Instructions to use AesSedai/MiMo-V2.5-Pro-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use AesSedai/MiMo-V2.5-Pro-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 AesSedai/MiMo-V2.5-Pro-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AesSedai/MiMo-V2.5-Pro-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AesSedai/MiMo-V2.5-Pro-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AesSedai/MiMo-V2.5-Pro-GGUF:Q4_K_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 AesSedai/MiMo-V2.5-Pro-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AesSedai/MiMo-V2.5-Pro-GGUF:Q4_K_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 AesSedai/MiMo-V2.5-Pro-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AesSedai/MiMo-V2.5-Pro-GGUF:Q4_K_M

Use Docker

docker model run hf.co/AesSedai/MiMo-V2.5-Pro-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use AesSedai/MiMo-V2.5-Pro-GGUF with Ollama:
```
ollama run hf.co/AesSedai/MiMo-V2.5-Pro-GGUF:Q4_K_M
```
Unsloth Desktop

How to use AesSedai/MiMo-V2.5-Pro-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AesSedai/MiMo-V2.5-Pro-GGUF:Q4_K_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": "AesSedai/MiMo-V2.5-Pro-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use AesSedai/MiMo-V2.5-Pro-GGUF with Docker Model Runner:
```
docker model run hf.co/AesSedai/MiMo-V2.5-Pro-GGUF:Q4_K_M
```

Lemonade

How to use AesSedai/MiMo-V2.5-Pro-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AesSedai/MiMo-V2.5-Pro-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MiMo-V2.5-Pro-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use AesSedai/MiMo-V2.5-Pro-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 AesSedai/MiMo-V2.5-Pro-GGUF:Q4_K_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 AesSedai/MiMo-V2.5-Pro-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AesSedai/MiMo-V2.5-Pro-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AesSedai/MiMo-V2.5-Pro-GGUF:Q4_K_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 "AesSedai/MiMo-V2.5-Pro-GGUF:Q4_K_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"

Any chance you could port the FA implementation and other changes to ik_llama.cpp?

by binahz - opened May 9

Discussion

binahz

May 9

Hey, thanks for implementing support for this model. I'm getting pretty good numbers for in llama.cpp for the Q5 quant. The model's thinking is pretty concise compared to Kimi-K2.6 and it seems to pay attention to details in the middle better than the local instances of GLM-5.1 I've ran. I'm getting 300T/s for prompt processing and 11T/s for inference on a 768GB cpu-gpu hybrid setup.
As this is a massive MoE, I believe the numbers here would be even better on ik_llama.cpp. I've already tried running this quant there, and it fails to load. Is there any chance you could open a PR to port the changes there?

AesSedai

Owner May 9

Quite honestly, I don't run ik_llama.cpp myself but without porting over the conversion, dequant, and inference code I'm not surprised it fails to load. There's enough divergence from the forks as well that I'm not particularly keen on trying to do it myself either. The code is available for someone else to do so though.

binahz

May 9

Got it, seeing as the author of that fork ported the initial changes, perhaps he can do this as well. Again, great work with the model. Definitely an underrated gem that's SOTA for 'local' models.

binahz changed discussion status to closed May 9

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