Guys and Girl nerds, the tiny 500m-1.2B models trained for a single task are taking off cause they are dirt cheap to run and do that task better then frontier models. Us Local AI folks can run AND train these at home. (I will soon) Actually ☝️🤓 For a training a highly specialized 1B model, 1,000 to 5,000 high-quality, varied examples are often enough to achieve frontier-level performance on that specific task. Not 50,000 to 1 million like with big models. Checkout this blog article by @ liquidai where they talk about their tiny task models. "LFM2-Extract – A 350M and 1.2B multilingual models for data extraction from unstructured text, like turning invoice emails into JSON objects. LFM2‑350M‑ENJP‑MT –  A 350M model for bidirectional English ↔ Japanese translation. LFM2‑1.2B‑RAG – A 1.2B model optimized for long‑context question answering in RAG pipelines. LFM2‑1.2B‑Tool – A 1.2B model built for function calling and agentic tool use. LFM2‑350M‑Math –  A 350M reasoning model for solving mathematical problems. Luth-LFM2 – An additional community-driven series of French fine-tunes to enable general-purpose assistants for on-device chat." Read more here: