Instructions to use ZeroWw/codegeex4-all-9b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use ZeroWw/codegeex4-all-9b-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="ZeroWw/codegeex4-all-9b-GGUF", filename="codegeex4-all-9b.f16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ZeroWw/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 ZeroWw/codegeex4-all-9b-GGUF:F16 # Run inference directly in the terminal: llama cli -hf ZeroWw/codegeex4-all-9b-GGUF:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ZeroWw/codegeex4-all-9b-GGUF:F16 # Run inference directly in the terminal: llama cli -hf ZeroWw/codegeex4-all-9b-GGUF:F16
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 ZeroWw/codegeex4-all-9b-GGUF:F16 # Run inference directly in the terminal: ./llama-cli -hf ZeroWw/codegeex4-all-9b-GGUF:F16
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 ZeroWw/codegeex4-all-9b-GGUF:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf ZeroWw/codegeex4-all-9b-GGUF:F16
Use Docker
docker model run hf.co/ZeroWw/codegeex4-all-9b-GGUF:F16
- LM Studio
- Jan
- Ollama
How to use ZeroWw/codegeex4-all-9b-GGUF with Ollama:
ollama run hf.co/ZeroWw/codegeex4-all-9b-GGUF:F16
- Unsloth Studio
How to use ZeroWw/codegeex4-all-9b-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 ZeroWw/codegeex4-all-9b-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 ZeroWw/codegeex4-all-9b-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ZeroWw/codegeex4-all-9b-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use ZeroWw/codegeex4-all-9b-GGUF with Docker Model Runner:
docker model run hf.co/ZeroWw/codegeex4-all-9b-GGUF:F16
- Lemonade
How to use ZeroWw/codegeex4-all-9b-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ZeroWw/codegeex4-all-9b-GGUF:F16
Run and chat with the model
lemonade run user.codegeex4-all-9b-GGUF-F16
List all available models
lemonade list
Upload folder using huggingface_hub
Browse files- .gitattributes +5 -0
- README.md +16 -0
- codegeex4-all-9b.f16.gguf +3 -0
- codegeex4-all-9b.q5_k.gguf +3 -0
- codegeex4-all-9b.q6_k.gguf +3 -0
- codegeex4-all-9b.q8_0.gguf +3 -0
- codegeex4-all-9b.q8_p.gguf +3 -0
.gitattributes
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---
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license: mit
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language:
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- en
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---
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My own (ZeroWw) quantizations.
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output and embed tensors quantized to f16.
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all other tensors quantized to q5_k or q6_k.
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Result:
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both f16.q6 and f16.q5 are smaller than q8_0 standard quantization
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and they perform as well as the pure f16.
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Updated on: Sat Jul 13, 09:06:20
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size 18806965376
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size 8690828416
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size 11158914176
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