JingyaoLi
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MoTCoder-7B-v1.5

Text Generation

text-generation-inference

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 • 📃 <a href="https://arxiv.org/abs/2312.15960" target="_blank">Paper</a> <br>
 </p>
-[![PWC](https://img.shields.io/endpoint?url=https%3A%2F%2Fpaperswithcode.com%2Fbadge%2Fmotcoder-elevating-large-language-models-with%2Fcode-generation-on-apps%3Fmetric%3DIntroductory%2520Pass%25401)](https://paperswithcode.com/sota/code-generation-on-apps?metric=Introductory%20Pass%401/motcoder-elevating-large-language-models-with)
-[![PWC](https://img.shields.io/endpoint?url=https%3A%2F%2Fpaperswithcode.com%2Fbadge%2Fmotcoder-elevating-large-language-models-with%2Fcode-generation-on-codecontests%3Fmetric%3DTest%2520Set%2520pass%25401)](https://paperswithcode.com/sota/code-generation-on-codecontests?metric=Test%20Set%20pass%401)
 Large Language Models (LLMs) have showcased impressive capabilities in handling straightforward programming tasks.
 However, their performance tends to falter when confronted with more challenging programming problems.
 We observe that conventional models often generate solutions as monolithic code blocks, restricting their effectiveness in tackling intricate questions.

 • 📃 <a href="https://arxiv.org/abs/2312.15960" target="_blank">Paper</a> <br>
 </p>
 Large Language Models (LLMs) have showcased impressive capabilities in handling straightforward programming tasks.
 However, their performance tends to falter when confronted with more challenging programming problems.
 We observe that conventional models often generate solutions as monolithic code blocks, restricting their effectiveness in tackling intricate questions.