It will not answer your questions, follow instructions, or hold a conversation. Used as an assistant it will produce nonsense, because that is not what it was trained to do. It is a distiller: a batch tool for reasoning-trace datasets. You give it a trace another model produced — the task, the full rollout, and the answer that model gave — and it returns a compact version in a fixed format. It does not solve the task. The answer is already in the input; the model’s job is to re-express the reasoning and pass the answer through without damaging it. Typical use: condensing a corpus of reasoning traces into training data. Two caveats on this table. The format row scores v1 against a contract that did not exist when v1 was built, so it is unfair by construction. The other rows are not: not inventing, not losing the verdict and not dropping literals are general requirements, and that is where most of the gain is. v1’s own headline result — matching the 27B teacher on verifiable verdicts, 90 % vs 90 % — measures something different and is not superseded here. v2 does not compress more than v1. On the 100-trace production run the median is 1.4× overall and 4.0× for the deliverable alone,…
Модальности:
Генерация текста
Области применения:
Логика и рассуждение Диалог / чат
Задача: Генерация текста
Автор: Davd-b01
Теги: gguf, lfm2, reasoning, distillation, thinking-cap, chain-of-thought, trace-translation, condensed-thinking
Лайков: 3 | Загрузок: 2,064
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
