Instructions to use unsloth/GLM-4.6-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unsloth/GLM-4.6-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="unsloth/GLM-4.6-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("unsloth/GLM-4.6-GGUF", dtype="auto") - llama-cpp-python
How to use unsloth/GLM-4.6-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="unsloth/GLM-4.6-GGUF", filename="BF16/GLM-4.6-BF16-00001-of-00015.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/GLM-4.6-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/GLM-4.6-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama-cli -hf unsloth/GLM-4.6-GGUF:UD-Q4_K_XL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf unsloth/GLM-4.6-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama-cli -hf unsloth/GLM-4.6-GGUF:UD-Q4_K_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 unsloth/GLM-4.6-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./llama-cli -hf unsloth/GLM-4.6-GGUF:UD-Q4_K_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 unsloth/GLM-4.6-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./build/bin/llama-cli -hf unsloth/GLM-4.6-GGUF:UD-Q4_K_XL
Use Docker
docker model run hf.co/unsloth/GLM-4.6-GGUF:UD-Q4_K_XL
- LM Studio
- Jan
- vLLM
How to use unsloth/GLM-4.6-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "unsloth/GLM-4.6-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-4.6-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/unsloth/GLM-4.6-GGUF:UD-Q4_K_XL
- SGLang
How to use unsloth/GLM-4.6-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "unsloth/GLM-4.6-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "unsloth/GLM-4.6-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "unsloth/GLM-4.6-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "unsloth/GLM-4.6-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use unsloth/GLM-4.6-GGUF with Ollama:
ollama run hf.co/unsloth/GLM-4.6-GGUF:UD-Q4_K_XL
- Unsloth Studio new
How to use unsloth/GLM-4.6-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-4.6-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-4.6-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-4.6-GGUF to start chatting
- Pi new
How to use unsloth/GLM-4.6-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/GLM-4.6-GGUF:UD-Q4_K_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": "unsloth/GLM-4.6-GGUF:UD-Q4_K_XL" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use unsloth/GLM-4.6-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/GLM-4.6-GGUF:UD-Q4_K_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 unsloth/GLM-4.6-GGUF:UD-Q4_K_XL
Run Hermes
hermes
- Docker Model Runner
How to use unsloth/GLM-4.6-GGUF with Docker Model Runner:
docker model run hf.co/unsloth/GLM-4.6-GGUF:UD-Q4_K_XL
- Lemonade
How to use unsloth/GLM-4.6-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull unsloth/GLM-4.6-GGUF:UD-Q4_K_XL
Run and chat with the model
lemonade run user.GLM-4.6-GGUF-UD-Q4_K_XL
List all available models
lemonade list
what's the best Q4 quant?
| Model | Memory | Comment |
|---|---|---|
| Q4_0 | 202 GB | legacy |
| Q4_1 | 224 GB | legacy |
| Q4_K_M | 216 GB | ? |
| IQ4_NL | 202 GB | ? |
| IQ4_XS | 191 GB | ? |
| UD‑Q4_K_XL | 204 GB | Unsloth Dynamics |
Also, there is MXFP4 quants available - 199 GB.
Can someone knowledgeable add comment about PROs & CONs of these quants?
System config:
- Nvidia GPU (in my case 48GB VRAM) and most layers on CPU & RAM.
Few comments I found:
- https://github.com/ggml-org/llama.cpp/wiki/Feature-matrix
- on CPU or Vulkan, I-quants are slower than K-quants of comparable size
Usually I recommend the K_XL one!
today I try UD-Q4_K_XL and UD-Q8_XL, the think process is very verbose.
with Q8_XL, just send hi get 1195 token response, most of them are think. for Chinese "你好" it response OK.
with UD-Q4_K_XL, hi and 你好 both cause 1300 ~ 1700 token. I try -temp 1.0, with and without --top-p 0.95 --top-k 40.
ubergarm/GLM-4.6-IQ5_K hi response 1300 token.
today I try
UD-Q4_K_XLandUD-Q8_XL, the think process is very verbose.with
Q8_XL, just sendhiget 1195 token response, most of them are think. for Chinese "你好" it response OK.with
UD-Q4_K_XL,hiand你好both cause 1300 ~ 1700 token. I try-temp 1.0, with and without--top-p 0.95 --top-k 40.
ubergarm/GLM-4.6-IQ5_Khiresponse 1300 token.
How many times did you test it? I tried Q4_K_XL and got an average of ~1.2k-1.5k. I tried around 10 times
I try more than 10 times, I guess this is normal for GLM-4.6