Instructions to use thad0ctor/MiniMax-M2.5-Q6_K-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 thad0ctor/MiniMax-M2.5-Q6_K-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 thad0ctor/MiniMax-M2.5-Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf thad0ctor/MiniMax-M2.5-Q6_K-GGUF:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf thad0ctor/MiniMax-M2.5-Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf thad0ctor/MiniMax-M2.5-Q6_K-GGUF:Q6_K

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 thad0ctor/MiniMax-M2.5-Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf thad0ctor/MiniMax-M2.5-Q6_K-GGUF:Q6_K

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 thad0ctor/MiniMax-M2.5-Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf thad0ctor/MiniMax-M2.5-Q6_K-GGUF:Q6_K

Use Docker

docker model run hf.co/thad0ctor/MiniMax-M2.5-Q6_K-GGUF:Q6_K

LM Studio
Jan
Ollama
How to use thad0ctor/MiniMax-M2.5-Q6_K-GGUF with Ollama:
```
ollama run hf.co/thad0ctor/MiniMax-M2.5-Q6_K-GGUF:Q6_K
```
Unsloth Desktop

How to use thad0ctor/MiniMax-M2.5-Q6_K-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf thad0ctor/MiniMax-M2.5-Q6_K-GGUF:Q6_K

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": "thad0ctor/MiniMax-M2.5-Q6_K-GGUF:Q6_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use thad0ctor/MiniMax-M2.5-Q6_K-GGUF with Docker Model Runner:
```
docker model run hf.co/thad0ctor/MiniMax-M2.5-Q6_K-GGUF:Q6_K
```

Lemonade

How to use thad0ctor/MiniMax-M2.5-Q6_K-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull thad0ctor/MiniMax-M2.5-Q6_K-GGUF:Q6_K

Run and chat with the model

lemonade run user.MiniMax-M2.5-Q6_K-GGUF-Q6_K

List all available models

lemonade list

Hermes Agent

How to use thad0ctor/MiniMax-M2.5-Q6_K-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 thad0ctor/MiniMax-M2.5-Q6_K-GGUF:Q6_K

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 thad0ctor/MiniMax-M2.5-Q6_K-GGUF:Q6_K

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use thad0ctor/MiniMax-M2.5-Q6_K-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf thad0ctor/MiniMax-M2.5-Q6_K-GGUF:Q6_K

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 "thad0ctor/MiniMax-M2.5-Q6_K-GGUF:Q6_K" \
  --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"

thad0ctor commited on Feb 13

Commit

9d41769

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MiniMax-M2.5.Q6_K.gguf +3 -0
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+MIT License
+Copyright 2025 MiniMax AI.
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+Our only modification is that, if the Software (or any derivative works
+thereof) is used for any of your commercial products or services, you shall
+prominently display “MiniMax M2.5” on the user interface of such product or
+service.

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0011113531d03a7c7f408b3fbebf73568c82610fb345de6ff7cb857b00c5f1a6
+size 187763082496

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+---
+base_model: MiniMaxAI/MiniMax-M2.5
+base_model_relation: quantized
+license: other
+license_name: modified-mit
+license_link: LICENSE
+tags:
+- gguf
+- quantized
+- llama.cpp
+---
+# MiniMax-M2.5 GGUF
+GGUF quantization of `MiniMaxAI/MiniMax-M2.5`, created with `llama.cpp`.
+## Model Details
+| Property | Value |
+| --- | --- |
+| Base model | MiniMaxAI/MiniMax-M2.5 |
+| Architecture | Mixture of Experts (MoE) |
+| Total parameters | 230B |
+| Active parameters | 10B per token |
+| Layers | 62 |
+| Total experts | 256 |
+| Active experts per token | 8 |
+| Source precision | FP8 (`float8_e4m3fn`) |
+## Available Quantizations
+| Quantization | Size | Description |
+| --- | --- | --- |
+| Q6_K | 175 GB | 6-bit K-quant, strong quality/size balance |
+## Usage
+These GGUFs can be used with `llama.cpp` and compatible frontends.
+```bash
+# Example with llama-cli
+llama-cli -m MiniMax-M2.5.Q6_K.gguf -p "Hello" -n 128
+```
+## Notes
+- The source model uses FP8 (`float8_e4m3fn`) precision.
+- This is a large MoE model and requires significant memory.
+- Quantized from the official `MiniMaxAI/MiniMax-M2.5` weights.