@mtasic85: You know I am going down the rabbit hole, when you see me making LFM2.5 2.6B behaving close to Qwen3.8 27B when it come…

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Summary

@mtasic85 demonstrates that with prompt programming alone, without fine-tuning, LFM2.5 2.6B can behave close to Qwen3.8 27B in tool calling and skill system applications.

You know I am going down the rabbit hole, when you see me making LFM2.5 2.6B behaving close to Qwen3.8 27B when it comes to tool calling and using skill system - all prompt programming, zero fine-tuning. https://t.co/QQATTb3gYA
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Cached at: 08/20/26, 06:58 AM

You know I am going down the rabbit hole, when you see me making LFM2.5 2.6B behaving close to Qwen3.8 27B when it comes to tool calling and using skill system - all prompt programming, zero fine-tuning. https://t.co/QQATTb3gYA

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A community fine-tune of Qwen3.8-27B (Coder390-EfficientThink) is released, post-trained with SFT + RLOO + MTP on 3.9M lines of code and distilled from frontier models, boosting LiveCodeBench to 90 while cutting reasoning tokens by ~44-53%. Apache-2.0 GGUF builds run locally on 24GB GPUs via llama.cpp or SGLang with DFlash2 speculative decoding.

LFM 2.6B is a lot of fun.

Reddit r/LocalLLaMA

The author shares hands-on experience with LFM 2.6B, a small model designed for phones, praising its speed and usefulness for quick tasks like summarization and autocomplete, though it has a 128k context limit.