Instructions to use ubergarm/GLM-4.7-Flash-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 ubergarm/GLM-4.7-Flash-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 ubergarm/GLM-4.7-Flash-GGUF:MXFP4
# Run inference directly in the terminal:
llama cli -hf ubergarm/GLM-4.7-Flash-GGUF:MXFP4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ubergarm/GLM-4.7-Flash-GGUF:MXFP4
# Run inference directly in the terminal:
llama cli -hf ubergarm/GLM-4.7-Flash-GGUF:MXFP4

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/GLM-4.7-Flash-GGUF:MXFP4
# Run inference directly in the terminal:
./llama-cli -hf ubergarm/GLM-4.7-Flash-GGUF:MXFP4

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/GLM-4.7-Flash-GGUF:MXFP4
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ubergarm/GLM-4.7-Flash-GGUF:MXFP4

Use Docker

docker model run hf.co/ubergarm/GLM-4.7-Flash-GGUF:MXFP4

LM Studio
Jan

vLLM

How to use ubergarm/GLM-4.7-Flash-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ubergarm/GLM-4.7-Flash-GGUF:MXFP4

Ollama
How to use ubergarm/GLM-4.7-Flash-GGUF with Ollama:
```
ollama run hf.co/ubergarm/GLM-4.7-Flash-GGUF:MXFP4
```
Unsloth Desktop

How to use ubergarm/GLM-4.7-Flash-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ubergarm/GLM-4.7-Flash-GGUF:MXFP4

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": "ubergarm/GLM-4.7-Flash-GGUF:MXFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ubergarm/GLM-4.7-Flash-GGUF with Docker Model Runner:
```
docker model run hf.co/ubergarm/GLM-4.7-Flash-GGUF:MXFP4
```

Lemonade

How to use ubergarm/GLM-4.7-Flash-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ubergarm/GLM-4.7-Flash-GGUF:MXFP4

Run and chat with the model

lemonade run user.GLM-4.7-Flash-GGUF-MXFP4

List all available models

lemonade list

Hermes Agent

How to use ubergarm/GLM-4.7-Flash-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 ubergarm/GLM-4.7-Flash-GGUF:MXFP4

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/GLM-4.7-Flash-GGUF:MXFP4

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ubergarm/GLM-4.7-Flash-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ubergarm/GLM-4.7-Flash-GGUF:MXFP4

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 "ubergarm/GLM-4.7-Flash-GGUF:MXFP4" \
  --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"

Reporting successful run of MXFP4 using ik_llama cpp

by nimishchaudhari - opened Jan 26

Discussion

nimishchaudhari

Jan 26

Hello,

Thanks for these quants, I tried mxfp4 as it had the least perplexity and it seems to be working much better for me than the UD_Q5 from unsloth.

Command I ran:

      ${ik_llama_cpp}
      -m ${models_dir}/LLMs/GLM-4.7-Flash-MXFP4.gguf
      -a 'GLM_4.7_Flash'
      -ger --special
      --merge-qkv 
      -mla 3 -amb 512 
      -ngl 99 
      -c 100000
      --temp 0.7
      --top-p 1.0
      --min-p 0.01
      --jinja

ubergarm

Owner Feb 2

@nimishchaudhari

Nice, thanks for testing!

I did a few KLD benchmarks suggesting that the MXFP4 while having lowest perplexity, diverges more from the full bf16 than the other two quants I've released. Sorry life is busy right now so slow getting back to folks.

Here is a quick data dump on my limited testing: https://github.com/Thireus/GGUF-Tool-Suite/issues/52#issuecomment-3795175551

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