Instructions to use QuantPasture/GLM-5.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use QuantPasture/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 QuantPasture/GLM-5.1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantPasture/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 QuantPasture/GLM-5.1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantPasture/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 QuantPasture/GLM-5.1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantPasture/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 QuantPasture/GLM-5.1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantPasture/GLM-5.1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantPasture/GLM-5.1-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use QuantPasture/GLM-5.1-GGUF with Ollama:
ollama run hf.co/QuantPasture/GLM-5.1-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use QuantPasture/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 QuantPasture/GLM-5.1-GGUF: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": "QuantPasture/GLM-5.1-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use QuantPasture/GLM-5.1-GGUF with Docker Model Runner:
docker model run hf.co/QuantPasture/GLM-5.1-GGUF:Q4_K_M
- Lemonade
How to use QuantPasture/GLM-5.1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantPasture/GLM-5.1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.GLM-5.1-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use QuantPasture/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 QuantPasture/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 QuantPasture/GLM-5.1-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use QuantPasture/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 QuantPasture/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 "QuantPasture/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"
Q5_K_M inference speed on llama.cpp vs ik_llama.cpp
Thank you @AesSedai for these quants.
I was measuring hybrid GPU+CPU inference speeds of Q5_K_M quant on ik_llama.cpp and llama.cpp (GGML in the charts). It was meant for my own needs but the results are quite interesting so I will post it here. I threw-in @ubergarm 's IQ4_K and Unsloth's Q6_K_XL for comparison.
This is on EPYC 9355 and a single RTX PRO 6000, set-up with 160K context size, -b 8192 -ub 8192, ik -mla 3 -amb 512 -mqkv -muge, not taking more than 45 GB VRAM, measured with llama-benchy.
