Instructions to use XiaomiMiMo/MiMo-V2.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use XiaomiMiMo/MiMo-V2.5 with Transformers:

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

pipe = pipeline("text-generation", model="XiaomiMiMo/MiMo-V2.5", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("XiaomiMiMo/MiMo-V2.5", trust_remote_code=True, device_map="auto")

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use XiaomiMiMo/MiMo-V2.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XiaomiMiMo/MiMo-V2.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XiaomiMiMo/MiMo-V2.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/XiaomiMiMo/MiMo-V2.5

SGLang

How to use XiaomiMiMo/MiMo-V2.5 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 "XiaomiMiMo/MiMo-V2.5" \
    --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": "XiaomiMiMo/MiMo-V2.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "XiaomiMiMo/MiMo-V2.5" \
        --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": "XiaomiMiMo/MiMo-V2.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use XiaomiMiMo/MiMo-V2.5 with Docker Model Runner:
```
docker model run hf.co/XiaomiMiMo/MiMo-V2.5
```

Add ResearchClawBench evaluation result

#17

by black-yt - opened May 21

base: refs/heads/main

←

from: refs/pr/17

Discussion Files changed

-0

black-yt

May 21

•

edited May 21

Hi Xiaomi MiMo team,

This PR adds the ResearchClawBench overall evaluation result for MiMo-V2.5.

ResearchClawBench is an end-to-end scientific research benchmark for evaluating AI agents and LLMs on tasks that require reading task data and related work, writing and executing code, producing figures, and generating publication-style reports. Final reports are scored against expert checklists derived from human-authored target papers.

The run was executed with ResearchHarness, using tools enabled, code execution, and a file-system workspace. The submitted value is the overall mean score out of 100 over completed ResearchClawBench tasks:

Model: MiMo-V2.5
Score: 16.91 / 100
Completed tasks: 39/40
Run date: 2026-05-15
Benchmark task id: overall

The detailed leaderboard is available here: https://internscience.github.io/ResearchClawBench-Home/

Thank you!

Add ResearchClawBench evaluation result905cf418

Simplify ResearchClawBench eval notes3cf850a6

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