MiMo-V2.6-Distill-Qwen-9B-GGUF

MiMo-V2.6-Distill-Qwen-9B-GGUF

MiMo-V2.6-Distill-Qwen-9B is a 9-billion-parameter agentic model from Xiaomi MiMo, built via supervised fine-tuning of Qwen3.5-9B on MiMo-generated data spanning coding, general-purpose agent tasks, visual coding, and cybersecurity, released as an SFT checkpoint intended as a starting point for open research in agentic reinforcement learning. It was trained on a weighted mixture of 77.4B total tokens (27.2B loss-bearing) split roughly evenly across code (29.9%), general (28.5%), visual (27.4%), and cyber (14.2%) domains, and shows dramatic gains over its Qwen3.5-9B base across nearly every reported benchmark — jumping from 5.0 to 30.3 on AutomationBench v1.0.6, 5.7 to 31.3 on internal cyber evaluation, 27.0 to 37.1 on Terminal-Bench 2.1, and 19.5 to 51.6 on internal coding evaluation, alongside solid improvements on SWE-bench Verified (61.1) and SWE-bench Pro (44.6). It ships with its tokenizer and MiMo v2.6 chat template, is served via SGLang with a dedicated mimo reasoning parser and explicit thinking-mode control through an OpenAI-compatible API, and is fine-tuned from the Apache-2.0-licensed Qwen3.5-9B base.

Model Files

llama.cpp

LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp