## 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') 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