Instructions to use unsloth/GLM-5.2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use unsloth/GLM-5.2-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="unsloth/GLM-5.2-GGUF", filename="BF16/GLM-5.2-BF16-00001-of-00033.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 unsloth/GLM-5.2-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-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-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
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
- Pi
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-server -hf unsloth/GLM-5.2-GGUF:UD-Q4_K_M
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-5.2-GGUF:UD-Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
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-server -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 new
- 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
Is it possible to make less than 1 bit quantization?
I'm look for if there is any possible methods to make large frontier models 10x, 20x smaller size, maybe some weights fusion techs?
just don't install it atp
I have tried some older GLM models froms Cerebras REAP (https://huggingface.co/collections/cerebras/cerebras-reap). They were pruned by about 20% and then being quantized (e.g. by Unsloth). But that is still not another 10-20x on top of quantization. REAPed models work ok, but at that point you're probably just chasing shadows.
There are plenty of good enough smaller models out there if you don't have a few spare millions of $ in the bank to whip-up terabytes of VRAM.
That'll be hard - 1-bit is currently 86% smaller and retains around 76.2% accuracy
xD, man how much I want to see IQ0_XXXXXS but no, less then 1 bit quantization isn't possible with our today's compute. The tiniest unit in compute is a 1 or a 0 so... xD
unless if I have been lied to
You are crazy.
Yes, it is possible to do below-1-bit quantization, but it's not trivial to do. Basically, you have to pack individual values in tensors into groups and then quantize the groups - so you basically quantize something like a [0.5, 0.3, 1.2, 0.9] quadruple into say [-2]. As long as the bit-budget for the aggregate is smaller than the number of aggregates, you get a below-1-bit quant.