Instructions to use lovedheart/MiMo-V2.6-Distill-Qwen-9B-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lovedheart/MiMo-V2.6-Distill-Qwen-9B-FP8 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="lovedheart/MiMo-V2.6-Distill-Qwen-9B-FP8")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("lovedheart/MiMo-V2.6-Distill-Qwen-9B-FP8")
model = AutoModelForMultimodalLM.from_pretrained("lovedheart/MiMo-V2.6-Distill-Qwen-9B-FP8", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lovedheart/MiMo-V2.6-Distill-Qwen-9B-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lovedheart/MiMo-V2.6-Distill-Qwen-9B-FP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lovedheart/MiMo-V2.6-Distill-Qwen-9B-FP8",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/lovedheart/MiMo-V2.6-Distill-Qwen-9B-FP8

SGLang

How to use lovedheart/MiMo-V2.6-Distill-Qwen-9B-FP8 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "lovedheart/MiMo-V2.6-Distill-Qwen-9B-FP8" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lovedheart/MiMo-V2.6-Distill-Qwen-9B-FP8",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "lovedheart/MiMo-V2.6-Distill-Qwen-9B-FP8" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lovedheart/MiMo-V2.6-Distill-Qwen-9B-FP8",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use lovedheart/MiMo-V2.6-Distill-Qwen-9B-FP8 with Docker Model Runner:
```
docker model run hf.co/lovedheart/MiMo-V2.6-Distill-Qwen-9B-FP8
```

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 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 build with Qwen3.5 support. The checkpoint includes its tokenizer and MiMo v2.6 chat template.

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:

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:30000/v1",
    api_key="EMPTY",
)

response = client.chat.completions.create(
    model="XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B",
    messages=[
        {"role": "user", "content": "What is 15% of 240?"}
    ],
    max_tokens=2048,
    extra_body={"chat_template_kwargs": {"enable_thinking": True}},
)

message = response.choices[0].message
print("Thinking:", getattr(message, "reasoning_content", "") or "")
print("Answer:", message.content or "")

Citation

@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}},
}

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Safetensors

Model size

9B params

Tensor type

BF16

F8_E4M3

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