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

Libraries

How to use ubergarm/MiniMax-M2.5-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ubergarm/MiniMax-M2.5-GGUF",
	filename="IQ2_KS/MiniMax-M2.5-IQ2_KS-00001-of-00003.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use ubergarm/MiniMax-M2.5-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ubergarm/MiniMax-M2.5-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf ubergarm/MiniMax-M2.5-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ubergarm/MiniMax-M2.5-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf ubergarm/MiniMax-M2.5-GGUF:Q2_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 ubergarm/MiniMax-M2.5-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf ubergarm/MiniMax-M2.5-GGUF:Q2_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 ubergarm/MiniMax-M2.5-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ubergarm/MiniMax-M2.5-GGUF:Q2_K

Use Docker

docker model run hf.co/ubergarm/MiniMax-M2.5-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use ubergarm/MiniMax-M2.5-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ubergarm/MiniMax-M2.5-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": "ubergarm/MiniMax-M2.5-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ubergarm/MiniMax-M2.5-GGUF:Q2_K

Ollama
How to use ubergarm/MiniMax-M2.5-GGUF with Ollama:
```
ollama run hf.co/ubergarm/MiniMax-M2.5-GGUF:Q2_K
```

Unsloth Studio new

How to use ubergarm/MiniMax-M2.5-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 ubergarm/MiniMax-M2.5-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 ubergarm/MiniMax-M2.5-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

Pi new

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ubergarm/MiniMax-M2.5-GGUF:Q2_K

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": "ubergarm/MiniMax-M2.5-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ubergarm/MiniMax-M2.5-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 ubergarm/MiniMax-M2.5-GGUF:Q2_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 ubergarm/MiniMax-M2.5-GGUF:Q2_K

Run Hermes

hermes

Docker Model Runner
How to use ubergarm/MiniMax-M2.5-GGUF with Docker Model Runner:
```
docker model run hf.co/ubergarm/MiniMax-M2.5-GGUF:Q2_K
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ubergarm/MiniMax-M2.5-GGUF:Q2_K

Run and chat with the model

lemonade run user.MiniMax-M2.5-GGUF-Q2_K

List all available models

lemonade list

Direct link for IQ5_K

by mayhem4markets - opened Feb 13

Discussion

mayhem4markets

Feb 13

I may be missing something, but I don't see a direct link to download IQ5_K? When I go to https://huggingface.co/ubergarm/MiniMax-M2.5-GGUF/tree/main/IQ5_K and select Use this model and then scroll down to llama.cpp, it links me to the IQ4_XS version. Is there another way to grab it or does something at HF need to be updated? Thanks for your work!

ubergarm

Owner Feb 13

Ahh, so huggingface has some issues with ik_llama.cpp specific quants, but they work fine. Here is how I suggest downloading them:

# pip install huggingface_hub
hf download --local-dir ./MiniMax-M2.5-GGUF/ --include=IQ5_K/*.gguf ubergarm/MiniMax-M2.5-GGUF

Then just pass the first model file in the llama-server ... command and it will work from there!

Let me know if you get stuck or have any issues!

This one is working pretty well so far with opencode as i'm testing while cooking quants haha

mayhem4markets

Feb 13

Thank you!

mayhem4markets

Feb 13

•

edited Feb 13

Gave it a go, but having an issue. May just be llama.cpp not supporting this quant? I defer to your wisdom.

Used llama-server -m path_to_first_model_GGUF

Error (truncated for efficiency):

gguf_init_from_file_impl: tensor 'blk.0.ffn_gate_exps.weight' has invalid ggml type 140 (NONE)
gguf_init_from_file_impl: failed to read tensor info
llama_model_load: error loading model: llama_model_loader: failed to load GGUF split from llama.cpp/MiniMax-M2.5-GGUF/IQ5_K/MiniMax-M2.5-IQ5_K-00002-of-00005.gguf
llama_model_load_from_file_impl: failed to load model

Despite the error message, I do have all five chunks fully downloaded and there were no errors in the downloads that I saw. I am interested in the quants you are producing as they seem to have very low perplexity scores. Any thoughts? Thanks again for your help and work.

ubergarm

Owner Feb 13

@mayhem4markets

So the IQ5_KS is for ik_llama.cpp only. The only quant I released that works on mainline llama.cpp is the IQ4_XS.

You can see the quickstart here for how to quickly compile ik_llama.cpp which works very similarly to llama.cpp given ik worked on mainline years ago and they are a fork of each other: https://huggingface.co/ubergarm/MiniMax-M2.5-GGUF#quick-start

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