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

Libraries

How to use XiaomiMiMo/MiMo-V2-Flash with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XiaomiMiMo/MiMo-V2-Flash", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XiaomiMiMo/MiMo-V2-Flash", trust_remote_code=True, device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use XiaomiMiMo/MiMo-V2-Flash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XiaomiMiMo/MiMo-V2-Flash"
# 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-Flash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

bwshen-mi commited on Dec 16, 2025

Commit

4db358b

verified ·

1 Parent(s): 1cc3ac3

Update README.md

Browse files

Files changed (1) hide show

README.md +23 -7

README.md CHANGED Viewed

@@ -5,6 +5,8 @@ base_model:
 library_name: transformers
 ---
 <div align="center">
   <picture>
     <source srcset="https://github.com/XiaomiMiMo/MiMo-V2-Flash/raw/main/figures/Xiaomi_MiMo_darkmode.png?raw=true" media="(prefers-color-scheme: dark)">
@@ -16,13 +18,17 @@ library_name: transformers
 <div align="center" style="line-height: 1;">
   |
-  <a href="https://huggingface.co/xiaomimimo/MiMo-V2-Flash" target="_blank">🤗 HuggingFace</a>
   &nbsp;|
-  <a href="https://github.com/xiaomimimo/MiMo-V2-Flash/paper.pdf" target="_blank">📔 Technical Report </a>
   &nbsp;|
   <a href="https://mimo.xiaomi.com/blog/mimo-v2-flash" target="_blank">📰 Blog </a>
   &nbsp;|
-  <br/>
 </div>
 <br/>
@@ -51,8 +57,8 @@ MiMo-V2-Flash creates a new balance between long-context modeling capability and
 | Model                  | Total Params | Active Params | Context Length |                               Download                                |
 | :--------------------- | :----------: | :-----------: | :------------: | :-------------------------------------------------------------------: |
-| **MiMo-V2-Flash-Base** |     309B     |      15B      |      256k      | [🤗 HuggingFace](https://huggingface.co/xiaomimimo/MiMo-V2-Flash-Base) |
-| **MiMo-V2-Flash**      |     309B     |      15B      |      256k      |   [🤗 HuggingFace](https://huggingface.co/xiaomimimo/MiMo-V2-Flash)    |
 > [!IMPORTANT]
 > We also open-source the 3-layer MTP weights to foster community research.
@@ -248,6 +254,9 @@ curl -i http://localhost:9001/v1/chat/completions \
         }'
 ```
 -----
 ## 7. Citation
@@ -259,10 +268,17 @@ If you find our work helpful, please cite our technical report:
   title={MiMo-V2-Flash Technical Report},
   author={LLM-Core Xiaomi},
   year={2025},
-  url={https://github.com/xiaomimimo/MiMo-V2-Flash/paper.pdf}
 }
 ```
 ## 8. Contact
-Please contact us at [mimo@xiaomi.com](mailto:mimo@xiaomi.com) or open an issue if you have any questions.

 library_name: transformers
 ---
+<br/><br/>
 <div align="center">
   <picture>
     <source srcset="https://github.com/XiaomiMiMo/MiMo-V2-Flash/raw/main/figures/Xiaomi_MiMo_darkmode.png?raw=true" media="(prefers-color-scheme: dark)">
 <div align="center" style="line-height: 1;">
   |
+  <a href="https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash" target="_blank">🤗 HuggingFace</a>
   &nbsp;|
+  <a href="https://github.com/XiaomiMiMo/MiMo-V2-Flash/blob/main/paper.pdf" target="_blank">📔 Technical Report </a>
   &nbsp;|
   <a href="https://mimo.xiaomi.com/blog/mimo-v2-flash" target="_blank">📰 Blog </a>
   &nbsp;|
+  <br/><br/>
+  <strong>Play around!</strong> &nbsp;
+  <a href="https://aistudio.xiaomimimo.com" target="_blank">🗨️ Xiaomi MiMo Studio </a>
+  &nbsp;
+  <a href="https://platform.xiaomimimo.com/" target="_blank">🎨 Xiaomi MiMo API Platform </a>
 </div>
 <br/>
 | Model                  | Total Params | Active Params | Context Length |                               Download                                |
 | :--------------------- | :----------: | :-----------: | :------------: | :-------------------------------------------------------------------: |
+| **MiMo-V2-Flash-Base** |     309B     |      15B      |      256k      | [🤗 HuggingFace](https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash-Base) |
+| **MiMo-V2-Flash**      |     309B     |      15B      |      256k      |   [🤗 HuggingFace](https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash)    |
 > [!IMPORTANT]
 > We also open-source the 3-layer MTP weights to foster community research.
         }'
 ```
+> [!IMPORTANT]
+> In the thinking mode with multi-turn tool calls, the model returns a `reasoning_content` field alongside `tool_calls`. To continue the conversation, the user must persist all history `reasoning_content` in the `messages` array of each subsequent request.
 -----
 ## 7. Citation
   title={MiMo-V2-Flash Technical Report},
   author={LLM-Core Xiaomi},
   year={2025},
+  url={https://github.com/XiaomiMiMo/MiMo-V2-Flash/paper.pdf}
 }
 ```
 ## 8. Contact
+Please contact us at [mimo@xiaomi.com](mailto:mimo@xiaomi.com), join our WeChat group below or open an issue if you have any questions.
+<p align="center">
+  <img src="https://github.com/XiaomiMiMo/MiMo-V2-Flash/raw/main/figures/wechat_group/wechat1.jpg?raw=true" width="20%" />
+  <img src="https://github.com/XiaomiMiMo/MiMo-V2-Flash/raw/main/figures/wechat_group/wechat2.jpg?raw=true" width="20%" />
+  <img src="https://github.com/XiaomiMiMo/MiMo-V2-Flash/raw/main/figures/wechat_group/wechat3.jpg?raw=true" width="20%" />
+  <img src="https://github.com/XiaomiMiMo/MiMo-V2-Flash/raw/main/figures/wechat_group/wechat4.jpg?raw=true" width="20%" />
+</p>