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. The family was developed jointly by Insilico Medicine and Liquid AI and accompanies the study «An Open Benchmark and Language Models for AI in Aging Biology» (Zhavoronkov et al., 2026). — Base model: Qwen/Qwen3-1.7B (Apache-2.0) — Model type: Decoder-only transformer, multitask instruction-tuned — Language: English — License: CC-BY-ND-4.0 — Context length: 32,768 tokens — Precision: BF16 The model was trained on the shared L-LLM corpus spanning aging biology. See LongevityBench for more details L-Qwen3-1.7B was trained with full-parameter supervised fine-tuning using the Hugging Face TRL library. Prompts were formatted in ChatML with a dynamic-thinking template (user turns suffixed with /think or /nothink` to select response mode at inference). Intended for research on aging biology and omics interpretation. Outputs are model predictions, not clinical advice, and should be validated experimentally.…
Модальности:
Генерация текста
Области применения:
Диалог / чат Биология
Задача: Генерация текста
Автор: insilicomedicine
Теги: qwen3, english, aging, longevity, biomedical, biology, conversational, text-generation-inference
Лайков: 3 | Загрузок: 521
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
