--- license: apache-2.0 datasets: - SenseLLM/ReflectionSeq-GPT - SenseLLM/ReflectionSeq-DS language: - en --- **Exllamav2** quant (**exl2** / **2.2 bpw**) made with ExLlamaV2 v0.1.1 Other EXL2 quants: | **Quant** | **Model Size** | **lm_head** | | ----- | ---------- | ------- | |
**[2.2](https://huggingface.co/Zoyd/SenseLLM_ReflectionCoder-DS-6.7B-2_2bpw_exl2)**
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2055 MB
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6
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**[2.5](https://huggingface.co/Zoyd/SenseLLM_ReflectionCoder-DS-6.7B-2_5bpw_exl2)**
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2276 MB
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6
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**[3.0](https://huggingface.co/Zoyd/SenseLLM_ReflectionCoder-DS-6.7B-3_0bpw_exl2)**
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2665 MB
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6
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**[3.5](https://huggingface.co/Zoyd/SenseLLM_ReflectionCoder-DS-6.7B-3_5bpw_exl2)**
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3051 MB
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6
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**[3.75](https://huggingface.co/Zoyd/SenseLLM_ReflectionCoder-DS-6.7B-3_75bpw_exl2)**
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3245 MB
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6
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**[4.0](https://huggingface.co/Zoyd/SenseLLM_ReflectionCoder-DS-6.7B-4_0bpw_exl2)**
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3437 MB
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6
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**[4.25](https://huggingface.co/Zoyd/SenseLLM_ReflectionCoder-DS-6.7B-4_25bpw_exl2)**
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3630 MB
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6
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**[5.0](https://huggingface.co/Zoyd/SenseLLM_ReflectionCoder-DS-6.7B-5_0bpw_exl2)**
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4208 MB
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6
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**[6.0](https://huggingface.co/Zoyd/SenseLLM_ReflectionCoder-DS-6.7B-6_0bpw_exl2)**
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5000 MB
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8
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**[6.5](https://huggingface.co/Zoyd/SenseLLM_ReflectionCoder-DS-6.7B-6_5bpw_exl2)**
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5388 MB
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8
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**[8.0](https://huggingface.co/Zoyd/SenseLLM_ReflectionCoder-DS-6.7B-8_0bpw_exl2)**
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6232 MB
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8
| ## 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! ![](method.png)
## Models | Model | Checkpoint | Size | HumanEval (+) | MBPP (+) | License| |:-------|:------------|:------|:---------------|:----------|:--------| | ReflectionCoder-CL-7B | 🤗 [HF Link](https://huggingface.co/SenseLLM/ReflectionCoder-CL-7B) | 7B | 75.0 (68.9) | 72.2 (61.4) | [Llama2](https://ai.meta.com/llama/license/) | | ReflectionCoder-CL-34B | 🤗 [HF Link](https://huggingface.co/SenseLLM/ReflectionCoder-CL-34B) | 34B | 70.7 (66.5) | 68.4 (56.6) | [Llama2](https://ai.meta.com/llama/license/) | | ReflectionCoder-DS-6.7B | 🤗 [HF Link](https://huggingface.co/SenseLLM/ReflectionCoder-DS-6.7B) | 6.7B | 80.5 (74.4) | 81.5 (69.6) | [DeepSeek](https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL) | | ReflectionCoder-DS-33B | 🤗 [HF Link](https://huggingface.co/SenseLLM/ReflectionCoder-DS-33B) | 33B | 82.9 (76.8) | 84.1 (72.0) | [DeepSeek](https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL) | ## Datasets | Dataset | Link | License | |:-------------------|:----------------|:----------------------------------------------| | ReflectionSeq-GPT | 🤗 [HF Link](https://huggingface.co/datasets/SenseLLM/ReflectionSeq-GPT) | [License](LICENSE) | | ReflectionSeq-DS | 🤗 [HF Link](https://huggingface.co/datasets/SenseLLM/ReflectionSeq-DS) | [License](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.*, ``<|user|>``, ``<|assistant|>``, and ``<|endofmessage|>``. Furthermore, we use two special tokens to wrap the content of different blocks, *i.e.*, ``<|text|>`` and ``<|endofblock|>``. You can use the following template to prompt our ReflectionCoder. ```python <|user|><|text|> Your Instruction <|endofblock|><|endofmessage|><|assistant|> ``` #### Inference Code Please refer to our [GitHub Repo](https://github.com/SenseLLM/ReflectionCoder) for more technical details. ## Citation If you find this repo useful for your research, please kindly cite our paper: ``` @misc{ren2024reflectioncoder, title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation}, author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li}, year={2024}, eprint={2405.17057}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## Acknowledgments We thank the following amazing projects that truly inspired us: - [CodeLlama](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/) - [DeepSeek-Coder](https://github.com/deepseek-ai/DeepSeek-Coder) - [WizardCoder](https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder) - [Evol-CodeAlpaca-v1](https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1) - [MagiCoder](https://github.com/ise-uiuc/magicoder/tree/main) - [EvalPlus](https://github.com/evalplus/evalplus) - [OpenCoderInterpreter](https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/tree/main)