Instructions to use noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF",
	filename="GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp.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 noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-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 noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF:MXFP4_MOE_XL
# Run inference directly in the terminal:
llama cli -hf noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF:MXFP4_MOE_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF:MXFP4_MOE_XL
# Run inference directly in the terminal:
llama cli -hf noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF:MXFP4_MOE_XL

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 noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF:MXFP4_MOE_XL
# Run inference directly in the terminal:
./llama-cli -hf noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF:MXFP4_MOE_XL

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 noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF:MXFP4_MOE_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF:MXFP4_MOE_XL

Use Docker

docker model run hf.co/noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF:MXFP4_MOE_XL

LM Studio
Jan

vLLM

How to use noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-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": "noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF:MXFP4_MOE_XL

Ollama

How to use noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF with Ollama:

ollama run hf.co/noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF:MXFP4_MOE_XL

Unsloth Studio

How to use noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-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 noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-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 noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF to start chatting

How to use noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF:MXFP4_MOE_XL

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": "noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF:MXFP4_MOE_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-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 noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF:MXFP4_MOE_XL

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 noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF:MXFP4_MOE_XL

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF with Docker Model Runner:
```
docker model run hf.co/noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF:MXFP4_MOE_XL
```

Lemonade

How to use noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF:MXFP4_MOE_XL

Run and chat with the model

lemonade run user.GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF-MXFP4_MOE_XL

List all available models

lemonade list

This is an experimental MXFP4_MOE quantization of the model GLM-4.7-Flash.

I have created an importance-aware MXFP4_MOE quantization that dynamically allocates precision based on tensor importance scores from an imatrix I created with code_tiny.
This is a coding optimized quantization and is slightly larger than the mainline MXFP4_MOE, and the way it works is that it keeps a better quantization depending on the importance of each tensor.

BF16 (16-bit) for highly important tensors (>75% importance)
Q8_0 (8-bit) for moderately important tensors (>60% importance)
MXFP4 (4-bit) for less important tensors (<50% importance)

As I've mentioned it is experimental, and still not have done any benchmark on it, to see if it's any better than mainline, but you are freely to try it out and report back!

Downloads last month: 67

GGUF

Model size

30B params

Architecture

deepseek2

Hardware compatibility

4-bit

Model tree for noctrex/GLM-4.7-Flash-i1-MXFP4_MOE_XL-exp-GGUF

Base model

zai-org/GLM-4.7-Flash

Quantized

(87)

this model