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| 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 |
<|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.1import torch
2from transformers import pipeline
3
4chat = [
5 {"role": "user", "content": "<Your code instruction here>"}
6]
7
8generator = pipeline(
9 model="SenseLLM/ReflectionCoder-CL-34B",
10 task="text-generation",
11 torch_dtype=torch.bfloat16,
12 device_map="auto",
13)
14
15result = generator(chat, max_length=128, num_return_sequences=1)
16
17print(result)@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}
}