Instructions to use Foremost04/Mimo-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Foremost04/Mimo-base with Transformers:

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

pipe = pipeline("image-text-to-text", model="Foremost04/Mimo-base")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Foremost04/Mimo-base")
model = AutoModelForMultimodalLM.from_pretrained("Foremost04/Mimo-base", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Foremost04/Mimo-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Foremost04/Mimo-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Foremost04/Mimo-base",
		"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/Foremost04/Mimo-base

SGLang

How to use Foremost04/Mimo-base 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 "Foremost04/Mimo-base" \
    --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": "Foremost04/Mimo-base",
		"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 "Foremost04/Mimo-base" \
        --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": "Foremost04/Mimo-base",
		"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 Foremost04/Mimo-base with Docker Model Runner:
```
docker model run hf.co/Foremost04/Mimo-base
```

Mimo-base / README.md

Foremost04

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	---
	library_name: transformers
	base_model: Qwen/Qwen3.5-9B
	base_model_relation: finetune
	tags:
	- mimo_v2
	- agentic
	- distillation
	- supervised-fine-tuning
	- code
	- tool-use
	license: mit
	---

	# 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(
	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

	```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}},
	}
	```