Text Generation
Transformers
Safetensors
English
Chinese
mimo_v2
agent
long-context
code
conversational
custom_code
Eval Results
fp8
Instructions to use XiaomiMiMo/MiMo-V2.5-Pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use XiaomiMiMo/MiMo-V2.5-Pro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="XiaomiMiMo/MiMo-V2.5-Pro", 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-Pro", trust_remote_code=True, device_map="auto") - Inference
- HuggingChat
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use XiaomiMiMo/MiMo-V2.5-Pro 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-Pro" # 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-Pro", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/XiaomiMiMo/MiMo-V2.5-Pro
- SGLang
How to use XiaomiMiMo/MiMo-V2.5-Pro 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-Pro" \ --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-Pro", "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-Pro" \ --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-Pro", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use XiaomiMiMo/MiMo-V2.5-Pro with Docker Model Runner:
docker model run hf.co/XiaomiMiMo/MiMo-V2.5-Pro
https://huggingface.co/datasets/llamaindex/ParseBench/discussions/1#69d840512a84200570b1bdd1
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by milezdeep13 - opened
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<strong>Community</strong><br/>
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<a href="https://discord.gg/
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- [WeChat Group](https://work.weixin.qq.com/apph5/external_room/join/group_mng?plg_id=c417f99bd9014b5dd894daa8bfe19790&)
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- [Discord](https://discord.gg/WX2R2uNp)
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- [Telegram](https://t.me/+3T-I0pekOVIyNDBl)
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- [Reddit](https://www.reddit.com/r/XiaomiMiMo_Official/)
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<div align="center" style="line-height: 1.2;">
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<strong>Community</strong><br/>
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<a href="https://work.weixin.qq.com/apph5/external_room/join/group_mng?plg_id=c417f99bd9014b5dd894daa8bfe19790&" target="_blank">WeChat Group</a>
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<a href="https://discord.gg/WX2R2uNp" target="_blank">Discord</a>
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<a href="https://t.me/+3T-I0pekOVIyNDBl" target="_blank">Telegram</a>
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- [WeChat Group](https://work.weixin.qq.com/apph5/external_room/join/group_mng?plg_id=c417f99bd9014b5dd894daa8bfe19790&)
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- [Discord](https://discord.gg/WX2R2uNp)
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- [Telegram](https://t.me/+3T-I0pekOVIyNDBl)
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- [Reddit](https://www.reddit.com/r/XiaomiMiMo_Official/)
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