| ## 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 | |
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