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https://github.com/THUDM/CodeGeeX4
CodeGeeX4-ALL-9B, a versatile model for all AI software development scenarios, including code completion, code interpreter, web search, function calling, repository-level Q&A and much more.
https://github.com/THUDM/CodeGeeX4
Last synced: 29 days ago
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CodeGeeX4-ALL-9B, a versatile model for all AI software development scenarios, including code completion, code interpreter, web search, function calling, repository-level Q&A and much more.
- Host: GitHub
- URL: https://github.com/THUDM/CodeGeeX4
- Owner: THUDM
- License: apache-2.0
- Created: 2024-07-03T11:01:55.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-08-25T14:26:16.000Z (4 months ago)
- Last Synced: 2024-11-14T06:31:27.906Z (29 days ago)
- Language: Python
- Homepage: https://codegeex.cn
- Size: 14.6 MB
- Stars: 1,445
- Watchers: 17
- Forks: 111
- Open Issues: 43
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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- StarryDivineSky - THUDM/CodeGeeX4 - ALL-9B 是一个开源的多语言代码生成模型，基于 GLM-4-9B 训练，支持代码补全、代码解释、网页搜索、函数调用、代码库级问答等多种功能。该模型在 BigCodeBench 和 NaturalCodeBench 等公开基准测试中取得了优异的成绩，是参数量小于 100 亿的代码生成模型中性能最强的，在推理速度和模型性能之间取得了最佳平衡。用户可以通过 Ollama、Huggingface transformers 等方式使用 CodeGeeX4-ALL-9B 模型。 (A01_文本生成_文本对话 / 大语言对话模型及数据)
README
🏠 Homepage｜🛠 Extensions VS Code, Jetbrains｜🤗 HF Repo | 🪧 HF DEMO
[English](./README.md) | [中文](./README_zh.md) | [日本語](./README_ja.md)
# CodeGeeX4: Open Multilingual Code Generation Model
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.
## Model List
| Model | Type | Seq Length | Download |
|-------------------|------|------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| codegeex4-all-9b | Chat | 128K | [🤗 Huggingface](https://huggingface.co/THUDM/codegeex4-all-9b) [🤖 ModelScope](https://modelscope.cn/models/ZhipuAI/codegeex4-all-9b) [🟣 WiseModel](https://wisemodel.cn/models/ZhipuAI/codegeex4-all-9b) |
## Get Started
### Ollama
CodeGeeX4 is now available on [Ollama](https://ollama.com/library/codegeex4)!
Please install [Ollama 0.2](https://github.com/ollama/ollama/releases/tag/v0.2.0) or later and run the following command:
```bash
ollama run codegeex4
```
To connect the local model to our [VS Code](https://marketplace.visualstudio.com/items?itemName=aminer.codegeex) / [Jetbrains](https://plugins.jetbrains.com/plugin/20587-codegeex) extensions, please check [Local Mode Guideline](./guides/Local_mode_guideline.md).
### Huggingface transformers
Use `4.39.0<=transformers<=4.40.2` to quickly launch [codegeex4-all-9b](https://huggingface.co/THUDM/codegeex4-all-9b)：
```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))
```
### vLLM
Use `vllm==0.5.1` to quickly launch [codegeex4-all-9b](https://huggingface.co/THUDM/codegeex4-all-9b):
```
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
# CodeGeeX4-ALL-9B
# max_model_len, tp_size = 1048576, 4
# If OOM，please reduce max_model_len，or increase tp_size
max_model_len, tp_size = 131072, 1
model_name = "codegeex4-all-9b"
prompt = [{"role": "user", "content": "Hello"}]
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
llm = LLM(
model=model_name,
tensor_parallel_size=tp_size,
max_model_len=max_model_len,
trust_remote_code=True,
enforce_eager=True,
# If OOM，try using follong parameters
# enable_chunked_prefill=True,
# max_num_batched_tokens=8192
)
stop_token_ids = [151329, 151336, 151338]
sampling_params = SamplingParams(temperature=0.95, max_tokens=1024, stop_token_ids=stop_token_ids)
inputs = tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=True)
outputs = llm.generate(prompts=inputs, sampling_params=sampling_params)
print(outputs[0].outputs[0].text)
```
Set up OpenAI Compatible Server via vllm, detailed please check [OpenAI Compatible Server](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html)
```
python -m vllm.entrypoints.openai.api_server \
--model THUDM/codegeex4-all-9b \
--trust_remote_code
```
### Rust-candle
Codegeex4 now suport Candle framwork [Repo](https://github.com/huggingface/candle/blob/main/candle-examples/examples/codegeex4-9b/README.org)
#### Cli
Use Rust to launch [codegeex4-all-9b](https://huggingface.co/THUDM/codegeex4-all-9b):
``` shell
cd candle_demo
cargo build -p codegeex4-cli --release --features cuda # for Cuda
cargo build -p codegeex4-cli --release # for cpu
./target/release/codegeex4-cli --sample-len 512
```
## Tutorials
CodeGeeX4-ALL-9B provides three user guides to help users quickly understand and use the model:
1. **[System Prompt Guideline](./guides/System_prompt_guideline.md)**: This guide introduces how to use system prompts in CodeGeeX4-ALL-9B, including the VSCode extension official system prompt, customized system prompts, and some tips for maintaining multi-turn dialogue history.
2. **[Infilling Guideline](./guides/Infilling_guideline.md)**: This guide explains the VSCode extension official infilling format, covering general infilling, cross-file infilling, and generating a new file in a repository.
3. **[Repository Tasks Guideline](./guides/Repository_tasks_guideline.md)**: This guide demonstrates how to use repository tasks in CodeGeeX4-ALL-9B, including QA tasks at the repository level and how to trigger the aicommiter capability of CodeGeeX4-ALL-9B to perform deletions, additions, and changes to files at the repository level.
4. **[Local Mode Guideline](./guides/Local_mode_guideline.md)**：This guide introduces how to deploy CodeGeeX4-ALL-9B locally and connect it to Visual Studio Code / Jetbrains extensions.
These guides aim to provide a comprehensive understanding and facilitate efficient use of the model.
## Evaluation
CodeGeeX4-ALL-9B is ranked as the most powerful model under 10 billion parameters, even surpassing general models several times its size, achieving the best balance between inference performance and model effectiveness.
| **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 |
CodeGeeX4-ALL-9B scored `48.9` and `40.4` for the `complete` and `instruct` tasks of BigCodeBench, which are the highest scores among models with less than 20 billion parameters.
In CRUXEval, a benchmark for testing code reasoning, understanding, and execution capabilities, CodeGeeX4-ALL-9B presented remarkable results with its COT (chain-of-thought) abilities. From easy code generation tasks in HumanEval and MBPP, to very challenging tasks in NaturalCodeBench, CodeGeeX4-ALL-9B also achieved outstanding performance at its scale. It is currently the only code model that supports Function Call capabilities and even achieves a better execution success rate than GPT-4.
Furthermore, in the "Code Needle In A Haystack" (NIAH) evaluation, the CodeGeeX4-ALL-9B model demonstrated its ability to retrieve code within contexts up to 128K, achieving a 100% retrieval accuracy in all python scripts.
Details of the evaluation results can be found in the **[Evaluation](./metric/README.md)**.
## License
The code in this repository is open source under the [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) license. The model weights are licensed under the [Model License](MODEL_LICENSE). CodeGeeX4-9B weights are open for academic research. For users who wish to use the models for commercial purposes, please fill in the [registration form](https://bigmodel.cn/mla/form?mcode=CodeGeeX4-ALL-9B).
## Citation
If you find our work helpful, please feel free to cite the following paper:
```bibtex
@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}
}
```