Based on LFM2-350M, LFM2-350M-Math is a tiny reasoning model designed for tackling tricky math problems. You can find more information about other task-specific models in this blog post. Generation parameters: We strongly recommend using greedy decoding with a temperature=0.6, topp=0.95, minp=0.1, repetitionpenalty=1.05`. Chat template: LFM2 uses a ChatML-like chat template as follows: You can automatically apply it using the dedicated .applychattemplate() function from Hugging Face transformers. > [!WARNING] > ⚠️ The model is intended for single-turn conversations. Reasoning enables models to better structure their thought process, explore multiple solution strategies, and self-verify their final responses. Augmenting tiny models with extensive test-time compute in this way allows them to even solve challenging competition-level math problems. Our benchmark evaluations demonstrate that LFM2-350M-Math is highly capable for its size. As we are excited about edge deployment, our goal is to limit memory consumption and latency. Our post-training recipe leverages reinforcement learning to explicitly bring down response verbosity where it is not desirable. To this end, we combine…
Модальности:
Генерация текста
Области применения:
Диалог / чат Математика
Задача: Генерация текста
Автор: onnx-community
Теги: transformers.js, onnx, lfm2, liquid, edge, conversational, en
Лайков: 4 | Загрузок: 17
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
