Flagship open-source genomic LLM. State-of-the-art on GeneTuring (78.9%), ClinVar VUS classification (82.4%), and Gene-Disease Association (84.1%). Trained on ClinVar, NCBI, OMIM, gnomAD, and 8M+ PubMed genomics papers. Supports VCF input and clinical report output.

State-of-the-art biomedical LLM achieving 91.2% on MedQA and 89.4% on USMLE Step 1-3. Trained on 42M+ PubMed abstracts with DPO alignment using 120K physician-curated preference pairs. Surpasses GPT-4 on 7 of 9 medical benchmarks.

Compact, deployable biomedical LLM for on-premise and edge clinical systems. Achieves 82.4% on MedQA. Same training corpus as the 70B variant — distilled for speed. GGUF and AWQ quantized variants available. Runs on a single NVIDIA RTX 4090.

Clinical decision support model engineered for differential diagnosis generation and evidence-based treatment recommendations. Fine-tuned on de-identified EHR data, clinical pathways, SOAP notes, and clinical guidelines. 86.2% on clinical diagnosis accuracy benchmark.

Specialized for genomic sequence analysis and variant interpretation. Trained on NCBI, Ensembl, ClinVar, and OMIM datasets. 87% accuracy on clinical variant classification. Supports VCF input and automated ACMG variant classification output in structured JSON.

ADMET property prediction, SMILES-to-property conversion, molecular optimization, and compound-target interaction modeling. Trained on 10M+ compounds from ChEMBL and PubChem. Top performance on 14 ADMET benchmarks from TDC.

Multimodal LLM fusing pathology slide images and clinical text. Supports histopathology analysis, tumor grading, and automated radiology report generation with visual grounding. Based on LLaVA-Med architecture.