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 shaikat2007/MiMo-V2.6-Pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shaikat2007/MiMo-V2.6-Pro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="shaikat2007/MiMo-V2.6-Pro", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("shaikat2007/MiMo-V2.6-Pro", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use shaikat2007/MiMo-V2.6-Pro with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "shaikat2007/MiMo-V2.6-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": "shaikat2007/MiMo-V2.6-Pro", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/shaikat2007/MiMo-V2.6-Pro
- SGLang
How to use shaikat2007/MiMo-V2.6-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 "shaikat2007/MiMo-V2.6-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": "shaikat2007/MiMo-V2.6-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 "shaikat2007/MiMo-V2.6-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": "shaikat2007/MiMo-V2.6-Pro", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use shaikat2007/MiMo-V2.6-Pro with Docker Model Runner:
docker model run hf.co/shaikat2007/MiMo-V2.6-Pro
Download model.safetensors.index.json from shaikat2007/MiMo-V2.6-Pro: direct link, hf CLI and curl.
- Browser
- Download file 15.2 MB
-
https://huggingface.co/shaikat2007/MiMo-V2.6-Pro/resolve/main/model.safetensors.index.json
- Command line
-
hf download hf://shaikat2007/MiMo-V2.6-Pro/model.safetensors.index.json
-
curl -L -o model.safetensors.index.json https://huggingface.co/shaikat2007/MiMo-V2.6-Pro/resolve/main/model.safetensors.index.json
15.2 MB
- Xet hash:
- 17bb814b6a99fe2120e2f461ca53b1c2d7eeba3673f3193faf7878561477c681
- Size of remote file:
- 15.2 MB
- SHA256:
- e855ee9dd7ae748a6258b0725ca217d382a93561d17fc3dbf84d83a152deb54b
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