Instructions to use zai-org/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 zai-org/codegeex4-all-9b-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="zai-org/codegeex4-all-9b-GGUF", filename="codegeex4-all-9b-IQ2_M.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use zai-org/codegeex4-all-9b-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf zai-org/codegeex4-all-9b-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf zai-org/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-server -hf zai-org/codegeex4-all-9b-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf zai-org/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 zai-org/codegeex4-all-9b-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf zai-org/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 zai-org/codegeex4-all-9b-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf zai-org/codegeex4-all-9b-GGUF:Q4_K_M
Use Docker
docker model run hf.co/zai-org/codegeex4-all-9b-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use zai-org/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 "zai-org/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": "zai-org/codegeex4-all-9b-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/zai-org/codegeex4-all-9b-GGUF:Q4_K_M
- Ollama
How to use zai-org/codegeex4-all-9b-GGUF with Ollama:
ollama run hf.co/zai-org/codegeex4-all-9b-GGUF:Q4_K_M
- Unsloth Studio new
How to use zai-org/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 zai-org/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 zai-org/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 zai-org/codegeex4-all-9b-GGUF to start chatting
- Docker Model Runner
How to use zai-org/codegeex4-all-9b-GGUF with Docker Model Runner:
docker model run hf.co/zai-org/codegeex4-all-9b-GGUF:Q4_K_M
- Lemonade
How to use zai-org/codegeex4-all-9b-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull zai-org/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
CodeGeeX4: Open Multilingual Code Generation Model
!!! This is the GGUF version of CodeGeeX4, the original version can be found here. !!!
We introduce CodeGeeX4-ALL-9B, the open-source version of the latest CodeGeeX4 model series. It is a multilingual code generation model continually trained on the GLM-4-9B, significantly enhancing its code generation capabilities. Using a single CodeGeeX4-ALL-9B model, it can support comprehensive functions such as code completion and generation, code interpreter, web search, function call, repository-level code Q&A, covering various scenarios of software development. CodeGeeX4-ALL-9B has achieved highly competitive performance on public benchmarks, such as BigCodeBench and NaturalCodeBench. It is currently the most powerful code generation model with less than 10B parameters, even surpassing much larger general-purpose models, achieving the best balance in terms of inference speed and model performance.
Get Started
Download model weights:
huggingface-cli download THUDM/codegeex4-all-9b-GGUF
Use the latest llama.cpp to launch codegeex4-all-9b-GGUF
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake . -B build
cmake --build build --config Release
build/bin/llama-cli -m Your_Model_Path -p "Your_Input"
Please make sure the prompt is under the following format:
f"<|system|>\n{system_prompt}\n<|user|>\n{prompt}\n<|assistant|>\n"
Default system_prompt:
你是一位智能编程助手,你叫CodeGeeX。你会为用户回答关于编程、代码、计算机方面的任何问题,并提供格式规范、可以执行、准确安全的代码,并在必要时提供详细的解释。
The English version:
You are an intelligent programming assistant named CodeGeeX. You will answer any questions users have about programming, coding, and computers, and provide code that is formatted correctly.
Evaluation
| Model | Seq Length | HumanEval | MBPP | NCB | LCB | HumanEvalFIM | CRUXEval-O |
|---|---|---|---|---|---|---|---|
| Llama3-70B-intruct | 8K | 77.4 | 82.3 | 37.0 | 27.4 | - | - |
| DeepSeek Coder 33B Instruct | 16K | 81.1 | 80.4 | 39.3 | 29.3 | 78.2 | 49.9 |
| Codestral-22B | 32K | 81.1 | 78.2 | 46.0 | 35.3 | 91.6 | 51.3 |
| CodeGeeX4-All-9B | 128K | 82.3 | 75.7 | 40.4 | 28.5 | 85.0 | 47.1 |
License
The model weights are licensed under the following License.
Citation
If you find our work helpful, please feel free to cite the following paper:
@inproceedings{zheng2023codegeex,
title={CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X},
author={Qinkai Zheng and Xiao Xia and Xu Zou and Yuxiao Dong and Shan Wang and Yufei Xue and Zihan Wang and Lei Shen and Andi Wang and Yang Li and Teng Su and Zhilin Yang and Jie Tang},
booktitle={Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages={5673--5684},
year={2023}
}
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