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SmileBERTa is a RoBERTa-based language model for chemistry, built on the ChemBERTa architecture and fine-tuned to predict small-molecule fragment SMILES from full small-molecule drug SMILES.

Minimal HuggingFace repackage of the large variant of ModernGENA -- a ModernBERT DNA encoder pretrained on vertebrate genomes with masked language modeling.

!Aerova

CENO-1B-1m is a checkpoint of the CENO base DNA foundation model (1M context (stage 4)). It is a plain causal language model over genomic sequence on a Nemotron-H Mamba/Attention/MoE hybrid backbone, with no MSA inputs.

!Aerova

OmniTCR is a component-aware autoregressive foundation model for learning relationships among peptide epitopes, major histocompatibility complex (MHC) molecules, T-cell receptor alpha chains (TRA) and T-cell receptor beta chains (TRB).

PertMind is a biological language model built around a central discovery: public cellular perturbation atlases can be reorganized into reinforcement-learning environments, where measured gene responses act as computable reward signals for biological reasoning.

A from-scratch, decoder-only protein language model for the phosphotransferase superfamily (EC 2.7.-: protein kinases plus sugar/lipid/nucleotide kinases), trained entirely locally on Apple Silicon via MLX — no cloud compute, no fine-tuning of an existing model.

A 350M encoder that finds nine types of personally identifiable information across 17 languages and returns exact character spans for review and redaction.

BondShift: Organic Mechanism Reasoning

Parrotlet-a 2.5 Pro is a purpose-built automatic speech recognition (ASR) model for medical speech in Indian healthcare settings. It transcribes Indian English, Hindi, Marathi, Kannada and Telugu, including the heavily code-mixed speech typical of real consultations (English drug names and clinical…

A clinical reasoning assistant for early-stage Alzheimer's assessment. It joins a 3D MRI + biomarker classifier (Vbai-2.6AD) to a reasoning LLM (Gemma 4 12B) inside a single forward pass — the diagnosis is passed as vectors, not text.

Contrastively fine-tuned ESM-C 300M producing fixed-length protein embeddings where biological similarity maps to embedding proximity. Intended for retrieval, clustering, and nearest-neighbour transfer.

Technical Report 🧬

This 1,120,772,224-parameter nucleotide-level causal language model is a member of the eight-model MarinDNA v0.5 parameter-scaling ladder developed with Marin. This repository contains only the final step-215573 checkpoint from run dna-bolinas-scaling-v0.5-h1920-p1B-0dc6f4, with its tokenizer…

MarinDNA m5.1 is a 1.12B-parameter, nucleotide-level causal language model developed with Marin. This is the final m5.1 base-model checkpoint at step 59,158 from run dna-bolinas-mix-v0.9-p1B-i24-exp135-zoonomia-m5.1-bef41e, released with the A 1B standard Transformer rivals Evo 2 40B on variant…

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

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.

Longevity-LLM (L-LLM) is a family of compact, domain-adapted language models for interpreting heterogeneous aging biology data. This checkpoint, L-Qwen3-0.6B, is the smallest family member and was produced by full-parameter supervised fine-tuning of Qwen/Qwen3-0.6B on aging-related multi-omics and…

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HealthGPT-LoRA is a biomedical question-answering model built by fine-tuning Meta Llama 3.2 3B Instruct using QLoRA (PEFT) on the PubMedQA dataset.

ProtSent-V2 35M plus one more contrastive pass on a fresh draw of the corpus, with a DMS/ProteinGym CoSENT target and a Global Orthogonal Regularization term added.

A DINOv2 ViT-S/14-reg fine-tuned so that an image of a molecular structure diagram embeds where its molecule embeds in the frozen MIST-28M embedding space. Objective: smooth-L1 regression onto the frozen target, no negatives (the JEPA move).

A DINOv2 ViT-S/14-reg fine-tuned so that an image of a molecular structure diagram embeds where its molecule embeds in the frozen MIST-28M embedding space. Objective: SigLIP sigmoid pairwise loss.

A multilingual PII extractor for teams that need structured JSON from clinical and administrative text.

Contrastively fine-tuned ESM-2 150M producing fixed-length protein embeddings where biological similarity maps to embedding proximity. Intended for retrieval, clustering, and nearest-neighbour transfer.

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

464 fine-tuned DNABERT models for regulatory variant effect prediction

Trinity-Mini-AI-Scientist

In search engines, rerankers are crucial for improving the accuracy of your retrieval system.

In search engines, rerankers are crucial for improving the accuracy of your retrieval system.

In retrieval systems, embedding models determine the quality of your search.

MoLFormer is a class of models pretrained on SMILES string representations of up to 1.1B molecules from ZINC and PubChem. This repository is for the model pretrained on 10% of both datasets.

The commands below use the glyph package. Install it from the code repository:

This is a QLoRA adapter for query-focused structured extraction from one PubMed title and abstract. It was trained as part of BioEvidence Copilot and targets the repository's versioned ModelEvidenceExtraction JSON Schema.

Healthcare Brain Procedure Surgery NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of surgical procedures, diagnostic tests, interventions, and procedural details from unstructured clinical text.

Healthcare Brain Vitals NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of vital signs, body measurements, and physiological parameters from clinical text.

Healthcare Brain Laboratory NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of laboratory test results, values, units, reference ranges, and abnormality flags from unstructured clinical text.

Healthcare Brain Diagnosis ICD NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of diagnoses, conditions, and support for ICD-10/SNOMED code mapping from unstructured clinical text.

Healthcare Brain Medication NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of medication names, dosages, routes, frequencies, and administration details from unstructured clinical text.

Healthcare Brain Clinical Findings NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of clinical findings, diseases, conditions, anatomical locations, and clinical modifiers from unstructured clinical text.