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XwinCoder

We are glad to introduce our instruction finetuned code generation models based on CodeLLaMA: XwinCoder. We release model weights and evaluation code.

Repository: https://github.com/Xwin-LM/Xwin-LM/tree/main/Xwin-Coder

Models:

Model ๐Ÿค—hf link HumanEval pass@1 MBPP pass@1 APPS-intro pass@5
XwinCoder-7B link 63.8 57.4 31.5
XwinCoder-13B link 68.8 60.1 35.4
XwinCoder-34B link 74.2 64.8 43.0

Updates

  • ๐Ÿ’ฅ We released XwinCoder-7B, XwinCoder-13B, XwinCoder-34B. Our XwinCoder-34B reached 74.2 on HumanEval and it achieves comparable performance as GPT-3.5-turbo on 6 benchmarks.

  • โ—We support evaluating instruction finetuned models on HumanEval, MBPP, APPS, DS1000 and MT-Bench. See our github repository.

Overview

Chat demo

  • To fully demonstrate our model's coding capabilities in real-world usage scenarios, we have conducted thorough evaluations on several existing mainstream coding capability leaderboards (rather than only on the currently most popular HumanEval).
  • As shown in the radar chart results, our 34B model achieves comparable performance as GPT-3.5-turbo on coding abilities.
  • It is worth mentioning that, to ensure accurate visualization, our radar chart has not been scaled (only translated; MT-Bench score is scaled by 10x to be more comparable with other benchmarks).
  • Multiple-E-avg6 refer to the 6 languages used in CodeLLaMA paper. Results of GPT-4 and GPT-3.5-turbo are conducted by us, more details will be released later.

Demo

We provide a chat demo in our github repository, here are some examples: Chat demo

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