Instructions to use unsloth/MiniMax-M2.7-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use unsloth/MiniMax-M2.7-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="unsloth/MiniMax-M2.7-GGUF",
	filename="BF16/MiniMax-M2.7-BF16-00001-of-00010.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 unsloth/MiniMax-M2.7-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf unsloth/MiniMax-M2.7-GGUF:UD-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 unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf unsloth/MiniMax-M2.7-GGUF:UD-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 unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_M

Use Docker

docker model run hf.co/unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_M

LM Studio
Jan

vLLM

How to use unsloth/MiniMax-M2.7-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "unsloth/MiniMax-M2.7-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": "unsloth/MiniMax-M2.7-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_M

Ollama
How to use unsloth/MiniMax-M2.7-GGUF with Ollama:
```
ollama run hf.co/unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_M
```

Unsloth Studio new

How to use unsloth/MiniMax-M2.7-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 unsloth/MiniMax-M2.7-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 unsloth/MiniMax-M2.7-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for unsloth/MiniMax-M2.7-GGUF to start chatting

Pi new

How to use unsloth/MiniMax-M2.7-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf unsloth/MiniMax-M2.7-GGUF:UD-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": "unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use unsloth/MiniMax-M2.7-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 unsloth/MiniMax-M2.7-GGUF:UD-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 unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use unsloth/MiniMax-M2.7-GGUF with Docker Model Runner:
```
docker model run hf.co/unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_M
```

Lemonade

How to use unsloth/MiniMax-M2.7-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_M

Run and chat with the model

lemonade run user.MiniMax-M2.7-GGUF-UD-Q4_K_M

List all available models

lemonade list

MiniMax-2.7 GGUF Investigation + Fixes + Benchmarks

pinned

by shimmyshimmer - opened Apr 14

Discussion

shimmyshimmer

Unsloth AI org Apr 14

•

edited Apr 14

Hey guys, we did an investigation into MiniMax-M2.7 GGUF causing NaNs on perplexity. Our findings show the issue affects 21%-38% of all GGUFs on Hugging Face (not just ours).

Other popular community uploaders have 38% (10/26) NaNs, another deleted theirs (1/4), and 22% of ours had NaNs (5/23) - we fixed ours.
When running 99.9% KLD and other metrics, all are fine.
We found overflowing in llama.cpp to be the culprit.
We did PPL, KLD 99.9% benchmarks as well - lower left is better.

Original post on r/Localllama: https://www.reddit.com/r/LocalLLaMA/comments/1slk4di/minimax_m27_gguf_investigation_fixes_benchmarks/

Perplexity NaNs during block 32 - this was also found by the community and other quant uploaders. We also found block 311 to cause issues.
We found that blk.61.ffn_down_exps was the culprit - Q5_K and Q4_K of these produce NaNs starting at chunk 32 during PPL evals. Interestingly IQ4_XS, IQ3_XXS and smaller I quant types do not NaN.
This was quite confusing, since lower bit quants (Q2_K_XL for eg) did NOT NaN, but medium sized quants did (Q4_K_XL)!
We’ve now updated the M2.7 quants at https://huggingface.co/unsloth/MiniMax-M2.7-GGUF to alleviate the issue, though we still do not know the exact cause of the NaN perplexities - it could be a fluke, or most likely large multiplies causing overflows.

Which quants did we test?

10/26 NaNs (38%) found at https://huggingface.co/bartowski/MiniMaxAI_MiniMax-M2.7-GGUF: Chunk-32 failures (9): IQ3_XXS, IQ3_XS, IQ3_M, Q3_K_M, Q3_K_L, Q3_K_XL, Q4_K_S, Q4_1, Q5_K_S. Late failure (1): IQ1_S (crashed at chunk 311)
5/23 NaNs (21%) ours had NaNs - all fixed now at https://huggingface.co/unsloth/MiniMax-M2.7-GGUF: UD-Q4_K_S, UD-Q4_K_M, UD-Q4_K_XL, UD-Q5_K_S, MXFP4_MOE. All block 32.
1/4 NaN Q4_K_M at https://huggingface.co/AesSedai/MiniMax-M2.7-GGUF was deleted due to NaNs. Block 32 as well.

Also, CUDA 13.2 is still definitely an issue. This causes some low bit quants on all models to get gibberish. Some people have dismissed it as not being an issue, but from what we’ve seen, more than 50 people have now confirmed that using CUDA 13.1 and lower fixes it. You can also see some of the public comments in our Hugging Face discussions, Reddit posts etc. NVIDIA has acknowledged that they are investigating the issue - see Unsloth Issue 4849, llama.cpp issue 21255, issue 21371

shimmyshimmer pinned discussion Apr 14

Grossor

Apr 14

I downloaded rhe 8_0 ggufs. Do I need to redownload?

shimmyshimmer

Unsloth AI org Apr 15

I downloaded rhe 8_0 ggufs. Do I need to redownload?

No not necessary, only the ones listed. But you can if you want

TeaDiffusion

Apr 21

I downloaded rhe 8_0 ggufs. Do I need to redownload?

No not necessary, only the ones listed. But you can if you want

which one would you recommend then in the 90-100GB range?

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment