Instructions to use zai-org/codegeex4-all-9b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zai-org/codegeex4-all-9b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zai-org/codegeex4-all-9b", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("zai-org/codegeex4-all-9b", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use zai-org/codegeex4-all-9b 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" # 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", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/zai-org/codegeex4-all-9b
- SGLang
How to use zai-org/codegeex4-all-9b with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "zai-org/codegeex4-all-9b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zai-org/codegeex4-all-9b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "zai-org/codegeex4-all-9b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zai-org/codegeex4-all-9b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use zai-org/codegeex4-all-9b with Docker Model Runner:
docker model run hf.co/zai-org/codegeex4-all-9b
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[CodeGeeX4 GitHub](https://github.com/THUDM/CodeGeeX4)
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我们推出了 CodeGeeX4-ALL-9B,这是最新的 CodeGeeX4 系列模型的开源版本。该模型是在 [GLM-4-9B](https://github.com/THUDM/GLM-4) 基础上持续训练的多语言代码生成模型,显著提升了代码生成能力。使用单个 CodeGeeX4-ALL-9B 模型,可以支持代码补全与生成、代码解释、联网搜索、函数调用、仓库级代码问答等多种功能,覆盖了软件开发的各个场景。CodeGeeX4-ALL-9B 在 [BigCodeBench](https://huggingface.co/
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## 快速开始
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[CodeGeeX4 GitHub](https://github.com/THUDM/CodeGeeX4)
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我们推出了 CodeGeeX4-ALL-9B,这是最新的 CodeGeeX4 系列模型的开源版本。该模型是在 [GLM-4-9B](https://github.com/THUDM/GLM-4) 基础上持续训练的多语言代码生成模型,显著提升了代码生成能力。使用单个 CodeGeeX4-ALL-9B 模型,可以支持代码补全与生成、代码解释、联网搜索、函数调用、仓库级代码问答等多种功能,覆盖了软件开发的各个场景。CodeGeeX4-ALL-9B 在 [BigCodeBench](https://huggingface.co/spaces/bigcode/bigcodebench-leaderboard) 和 [NaturalCodeBench](https://github.com/THUDM/NaturalCodeBench) 等公开基准测试中取得了极具竞争力的表现。它是目前参数量少于 100 亿的最强代码生成模型,甚至超越了更大的通用模型,在推理速度和模型性能方面达到了最佳平衡。
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## 快速开始
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