|
--- |
|
frameworks: |
|
- Pytorch |
|
license: other |
|
tasks: |
|
- text-embedding |
|
--- |
|
|
|
## CodeFuse-CGE-Large |
|
<p align="center"> |
|
<img src="https://modelscope.cn/api/v1/models/codefuse-ai/CodeFuse-QWen-14B/repo?Revision=master&FilePath=LOGO.jpg&View=true" width="800"/> |
|
<p> |
|
|
|
## Model Description |
|
CodeFuse-CGE-Large is the Large version of the CodeFuse-CGE family which is fine-tuned based on CodeQwen1.5-7B. CodeFuse-CGE-Large is distinguish on text2code task for it's powerful ability of capturing the semantic relationship between code and text. |
|
|
|
This model has the following notable features: |
|
● Instruction-tuning is enabled for both query and code snippet sides. |
|
● The model obtains sentence-level and code-level representations through a layer of cross-attention computation module. |
|
● The model has a smaller dimensional size without significant degradation in performance. |
|
|
|
Model Configuration |
|
Model Size: 7B |
|
Embedding Dimension: 1024 |
|
Hidden Layers: 32 |
|
Max Input Tokens: 1024 |
|
|
|
|
|
Requirements |
|
``` |
|
flash_attn==2.4.2 |
|
torch==2.1.0 |
|
accelerate==0.28.0 |
|
transformers==4.39.2 |
|
vllm=0.5.3 |
|
``` |
|
|
|
|
|
## How to Use |
|
### transformers |
|
``` |
|
from transformers import AutoTokenizer, AutoModel |
|
|
|
model_name_or_path = "CodeFuse-CGE-Large" |
|
model = AutoModel.from_pretrained(model_name_or_path, trust_remote_code=True) |
|
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True, truncation_side='right', padding_side='right') |
|
model = model.to(torch.bfloat16) |
|
|
|
if torch.cuda.is_available(): |
|
device = 'cuda' |
|
else: |
|
device = 'cpu' |
|
model.to(device) |
|
|
|
prefix_dict = {'python':{'query':'Retrieve the Python code that solves the following query:', 'passage':'Python code:'}, |
|
'java':{'query':'Retrieve the Java code that solves the following query:', 'passage':'Java code:'}, |
|
'go':{'query':'Retrieve the Go code that solves the following query:', 'passage':'Go code:'}, |
|
'c++':{'query':'Retrieve the C++ code that solves the following query:', 'passage':'C++ code:'}, |
|
'javascript':{'query':'Retrieve the Javascript code that solves the following query:', 'passage':'Javascript code:'}, |
|
'php':{'query':'Retrieve the PHP code that solves the following query:', 'passage':'PHP code:'}, |
|
'ruby':{'query':'Retrieve the Ruby code that solves the following query:', 'passage':'Ruby code:'}, |
|
'default':{'query':'Retrieve the code that solves the following query:', 'passage':'Code:'} |
|
} |
|
|
|
text = ["Writes a Boolean to the stream.", |
|
"def writeBoolean(self, n): t = TYPE_BOOL_TRUE if n is False: t = TYPE_BOOL_FALSE self.stream.write(t)"] |
|
text[0] += prefix_dict['python']['query'] |
|
text[0] += prefix_dict['python']['passage'] |
|
embed = model.encode(tokenizer, text) |
|
score = embed[0] @ embed[1].T |
|
print("score", score) |
|
|
|
``` |
|
|
|
## Benchmark the Performance |
|
We use MRR metric to evaluate the ability on text2code retrieval tasks: AdvTest, CosQA, CSN |
|
|
|
|
|
 |
|
|
|
## Acknowledgement |
|
Thanks to the authors of open-sourced datasets, including CSN, Adv, CoSQA. |
|
|
|
## License |
|
Since CodeFuse-CGE-Large is fine-tuned based on CodeQwen1.5-7B model, our usage license follows the same terms as that of CodeQwen1.5-7B model. |
|
|
|
## 加入我们 |
|
我们是平台技术事业群AI Native团队，负责蚂蚁蚂蚁集团平台工程的智能化，团队成立3年多以来，支持了蚂蚁集团云计算基础设施智能化运维的升级改造。团队的Mission是，通过世界级的技术创新和影响，构建有广泛用户的算法服务和平台，支撑内外部产品和业务落地。团队秉承创新基因，在支撑业务落地的同时，推动技术影响。3年以来在ICLR、NeurIPS、KDD、ACL等顶会发表论文20余篇，创新业务结果获得两次蚂蚁技术最高奖T-Star，1次蚂蚁集团最高奖SuperMA。开源项目CodeFuse获得4K点赞(2024年2月)，Huggingface和modelscope上模型累积下载量超过150万次。 |
|
|
|
我们正在寻找行业中的佼佼者加入我们的团队！如果您希望在一个充满活力、创新和卓越文化的环境中发展您的职业生涯，欢迎您查看我们的社招&校招机会，加入我们，一起创造下一个行业里程碑。 |
|
|
|
校招：https://hrrecommend.antgroup.com/guide.html?code=8uoP5mlus5DqQYbE_EnqcE2FD5JZH21MwvMUIb9mb6X3osXPuBraG54SyM8GLn_7 |
|
|
|
社招：https://talent.antgroup.com/off-campus-position?positionId=1933830 |
