Instructions to use QuantFactory/codegeex4-all-9b-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 QuantFactory/codegeex4-all-9b-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 QuantFactory/codegeex4-all-9b-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/codegeex4-all-9b-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 QuantFactory/codegeex4-all-9b-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/codegeex4-all-9b-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 QuantFactory/codegeex4-all-9b-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/codegeex4-all-9b-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 QuantFactory/codegeex4-all-9b-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/codegeex4-all-9b-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/codegeex4-all-9b-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use QuantFactory/codegeex4-all-9b-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "QuantFactory/codegeex4-all-9b-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QuantFactory/codegeex4-all-9b-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/QuantFactory/codegeex4-all-9b-GGUF:Q4_K_M
- Ollama
How to use QuantFactory/codegeex4-all-9b-GGUF with Ollama:
ollama run hf.co/QuantFactory/codegeex4-all-9b-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use QuantFactory/codegeex4-all-9b-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/codegeex4-all-9b-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/codegeex4-all-9b-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/codegeex4-all-9b-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.codegeex4-all-9b-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download codegeex4-all-9b.Q4_0.gguf from QuantFactory/codegeex4-all-9b-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 5.46 GB
-
https://huggingface.co/QuantFactory/codegeex4-all-9b-GGUF/resolve/main/codegeex4-all-9b.Q4_0.gguf
- Command line
-
hf download hf://QuantFactory/codegeex4-all-9b-GGUF/codegeex4-all-9b.Q4_0.gguf
-
curl -L -o codegeex4-all-9b.Q4_0.gguf https://huggingface.co/QuantFactory/codegeex4-all-9b-GGUF/resolve/main/codegeex4-all-9b.Q4_0.gguf
5.46 GB
- Xet hash:
- 3faf08a9e9bad798232a3a73174c08321bc50ef0e5f095c725fa4d46f3c72a5f
- Size of remote file:
- 5.46 GB
- SHA256:
- 10a36e33cc7cf1fa869d5accb6a5da9e75e91e5997fe1167ff7eebced3431ac2
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