Instructions to use bartowski/zai-org_GLM-5.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use bartowski/zai-org_GLM-5.1-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="bartowski/zai-org_GLM-5.1-GGUF", filename="zai-org_GLM-5.1-IQ1_M/zai-org_GLM-5.1-IQ1_M-00001-of-00005.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 bartowski/zai-org_GLM-5.1-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 bartowski/zai-org_GLM-5.1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf bartowski/zai-org_GLM-5.1-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf bartowski/zai-org_GLM-5.1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf bartowski/zai-org_GLM-5.1-GGUF: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 bartowski/zai-org_GLM-5.1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf bartowski/zai-org_GLM-5.1-GGUF: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 bartowski/zai-org_GLM-5.1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf bartowski/zai-org_GLM-5.1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/bartowski/zai-org_GLM-5.1-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use bartowski/zai-org_GLM-5.1-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bartowski/zai-org_GLM-5.1-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": "bartowski/zai-org_GLM-5.1-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/bartowski/zai-org_GLM-5.1-GGUF:Q4_K_M
- Ollama
How to use bartowski/zai-org_GLM-5.1-GGUF with Ollama:
ollama run hf.co/bartowski/zai-org_GLM-5.1-GGUF:Q4_K_M
- Unsloth Studio
How to use bartowski/zai-org_GLM-5.1-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 bartowski/zai-org_GLM-5.1-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 bartowski/zai-org_GLM-5.1-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for bartowski/zai-org_GLM-5.1-GGUF to start chatting
- Pi
How to use bartowski/zai-org_GLM-5.1-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf bartowski/zai-org_GLM-5.1-GGUF: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": "bartowski/zai-org_GLM-5.1-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use bartowski/zai-org_GLM-5.1-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 bartowski/zai-org_GLM-5.1-GGUF: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 bartowski/zai-org_GLM-5.1-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use bartowski/zai-org_GLM-5.1-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf bartowski/zai-org_GLM-5.1-GGUF: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 "bartowski/zai-org_GLM-5.1-GGUF: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"
- Docker Model Runner
How to use bartowski/zai-org_GLM-5.1-GGUF with Docker Model Runner:
docker model run hf.co/bartowski/zai-org_GLM-5.1-GGUF:Q4_K_M
- Lemonade
How to use bartowski/zai-org_GLM-5.1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull bartowski/zai-org_GLM-5.1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.zai-org_GLM-5.1-GGUF-Q4_K_M
List all available models
lemonade list
Highest performance inference on <8 RTX 6000 Pros setups
Is there any way to run any of those quants via high performance engine like sglang or vllm?
Quantized safetensor versions dont fit into 1/2/4 GPUs like VLLMs wants and not sure where they are with gguf support. Just wondering if anyone did setup like that. VLLM's TP needs 1/2/4/8 GPUs.
Have you checked the ik-llama.cpp fork? You can find GLM-5.1 quants for it from Ubergarm here on huggingface. Multi-GPU performance is superior to llama.cpp mainline.
Another option would be exllama3, GLM-5 seems not yet supported, but you can contact turboderp and ask if and when support will land.
Yep, ik_llama is the way now. I'm getting 500-1000 prefil with 20-40tps generation. Its only 1 thread though and slows down a lot past 70k context. Can still pull off 200k sessions but its tough and agentic workflows are not great without parallelism. Ik has graph support which can efficiently utilize arbitrary number of gpus and its not supported for glm/kimi.