This is a merge of pre‑trained language models created with mergekit. Purpose: compact model (≈1.5B) tuned via model-merging for concise clinical recognition and short, structured rationales. Disclaimer: Educational use only — not a substitute for professional medical judgment or emergency care. Recognition / MCQ: greedy (dosample=False). Free text: light sampling (temperature≈0.2–0.4, topp≈0.9) if needed. * Keep generations short (50–150 tokens) to reduce drift. The merged model inherits constraints from all upstream licenses. Before distribution or commercial use, review the licenses of: yasserrmd/MedScholar-1.5B nvidia/OpenReasoning-Nemotron-1.5B * Your usage must comply with the most restrictive terms among the sources. If you build on this work, please cite the original model authors and the mergekit project: mergekit: cg123/mergekit (GitHub) Upstream models: yasserrmd/MedScholar-1.5B, nvidia/OpenReasoning-Nemotron-1.5B
Модальности:
Генерация текста
Задача: Генерация текста
Автор: yasserrmd
Теги: qwen2, mergekit, merge, text-generation-inference, endpoints_compatible
Лайков: 3 | Загрузок: 29
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
