A fast, tiny function-calling model fine-tuned from LiquidAI/LFM2.5-1.2B-Instruct. Built on LFM2.5’s hybrid recurrent-attention architecture for significantly faster inference than transformer-only models of similar or even smaller size — fast enough to run on the CPU of a mobile phone. No thinking trace or chain-of-thought required. As an instruct-tuned model, it produces accurate tool calls directly without verbose reasoning overhead, keeping latency low and token usage minimal. — Blazing fast inference — hybrid recurrent-attention architecture runs faster than similarly-sized (and even smaller) pure transformer models on both GPU and CPU — No thinking trace needed — direct tool calls without chain-of-thought overhead, unlike reasoning-based models — Runs anywhere — Q4KM quantization fits in ~700 MB, fast enough for Android phones, Raspberry Pi, edge servers — Strong irrelevance detection (80.42%) — reliably refuses to call tools when no tool matches
Модальности:
Генерация текста
Области применения:
Диалог / чат Вызов функций (Tool use)
Задача: Генерация текста
Автор: nexus-syntegra
Теги: gguf, function-calling, tool-use, lfm2, lfm2.5, liquid-ai, unsloth, chatml
Лайков: 4 | Загрузок: 203
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
