XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B — new model trending #30 on Hugging Face
Xiaomi MiMo released a new 9B agentic model checkpoint covering coding, general-purpose agent tasks, visual coding, and cybersecurity.
Xiaomi MiMo released a new 9B agentic model checkpoint for open research in agentic reinforcement learning, covering coding, general-purpose agent tasks, visual coding, and cybersecurity.
- Xiaomi MiMo released a new 9B agentic model checkpoint
- The model includes cybersecurity capabilities
Full article415 words · extracted from huggingface.co · click to collapse
# MiMo-V2.6-Distill-Qwen-9B
MiMo-V2.6-Distill-Qwen-9B is a 9B agentic model developed by Xiaomi MiMo through supervised fine-tuning of [Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B) on MiMo-generated data. It covers coding, general-purpose agent tasks, visual coding, and cybersecurity. We release this SFT checkpoint as a starting point for open research in agentic reinforcement learning.
## Evaluation
Results for the released SFT checkpoint, as reported in the MiMo-V2.6 technical report.
| Domain | Benchmark | Metric | Qwen3.5-9B | MiMo-V2.6-Distill-Qwen-9B (SFT) |
| --- | --- | --- | ---: | ---: |
| Code | SWE Verified | avg@3 | 60.0 | **61.1** |
| Code | SWE Pro | avg@3 | 32.0 | **44.6** |
| Code | MiMo Code (mini)† | avg@3 | 19.5 | **51.6** |
| Cyber | MiMo Cyber (mini)† | avg@3 | 5.7 | **31.3** |
| General | AutomationBench v1.0.6 | avg@1 | 5.0 | **30.3** |
| General | Terminal Bench 2.1 | avg@1 | 27.0 | **37.1** |
| General | Toolathlon-Verified | avg@1 | 25.9 | **35.2** |
| General | OfficeQA | avg@1 | 9.0 | **19.5** |
| General | JobBench | avg@1 | 2.6 | **18.3** |
| General | MiMo General (mini)† | avg@1 | 28.5 | **62.2** |
| Visual | MiMo Visual Coding (mini)† | avg@1 | 61.7 | **64.0** |
† Internal evaluation sets.
## Training Data
The weighted SFT data mixture contains 77.4B total tokens, including 27.2B loss-bearing tokens.
| Domain | Total tokens (B) | Token share (%) | Loss-bearing tokens (B) |
| --- | ---: | ---: | ---: |
| Code | 23.2 | 29.9 | 7.3 |
| Cyber | 11.0 | 14.2 | 4.8 |
| General | 22.0 | 28.5 | 5.7 |
| Visual | 21.2 | 27.4 | 9.4 |
| **Total** | **77.4** | **100.0** | **27.2** |
## Quickstart
For text generation, use a recent [SGLang](https://docs.sglang.io/get_started/install.html) build with Qwen3.5 support. The checkpoint includes its tokenizer and MiMo v2.6 chat template.
```bash
sglang serve \
--model-path XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B \
--reasoning-parser mimo \
--host 0.0.0.0 \
--port 30000
```
Query the endpoint with thinking explicitly enabled:
```python
from openai import OpenAI
client = OpenAI(
base_url="http://localhost:30000/v1",
api_key="EMPTY",
)
response = client.chat.completions.create(