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)

# 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)
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=40)
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
```

Domain recognition bias: <a> tags preserved only for amazon URLs

#37

by kazutoshidayon - opened Apr 12

Discussion

kazutoshidayon

Apr 12

Summary

When asked to reproduce HTML containing multiple <a> tags with
different URL domains, MiMo V2 consistently preserves only URLs
from amzn.to (Amazon's URL shortener) and strips <a> tags
from URLs of other domains, even when the prompt explicitly
instructs to preserve HTML exactly as written.

Reproduction

Given a prompt containing this HTML block:

<div>
  <p><a href="https://example1.com/path-a">Link Text 1</a></p>
  <p><a href="https://amzn.to/abc123">Link Text 2</a></p>
  <p><a href="https://example2.com/path-b">Link Text 3</a></p>
  <p><a href="https://example3.net/redirect/xyz">Link Text 4</a></p>
  <p><a href="https://example4.org/svt/ref?id=xxxxxx">Link Text 5</a></p>
</div>

Output:

<div>
  <p>Link Text 1</p>
  <p><a href="https://amzn.to/abc123">Link Text 2</a></p>
  <p>Link Text 3</p>
  <p>Link Text 4</p>
  <p>Link Text 5</p>
</div>

Only the amzn.to URL retains its <a> wrapper.

Tested Variations

Generic short URLs (bit.ly/xxx) — stripped
Custom short domain — stripped
Long URLs with query parameters — stripped
URLs of varying length (20-80 chars) — stripped
Amazon long URLs (amazon.co.jp/dp/XXX?tag=...) — preserved
amzn.to/xxx — preserved

Only amzn.to and amazon.* domains are reliably preserved.

Cross-Model Confirmation

Also reproduced in:

mistralai/mistral-small-creative
mistralai/mistral-small-3.2-24b-instruct

This suggests the bias is not vendor-specific but a general
property of mid-tier LLMs trained on web crawl data. Related
discussion filed at:
https://huggingface.co/mistralai/Mistral-Small-3.2-24B-Instruct-2506/discussions/42

Suspected Cause

Training data bias: Amazon URLs appear with disproportionate
frequency in web crawl datasets. The model learned a strong
prior of "amzn.to → wrap in <a>" but lacks the same prior
for other domains.

Why This Matters

Breaks use cases requiring preservation of arbitrary <a> tags:

E-commerce content with non-Amazon platforms
Internal docs with company-specific URLs
Multi-source citations
Localized content using region-specific platforms

Suggested Improvements

Augment training data with diverse <a> tag examples across
many domains
Increase weight of explicit HTML preservation instructions
Synthetic data to break the Amazon-specific prior

Severity

Medium-High — blocks content generation with non-Amazon links.

Happy to provide more reproduction materials if needed.

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