Text Generation
Transformers
Safetensors
English
Chinese
mimo_v2
multimodal
vision-language
audio
agent
video-understanding
long-context
conversational
custom_code
8-bit precision
fp8
Instructions to use XiaomiMiMo/MiMo-V2.6-Flash-MOPD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use XiaomiMiMo/MiMo-V2.6-Flash-MOPD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="XiaomiMiMo/MiMo-V2.6-Flash-MOPD", 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-V2.6-Flash-MOPD", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use XiaomiMiMo/MiMo-V2.6-Flash-MOPD with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "XiaomiMiMo/MiMo-V2.6-Flash-MOPD" # 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-V2.6-Flash-MOPD", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/XiaomiMiMo/MiMo-V2.6-Flash-MOPD
- SGLang
How to use XiaomiMiMo/MiMo-V2.6-Flash-MOPD 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-V2.6-Flash-MOPD" \ --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-V2.6-Flash-MOPD", "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-V2.6-Flash-MOPD" \ --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-V2.6-Flash-MOPD", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use XiaomiMiMo/MiMo-V2.6-Flash-MOPD with Docker Model Runner:
docker model run hf.co/XiaomiMiMo/MiMo-V2.6-Flash-MOPD
Download assets/mimo-repetition-rate-flash-en.png from XiaomiMiMo/MiMo-V2.6-Flash-MOPD: direct link, hf CLI and curl.
- Browser
- Download file 477 kB
-
https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-MOPD/resolve/main/assets/mimo-repetition-rate-flash-en.png
- Command line
-
hf download hf://XiaomiMiMo/MiMo-V2.6-Flash-MOPD/assets/mimo-repetition-rate-flash-en.png
-
curl -L -o mimo-repetition-rate-flash-en.png https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-MOPD/resolve/main/assets/mimo-repetition-rate-flash-en.png
477 kB

- Xet hash:
- 2aef9d484d09d27ab0e41feb5268b3e0cdd7c6f9e4ff1a3103ab7e8051d0a66b
- Size of remote file:
- 477 kB
- SHA256:
- cf12fd717f3782e9b76c6a2d6285570bf6d58524910fbcc8d22b286990c8a49b
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