Instructions to use SixVolts/GLM-5.2-ewaste-edition-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 SixVolts/GLM-5.2-ewaste-edition-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 SixVolts/GLM-5.2-ewaste-edition-GGUF:Q4_K_XL # Run inference directly in the terminal: llama cli -hf SixVolts/GLM-5.2-ewaste-edition-GGUF:Q4_K_XL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf SixVolts/GLM-5.2-ewaste-edition-GGUF:Q4_K_XL # Run inference directly in the terminal: llama cli -hf SixVolts/GLM-5.2-ewaste-edition-GGUF: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 SixVolts/GLM-5.2-ewaste-edition-GGUF:Q4_K_XL # Run inference directly in the terminal: ./llama-cli -hf SixVolts/GLM-5.2-ewaste-edition-GGUF: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 SixVolts/GLM-5.2-ewaste-edition-GGUF:Q4_K_XL # Run inference directly in the terminal: ./build/bin/llama-cli -hf SixVolts/GLM-5.2-ewaste-edition-GGUF:Q4_K_XL
Use Docker
docker model run hf.co/SixVolts/GLM-5.2-ewaste-edition-GGUF:Q4_K_XL
- LM Studio
- Jan
- vLLM
How to use SixVolts/GLM-5.2-ewaste-edition-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SixVolts/GLM-5.2-ewaste-edition-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": "SixVolts/GLM-5.2-ewaste-edition-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/SixVolts/GLM-5.2-ewaste-edition-GGUF:Q4_K_XL
- Ollama
How to use SixVolts/GLM-5.2-ewaste-edition-GGUF with Ollama:
ollama run hf.co/SixVolts/GLM-5.2-ewaste-edition-GGUF:Q4_K_XL
- Unsloth Studio
How to use SixVolts/GLM-5.2-ewaste-edition-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 SixVolts/GLM-5.2-ewaste-edition-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 SixVolts/GLM-5.2-ewaste-edition-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for SixVolts/GLM-5.2-ewaste-edition-GGUF to start chatting
- Pi
How to use SixVolts/GLM-5.2-ewaste-edition-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf SixVolts/GLM-5.2-ewaste-edition-GGUF: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": "SixVolts/GLM-5.2-ewaste-edition-GGUF:Q4_K_XL" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use SixVolts/GLM-5.2-ewaste-edition-GGUF with Docker Model Runner:
docker model run hf.co/SixVolts/GLM-5.2-ewaste-edition-GGUF:Q4_K_XL
- Lemonade
How to use SixVolts/GLM-5.2-ewaste-edition-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull SixVolts/GLM-5.2-ewaste-edition-GGUF:Q4_K_XL
Run and chat with the model
lemonade run user.GLM-5.2-ewaste-edition-GGUF-Q4_K_XL
List all available models
lemonade list
- Hermes Agent
How to use SixVolts/GLM-5.2-ewaste-edition-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 SixVolts/GLM-5.2-ewaste-edition-GGUF: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 SixVolts/GLM-5.2-ewaste-edition-GGUF:Q4_K_XL
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use SixVolts/GLM-5.2-ewaste-edition-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf SixVolts/GLM-5.2-ewaste-edition-GGUF:Q4_K_XL
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 "SixVolts/GLM-5.2-ewaste-edition-GGUF:Q4_K_XL" \ --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"
Thank you so much!!!
Dear SixVolts:
F Fable. This is hands-down the fastest model I have ever run on my HP Proliant Gen 10 server.. no GPUs.. just 112 cores. I am in heaven. Thank you. I am running your highest quantization and wish you had even higher quants. You have made me a G. The time savings this will create for me? It's literally giving a guy years of life span back! Daddy's home! Thanks! I will post the actual tokens per second in a little bit.
What size/kind of quant are you looking for? Q4? Q6?
A Q4_K to fit a 512gb system with the same recipe as your Q3_K_XL (with the non-experts kept at Q8) would be amazing for a dead end 512gb DDR4 system.
Ok, posted the Q4, let me know what you think
Thank you! I'm still setting my inference server up, but I will also post benchmarks for both Q3-XL and Q4 as soon as I can. I can imagine that the difference for both will be linear, since the only change between Q3 and Q4 are the experts. But I guess that's the difference between decoding from maybe 5tok/s to 6-7tok/s.
From what I can see comparing metadata, your quants are better than the other Q4s floating around here, keeping the important parts at Q8, while still being smaller lol.
Also thank you. Q4 runs with 4,5t/s - 1,5t/s (growing context) on cpu only ( epyc 7532 with 8 ccd, ddr 4 2999 ram ) or constant 8,5t/s with offloading to a single rtx5090 on latest llama.cpp.