Instructions to use bartowski/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use bartowski/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="bartowski/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF", filename="cerebras_GLM-4.5-Air-REAP-82B-A12B-IQ1_M.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/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf bartowski/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf bartowski/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf bartowski/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf bartowski/cerebras_GLM-4.5-Air-REAP-82B-A12B-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/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf bartowski/cerebras_GLM-4.5-Air-REAP-82B-A12B-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/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf bartowski/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/bartowski/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use bartowski/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bartowski/cerebras_GLM-4.5-Air-REAP-82B-A12B-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/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/bartowski/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF:Q4_K_M
- Ollama
How to use bartowski/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF with Ollama:
ollama run hf.co/bartowski/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF:Q4_K_M
- Unsloth Studio
How to use bartowski/cerebras_GLM-4.5-Air-REAP-82B-A12B-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/cerebras_GLM-4.5-Air-REAP-82B-A12B-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/cerebras_GLM-4.5-Air-REAP-82B-A12B-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/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF to start chatting
- Pi
How to use bartowski/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf bartowski/cerebras_GLM-4.5-Air-REAP-82B-A12B-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/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use bartowski/cerebras_GLM-4.5-Air-REAP-82B-A12B-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 bartowski/cerebras_GLM-4.5-Air-REAP-82B-A12B-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/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use bartowski/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF with Docker Model Runner:
docker model run hf.co/bartowski/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF:Q4_K_M
- Lemonade
How to use bartowski/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull bartowski/cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.cerebras_GLM-4.5-Air-REAP-82B-A12B-GGUF-Q4_K_M
List all available models
lemonade list
IQ4_XS fits 64G mac when set max vram to 61440
Hello. Thank you for the quants.
Just to inform ones who choose right quantization. On 64Gb M1 max macbook, I was able to run IQ4_XS, after setting sudo sysctl iogpu.wired_limit_mb=61440 and closing all other high-RAM apps. Context size set to 65536. Experienced crash after closing llama.cpp with ctrl+c. But before that moment it worked just fine.
Thank you, thank cerebras, thank z.ai, thank Gerganov and all who make this possible.
Great model.
It would have been really awesome if Q4s other than IQ were under 50Gb (for vulkan inference) but yes thank you cerebras, z.ai, Gerganov and bartowski.
Thanks also to the people who suggested that ggerganov put bundle-in model metadata (jinja) such as temp, top_k etc into his revised fileformat!
(that's me! :)
We might expect also higher context size to fit, once TurboQuant lands llama.cpp