Instructions to use RemySkye/MiniMax-M2.5-REAP-50-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RemySkye/MiniMax-M2.5-REAP-50-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="RemySkye/MiniMax-M2.5-REAP-50-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("RemySkye/MiniMax-M2.5-REAP-50-GGUF", dtype="auto")

llama-cpp-python

How to use RemySkye/MiniMax-M2.5-REAP-50-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RemySkye/MiniMax-M2.5-REAP-50-GGUF",
	filename="MiniMax-M2-5-REAP-50-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use RemySkye/MiniMax-M2.5-REAP-50-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RemySkye/MiniMax-M2.5-REAP-50-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf RemySkye/MiniMax-M2.5-REAP-50-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RemySkye/MiniMax-M2.5-REAP-50-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf RemySkye/MiniMax-M2.5-REAP-50-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 RemySkye/MiniMax-M2.5-REAP-50-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RemySkye/MiniMax-M2.5-REAP-50-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 RemySkye/MiniMax-M2.5-REAP-50-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RemySkye/MiniMax-M2.5-REAP-50-GGUF:Q4_K_M

Use Docker

docker model run hf.co/RemySkye/MiniMax-M2.5-REAP-50-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use RemySkye/MiniMax-M2.5-REAP-50-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RemySkye/MiniMax-M2.5-REAP-50-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RemySkye/MiniMax-M2.5-REAP-50-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RemySkye/MiniMax-M2.5-REAP-50-GGUF:Q4_K_M

SGLang

How to use RemySkye/MiniMax-M2.5-REAP-50-GGUF 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 "RemySkye/MiniMax-M2.5-REAP-50-GGUF" \
    --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": "RemySkye/MiniMax-M2.5-REAP-50-GGUF",
		"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 "RemySkye/MiniMax-M2.5-REAP-50-GGUF" \
        --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": "RemySkye/MiniMax-M2.5-REAP-50-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use RemySkye/MiniMax-M2.5-REAP-50-GGUF with Ollama:
```
ollama run hf.co/RemySkye/MiniMax-M2.5-REAP-50-GGUF:Q4_K_M
```

Unsloth Studio

How to use RemySkye/MiniMax-M2.5-REAP-50-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for RemySkye/MiniMax-M2.5-REAP-50-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for RemySkye/MiniMax-M2.5-REAP-50-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RemySkye/MiniMax-M2.5-REAP-50-GGUF to start chatting

How to use RemySkye/MiniMax-M2.5-REAP-50-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf RemySkye/MiniMax-M2.5-REAP-50-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "RemySkye/MiniMax-M2.5-REAP-50-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use RemySkye/MiniMax-M2.5-REAP-50-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf RemySkye/MiniMax-M2.5-REAP-50-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 RemySkye/MiniMax-M2.5-REAP-50-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use RemySkye/MiniMax-M2.5-REAP-50-GGUF with Docker Model Runner:
```
docker model run hf.co/RemySkye/MiniMax-M2.5-REAP-50-GGUF:Q4_K_M
```

Lemonade

How to use RemySkye/MiniMax-M2.5-REAP-50-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RemySkye/MiniMax-M2.5-REAP-50-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MiniMax-M2.5-REAP-50-GGUF-Q4_K_M

List all available models

lemonade list

MiniMax-M2-5-REAP-50 - GGUF

This repository contains GGUF format model files for Akicou/MiniMax-M2-5-REAP-50.

⚠️ Experimental Warning & Disclaimer

Please note that this is an experimental release. I am new to making GGUF models, and I generated these quantizations using Google Colab. Because I'm still learning the ropes, these files might not be perfect.

Additionally, due to hardware constraints, I have only been able to test the Q2_K quantization as my local system cannot run the higher quants.

You are more than welcome to download and try out the other quantizations! However, please keep in mind that they are entirely untested on my end, and there is no guarantee of high-quality output or stability. Feel happy to experiment, but adjust your expectations accordingly.

Available Quantizations

minimax-m2-5-reap-50-q2_k.gguf
minimax-m2-5-reap-50-q3_k_m.gguf
minimax-m2-5-reap-50-q4_0.gguf
minimax-m2-5-reap-50-q4_k_m.gguf
minimax-m2-5-reap-50-q5_k_m.gguf
minimax-m2-5-reap-50-q8_0.gguf

Downloads last month: 81

GGUF

Model size

116B params

Architecture

minimax-m2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

8-bit