…

The ESMC scaling-study checkpoints are being released to support reproducibility of the findings in our paper, please refer to the paper and github for details. Please use the ESMC model for research work. ESMC is a state-of-the-art protein language model trained on billions of protein sequences…

The ESMC scaling-study checkpoints are being released to support reproducibility of the findings in our paper, please refer to the paper and github for details. Please use the ESMC model for research work. ESMC is a state-of-the-art protein language model trained on billions of protein sequences…

ESMC is a state-of-the-art protein language model that has learned the rules of protein biology from training on billions of protein sequences. ESMC provides representations of proteins enabling novel AI applications from therapeutic protein engineering to unlocking basic insights into protein…

ESMC is a state-of-the-art protein language model that has learned the rules of protein biology from training on billions of protein sequences. ESMC provides representations of proteins enabling novel AI applications from therapeutic protein engineering to unlocking basic insights into protein…

ESMC is a state-of-the-art protein language model that has learned the rules of protein biology from training on billions of protein sequences. ESMC provides representations of proteins enabling novel AI applications from therapeutic protein engineering to unlocking basic insights into protein…

ESMFold2 is a state-of-the-art model for protein structure prediction and design that defines a new frontier for speed and accuracy. The model predicts high-resolution, all-atom 3D protein structures directly from amino acid sequences, with optional multiple sequence alignment (MSA) input for…

ESMFold2 is a state-of-the-art model for protein structure prediction and design that defines a new frontier for speed and accuracy. The model predicts high-resolution, all-atom 3D protein structures directly from amino acid sequences, with optional multiple sequence alignment (MSA) input for…

Evo2-7B (Transformers port)

Evo2-1B-Base (Transformers port)

# 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.