Instructions to use YanissAmz/MiMo-V2.6-Flash-RL-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use YanissAmz/MiMo-V2.6-Flash-RL-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf YanissAmz/MiMo-V2.6-Flash-RL-GGUF:IQ2_M # Run inference directly in the terminal: llama cli -hf YanissAmz/MiMo-V2.6-Flash-RL-GGUF:IQ2_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf YanissAmz/MiMo-V2.6-Flash-RL-GGUF:IQ2_M # Run inference directly in the terminal: llama cli -hf YanissAmz/MiMo-V2.6-Flash-RL-GGUF:IQ2_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf YanissAmz/MiMo-V2.6-Flash-RL-GGUF:IQ2_M # Run inference directly in the terminal: ./llama-cli -hf YanissAmz/MiMo-V2.6-Flash-RL-GGUF:IQ2_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf YanissAmz/MiMo-V2.6-Flash-RL-GGUF:IQ2_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf YanissAmz/MiMo-V2.6-Flash-RL-GGUF:IQ2_M
Use Docker
docker model run hf.co/YanissAmz/MiMo-V2.6-Flash-RL-GGUF:IQ2_M
- LM Studio
- Jan
- Ollama
How to use YanissAmz/MiMo-V2.6-Flash-RL-GGUF with Ollama:
ollama run hf.co/YanissAmz/MiMo-V2.6-Flash-RL-GGUF:IQ2_M
- Unsloth Desktop
- Pi
How to use YanissAmz/MiMo-V2.6-Flash-RL-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf YanissAmz/MiMo-V2.6-Flash-RL-GGUF:IQ2_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "YanissAmz/MiMo-V2.6-Flash-RL-GGUF:IQ2_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use YanissAmz/MiMo-V2.6-Flash-RL-GGUF with Docker Model Runner:
docker model run hf.co/YanissAmz/MiMo-V2.6-Flash-RL-GGUF:IQ2_M
- Lemonade
How to use YanissAmz/MiMo-V2.6-Flash-RL-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull YanissAmz/MiMo-V2.6-Flash-RL-GGUF:IQ2_M
Run and chat with the model
lemonade run user.MiMo-V2.6-Flash-RL-GGUF-IQ2_M
List all available models
lemonade list
- Hermes Agent
How to use YanissAmz/MiMo-V2.6-Flash-RL-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf YanissAmz/MiMo-V2.6-Flash-RL-GGUF:IQ2_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default YanissAmz/MiMo-V2.6-Flash-RL-GGUF:IQ2_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use YanissAmz/MiMo-V2.6-Flash-RL-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf YanissAmz/MiMo-V2.6-Flash-RL-GGUF:IQ2_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "YanissAmz/MiMo-V2.6-Flash-RL-GGUF:IQ2_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
MOPD version?
Thank you for quanizing this model, it works great with my Radeon AI PRO R9700 (32 gb) plus 96gb system memory. However it seems Xiaomi just released an update, https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-MOPD . Could you consider re doing the quantization with that as a base model?
Hello, thanks for your message. Happy to know it works great, and yeah i will do it very soon. With even higher precision i am trying to