Instructions to use muzzy/GLM-5.2-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 muzzy/GLM-5.2-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 muzzy/GLM-5.2-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf muzzy/GLM-5.2-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf muzzy/GLM-5.2-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf muzzy/GLM-5.2-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 muzzy/GLM-5.2-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf muzzy/GLM-5.2-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 muzzy/GLM-5.2-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf muzzy/GLM-5.2-GGUF:Q2_K

Use Docker

docker model run hf.co/muzzy/GLM-5.2-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use muzzy/GLM-5.2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "muzzy/GLM-5.2-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": "muzzy/GLM-5.2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/muzzy/GLM-5.2-GGUF:Q2_K

Ollama
How to use muzzy/GLM-5.2-GGUF with Ollama:
```
ollama run hf.co/muzzy/GLM-5.2-GGUF:Q2_K
```

Unsloth Studio

How to use muzzy/GLM-5.2-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 muzzy/GLM-5.2-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 muzzy/GLM-5.2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for muzzy/GLM-5.2-GGUF to start chatting

How to use muzzy/GLM-5.2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf muzzy/GLM-5.2-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": "muzzy/GLM-5.2-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use muzzy/GLM-5.2-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 muzzy/GLM-5.2-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 muzzy/GLM-5.2-GGUF:Q2_K

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use muzzy/GLM-5.2-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf muzzy/GLM-5.2-GGUF:Q2_K

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 "muzzy/GLM-5.2-GGUF:Q2_K" \
  --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"

Docker Model Runner
How to use muzzy/GLM-5.2-GGUF with Docker Model Runner:
```
docker model run hf.co/muzzy/GLM-5.2-GGUF:Q2_K
```

Lemonade

How to use muzzy/GLM-5.2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull muzzy/GLM-5.2-GGUF:Q2_K

Run and chat with the model

lemonade run user.GLM-5.2-GGUF-Q2_K

List all available models

lemonade list

UPDATE 08/01/2026

Everything is fixed, perplexities are calculated, I will be running swebench on all these weights soon (including the incorrectly made ones).

UPDATE 07/28/2026

I got the imatrix computed against ubergarm-imatrix-calibration-corpus-v02.txt, I bit the bullet and got a spot gcp host with 192 turin cores and 1.5 TB of ram for 5 hours (of which only 2 of those hours were actually for calculating the imatrix, the rest were downloading the gull weight gguf from huggingface and loading it. Cloud computing is a scam).

I am uploading the new quants in the root of the repo. The old quants have been moved to old-wiki-text-raw and may be deleted soon if I run out of huggingface space. The ubergarm corpus imatrix has been uploaded also. I'd love to run both quants against a mini benchmark to see just how well ubergarm's corpus does.

UPDATE 07/26/2026

These quants were made against wiki text raw rather than ubergarm's dataset. I am in the process of requanting with GGML_CUDA_NO_PINNED=1 ./build/bin/llama-imatrix -m /rulers/GLM5.2-GGUF/orig/GLM5.2-chris-00001-of-00033.gguf -f /rulers/ubergarm-imatrix-calibration-corpus-v02.txt -o /rulers/GLM5.2-imatrix-ubergarm.gguf --fit --dsa

This will take me days.

'ik_llama.cpp' imatrix quants of zai-org/GLM-5.2

These quants were made with the same scripts that Ubergarm used for his GLM5.1 quants. Many thanks to him!

Quant	Ubergarm Corpus perplexity against wiki.text raw	wiki.text raw perplexity against itself (original incorrect quants)
IQ1	4.6923 +/- 0.02774	4.4567 +/- 0.02620
IQ2 KS	3.8768 +/- 0.02205	3.7897 +/- 0.02148
IQ2 KL	3.1302 +/- 0.01712	3.1085 +/- 0.01690
IQ3	2.8577 +/- 0.01533	2.8533 +/- 0.01529
IQ4	2.7413 +/- 0.01450	2.7357 +/- 0.01445

Downloads last month: 7,226

GGUF

Model size

753B params

Architecture

glm-dsa

Hardware compatibility

2-bit

View +5 variants

Model tree for muzzy/GLM-5.2-GGUF

Base model

zai-org/GLM-5.2

Quantized

(130)

this model