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Model Details
Model Provider
Blackfrost-AI
Model Tree
XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B
Input Modalities
Output Modalities
Supported Functionality
The parent revision was pinned to
2367e865d009c13ac81713a2878291d33ab28177. No additional SFT, RL, merge, or
quantization was performed by Blackfrost-AI. The tokenizer, processor files,
configuration, and MiMo chat template are unchanged from that pinned parent.
No additional system prompt is embedded in this release.
Hugging Face's lineage UI classifies a full-weight derivative like this under
its finetune relation category. That platform label describes the repository
relationship here; the actual transformation was DWM, not gradient training.
1.0, one pass2 through 31Verification found 60 changed target matrices and 700 bit-identical non-target tensors. All 760 serialized tensors remain BF16. The 333 vision tensors were excluded from the edit and remain identical to the parent.
The packaged checkpoint passed shard/index integrity checks, structural tensor
verification, a coherent text-generation smoke test, and native structured tool
calling tests for non-streaming, streaming, thinking-enabled, and tool-result
round-trip requests. Tool tests used the MiMo reasoning parser together with the
qwen3_coder tool-call parser.
The configuration retains the parent's 262,144-token maximum context setting; usable context depends on the inference engine and available memory. This release has not yet been independently re-benchmarked for refusal behavior, coding, cybersecurity, long-context quality, or multimodal quality. Xiaomi's published parent-checkpoint results should not be treated as measurements of this derivative.
The configuration includes mtp_num_hidden_layers: 1, but the inherited
checkpoint index contains no separately serialized MTP/draft tensors. Do not
assume speculative-decoding support without validating it in your runtime.
Use a recent SGLang build with Qwen3.5 support. Both parsers below are required for the validated OpenAI-compatible reasoning and structured tool-call behavior.
bash
sglang serve \ --model-path Blackfrost-AI/MiMo-V2.6-Distill-Qwen-9B-Derisked-BF16 \ --served-model-name MiMo-V2.6-Distill-Qwen-9B-Derisked-BF16 \ --reasoning-parser mimo \ --tool-call-parser qwen3_coder \ --dtype bfloat16 \ --host 0.0.0.0 \ --port 30000Query it with thinking explicitly enabled or disabled:
python
from openai import OpenAI client = OpenAI( base_url="http://localhost:30000/v1", api_key="EMPTY",) response = client.chat.completions.create( model="MiMo-V2.6-Distill-Qwen-9B-Derisked-BF16", messages=[{"role": "user", "content": "What is 15% of 240?"}], max_tokens=2048, extra_body={"chat_template_kwargs": {"enable_thinking": True}},) message = response.choices[0].messageprint("Thinking:", getattr(message, "reasoning_content", "") or "")print("Answer:", message.content or "")Multimodal processor and vision files are included. The DWM edit did not touch the vision tower, but multimodal behavior has not been re-benchmarked on this derivative.
This checkpoint is intended for research, development, and authorized security work. Reduced false-refusal behavior is an experimental objective, not a safety or capability guarantee. Operators remain responsible for access control, validation, and lawful use in their deployment environment.
The Xiaomi MiMo parent repository declares the checkpoint under the MIT license; this repository preserves that license metadata and direct lineage. See the pinned parent repository for its model details, training-data description, reported evaluations, and attribution.
bibtex
@misc{mimo2026v26, title={MiMo-V2.6: Scaling Reinforcement Learning Towards Self-Improvement}, author={{Xiaomi MiMo Team}}, year={2026}, howpublished={\url{https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL}},}sglang serve \ --model-path Blackfrost-AI/MiMo-V2.6-Distill-Qwen-9B-Derisked-BF16 \ --served-model-name MiMo-V2.6-Distill-Qwen-9B-Derisked-BF16 \ --reasoning-parser mimo \ --tool-call-parser qwen3_coder \ --dtype bfloat16 \ --host 0.0.0.0 \ --port 30000from openai import OpenAI client = OpenAI( base_url="http://localhost:30000/v1", api_key="EMPTY",) response = client.chat.completions.create( model="MiMo-V2.6-Distill-Qwen-9B-Derisked-BF16", messages=[{"role": "user", "content": "What is 15% of 240?"}], max_tokens=2048, extra_body={"chat_template_kwargs": {"enable_thinking": True}},) message = response.choices[0].messageprint("Thinking:", getattr(message, "reasoning_content", "") or "")print("Answer:", message.content or "")@misc{mimo2026v26, title={MiMo-V2.6: Scaling Reinforcement Learning Towards Self-Improvement}, author={{Xiaomi MiMo Team}}, year={2026}, howpublished={\url{https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL}},}