Instructions to use unsloth/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 unsloth/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 unsloth/GLM-5.2-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
llama cli -hf unsloth/GLM-5.2-GGUF:UD-Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf unsloth/GLM-5.2-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
llama cli -hf unsloth/GLM-5.2-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/GLM-5.2-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf unsloth/GLM-5.2-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/GLM-5.2-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf unsloth/GLM-5.2-GGUF:UD-Q4_K_M

Use Docker

docker model run hf.co/unsloth/GLM-5.2-GGUF:UD-Q4_K_M

LM Studio
Jan

vLLM

How to use unsloth/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 "unsloth/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": "unsloth/GLM-5.2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/unsloth/GLM-5.2-GGUF:UD-Q4_K_M

Ollama
How to use unsloth/GLM-5.2-GGUF with Ollama:
```
ollama run hf.co/unsloth/GLM-5.2-GGUF:UD-Q4_K_M
```

Unsloth Studio

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

How to use unsloth/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 unsloth/GLM-5.2-GGUF:UD-Q4_K_M

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": "unsloth/GLM-5.2-GGUF:UD-Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use unsloth/GLM-5.2-GGUF with Docker Model Runner:
```
docker model run hf.co/unsloth/GLM-5.2-GGUF:UD-Q4_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull unsloth/GLM-5.2-GGUF:UD-Q4_K_M

Run and chat with the model

lemonade run user.GLM-5.2-GGUF-UD-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use unsloth/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 unsloth/GLM-5.2-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/GLM-5.2-GGUF:UD-Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use unsloth/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 unsloth/GLM-5.2-GGUF:UD-Q4_K_M

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 "unsloth/GLM-5.2-GGUF:UD-Q4_K_M" \
  --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"

IQ1M larger than GLM 5.1's IQ2XXS?

by nichedreams - opened Jun 19

Discussion

nichedreams

Jun 19

Hey, I thought HLGM 5.1 and 5.2 were the same size parameter wise, do you know how come it's so much larger? And would the quality of the IQ1M one be equivalent to the IQ2XXS of the GLM 5.1 unsloth release?

aviallon

Jun 19

@nichedreams they do dynamic quanitzation of each layer based on calibration data, which means the selection of actual quantization layer-by-layer depends on the model.

nichedreams

Jun 19

@nichedreams they do dynamic quanitzation of each layer based on calibration data, which means the selection of actual quantization layer-by-layer depends on the model.

Yeah, looks like the main difference between both IQ2XXS quants is the ffn_down_exps was quantized to IQ2_S on glm 5.1 vs IQ3_XXS / IQ4_XS for the glm 5.2, was curious if there's any special reason why for that though since problem with iq1m of glm 5.2 is the ffn_gate_exps and ffn_up_exps drop from iq2xxs to iq1m which is pretty rough.

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