Instructions to use XiaomiMiMo/MiMo-7B-RL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use XiaomiMiMo/MiMo-7B-RL with Transformers:

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

pipe = pipeline("text-generation", model="XiaomiMiMo/MiMo-7B-RL", 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-7B-RL", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use XiaomiMiMo/MiMo-7B-RL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XiaomiMiMo/MiMo-7B-RL"
# 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-7B-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/XiaomiMiMo/MiMo-7B-RL

SGLang

How to use XiaomiMiMo/MiMo-7B-RL 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-7B-RL" \
    --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-7B-RL",
		"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-7B-RL" \
        --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-7B-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use XiaomiMiMo/MiMo-7B-RL with Docker Model Runner:
```
docker model run hf.co/XiaomiMiMo/MiMo-7B-RL
```

bwshen-mi commited on May 13, 2025

Commit

8aa36ea

verified ·

1 Parent(s): 5304ef1

Update README.md

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Files changed (1) hide show

README.md +8 -6

README.md CHANGED Viewed

@@ -28,7 +28,7 @@ library_name: transformers
   &nbsp;|
   <a href="https://www.modelscope.cn/organization/XiaomiMiMo" target="_blank">🤖️ ModelScope</a>
   &nbsp;|
-  <a href="https://github.com/XiaomiMiMo/MiMo/blob/main/MiMo-7B-Technical-Report.pdf" target="_blank">📔 Technical Report</a>
   &nbsp;|
   <br/>
 </div>
@@ -203,7 +203,7 @@ Example script
 ```py
 from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer
-model_id = "XiaomiMiMo/MiMo-7B-Base"
 model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 inputs = tokenizer(["Today is"], return_tensors='pt')
@@ -221,16 +221,18 @@ print(tokenizer.decode(output.tolist()[0]))
 ## V. Citation
 ```bibtex
-@misc{xiaomi2025mimo,
-      title={MiMo: Unlocking the Reasoning Potential of Language Model – From Pretraining to Posttraining},
       author={{Xiaomi LLM-Core Team}},
       year={2025},
       primaryClass={cs.CL},
-      url={https://github.com/XiaomiMiMo/MiMo},
 }
 ```
 ## VI. Contact
-Please contact us at [mimo@xiaomi.com](mailto:mimo@xiaomi.com) or open an issue if you have any questions.

   &nbsp;|
   <a href="https://www.modelscope.cn/organization/XiaomiMiMo" target="_blank">🤖️ ModelScope</a>
   &nbsp;|
+  <a href="https://arxiv.org/abs/2505.07608" target="_blank">📔 Technical Report</a>
   &nbsp;|
   <br/>
 </div>
 ```py
 from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer
+model_id = "XiaomiMiMo/MiMo-7B-RL"
 model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 inputs = tokenizer(["Today is"], return_tensors='pt')
 ## V. Citation
 ```bibtex
+@misc{coreteam2025mimounlockingreasoningpotential,
+      title={MiMo: Unlocking the Reasoning Potential of Language Model -- From Pretraining to Posttraining},
       author={{Xiaomi LLM-Core Team}},
       year={2025},
+      eprint={2505.07608},
+      archivePrefix={arXiv},
       primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2505.07608},
 }
 ```
 ## VI. Contact
+Please contact us at [mimo@xiaomi.com](mailto:mimo@xiaomi.com) or open an issue if you have any questions.