frameworks:
- Pytorch
license: other
tasks:
- text-embedding
CodeFuse-CGE-Large
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。
社招：https://talent.antgroup.com/off-campus-position?positionId=1933830