mimo-v2.6-distill-qwen-9b: Xiaomi MiMo V2.6 Distill Qwen-9B Agentic Model

• Fact Model size and base[1]AIMiMo-V2.6-Distill-Qwen-9B is a 9B agentic model developed by Xiaomi MiMo through supervised fine-tuning of Qwen3.5-9B
• Fact Training data volume[1]AIThe weighted SFT data mixture contains 77.4B total tokens, including 27.2B loss-bearing tokens
• Fact Target domains[1]AIIt covers coding, general-purpose agent tasks, visual coding, and cybersecurity
• Fact Release purpose[1]AIWe release this SFT checkpoint as a starting point for open research in agentic reinforcement learning
• Fact Usage guidance[1]AIFor text generation, use a recent SGLang build with Qwen3.5 support. The checkpoint includes its tokenizer and MiMo v2.6 chat template
• Fact XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B model card is listed among trending models on the Hugging Face Hub.[1]
• Fact That is 606 likes in 11 days, an average of 54.9 per day.[1]

• Analysis Fine‑tune the checkpoint on custom data, integrate it via SGLang for text generation, or use it as a baseline for agentic RL research.AI
• Analysis Automatically detected from public activity data. Not yet reviewed by an analyst.

• Fact Model size and base[1]AIMiMo-V2.6-Distill-Qwen-9B is a 9B agentic model developed by Xiaomi MiMo through supervised fine-tuning of Qwen3.5-9B
• Fact Training data volume[1]AIThe weighted SFT data mixture contains 77.4B total tokens, including 27.2B loss-bearing tokens
• Fact Target domains[1]AIIt covers coding, general-purpose agent tasks, visual coding, and cybersecurity
• Fact Release purpose[1]AIWe release this SFT checkpoint as a starting point for open research in agentic reinforcement learning
• Fact Usage guidance[1]AIFor text generation, use a recent SGLang build with Qwen3.5 support. The checkpoint includes its tokenizer and MiMo v2.6 chat template

Architecture

Insufficient evidenceNo architecture has been documented for this signal.

Insufficient evidenceDeveloper implications have not been assessed yet.

Insufficient evidenceNo adoption indicators recorded.

Timeline

1. 2 Oct 2026

   First seenFirst seen by the Astrava pipeline[1]

• Fact Evaluation limited to internal sets[1]AIEvaluation Results for the released SFT checkpoint, as reported in the MiMo-V2.6 technical report. † Internal evaluation sets
• Analysis Generated from activity metrics only; verify the linked sources before acting on it.

Insufficient evidenceNo competing approaches documented yet.

Insufficient evidenceNo independent projects or organisations are documented as building on this yet.