ReflectionCoder: Learning from Reflection Sequence for Enhanced One off Code Generation 📄 Paper • 🏠 Repo • 🤖 Models • 📚 Datasets Introduction ReflectionCoder is a novel approach that effectively leverages reflection sequences constructed by integrating compiler feedback to improve one off code generation performance. Please refer to our paper and repo for more details! Models Model Checkpoint Size HumanEval (+) MBPP (+) License : : : : : : ReflectionCoder CL 7B 🤗 HF Link 7B 75.0 (68.9) 72.2 (61.4) Llama2 ReflectionCoder CL 34B 🤗 HF Link 34B 70.7 (66.5) 68.4 (56.6) Llama2 ReflectionCoder DS 6.7B 🤗 HF Link 6.7B 80.5 (74.4) 81.5 (69.6) DeepSeek ReflectionCoder DS 33B 🤗 HF Link 33B 82.9 (76.8) 84.1 (72.0) DeepSeek Datasets Dataset Link License : : : ReflectionSeq GPT 🤗 HF Link License ReflectionSeq DS 🤗 HF Link License How to Use Chat Format Following chat templates of most models, we use two special tokens to wrap the message of user and assistant, i.e. , , , and . Furthermore, we use two special tokens to wrap the content of different blocks, i.e. , and . You can use the following template to prompt our ReflectionCoder. Please refer to our GitHub Repo for more technical det…
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