---
title: codegeex4-all-9b
canonical_url: "https://www.modelscope.cn/models/maple77/codegeex4-all-9b"
md_url: "https://www.modelscope.cn/models/maple77/codegeex4-all-9b.md"
repository: maple77/codegeex4-all-9b
last_updated: 2024-07-06
license: other
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - chatglm
architectures:
  - ChatGLMModel
parameters: 9.4B
tensor_type:
  - BF16
library_name:
  - pytorch
  - transformer
  - safetensors
frameworks:
  - Pytorch
language:
  - zh
  - en
inference_backends:
  - "deploy_task text/emb"
  - "lmdeploy 0.9.1"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 44
stars: 1
tags:
  - glm
  - codegeex
  - thudm
---

# codegeex4-all-9b

> codegeex4-all-9b - maple77 在 ModelScope 开源的模型。CodeGeeX4: Open Multilingual Code Generation Model

maple77/codegeex4-all-9b 是 ModelScope 魔搭社区上的 9.4B 参数text-generation模型，采用 other 许可，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: maple77/codegeex4-all-9b
- **License**: other
- **Tasks**: text-generation
- **Parameters**: 9.4B
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Tags**: glm, codegeex, thudm
- **Downloads**: 44
- **Stars**: 1
- **Last updated**: 2024-07-06

Source: https://www.modelscope.cn/models/maple77/codegeex4-all-9b

---

# CodeGeeX4: Open Multilingual Code Generation Model

[中文](./README_zh.md)

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](https://github.com/THUDM/GLM-4), 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](https://huggingface.co/datasets/bigcode/bigcodebench) and [NaturalCodeBench](https://github.com/THUDM/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

Use `4.39.0<=transformers<=4.40.2` to quickly launch [codegeex4-all-9b](https://huggingface.co/THUDM/codegeex2-6b)：

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = AutoTokenizer.from_pretrained("THUDM/codegeex4-all-9b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    "THUDM/codegeex4-all-9b",
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
    trust_remote_code=True
).to(device).eval()
inputs = tokenizer.apply_chat_template([{"role": "user", "content": "write a quick sort"}], add_generation_prompt=True, tokenize=True, return_tensors="pt", return_dict=True ).to(device)
with torch.no_grad():
    outputs = model.generate(**inputs)
    outputs = outputs[:, inputs['input_ids'].shape[1]:]
    print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```


## License

The model weights are licensed under the following [License](./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 Evaluations 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={KDD},
      year={2023}
}
```