lovebird-ll/Annoy-PyEdu-Rs

Annoy: This should be a paper Title

<p align="left"> 📑 <a href="https://huggingface.co/papers/xxxx.xxxxx" target="blank">Paper</a> &nbsp&nbsp | &nbsp&nbsp 🌐 <a href="https://specx.github.io/" target="blank">Project Page</a> &nbsp&nbsp | &nbsp&nbsp 💾 <a href="https://huggingface.co/collections/lovebird-ll/specx-67a978e28fd926b56a4f55a2" target="blank">Released Resources</a> &nbsp&nbsp | &nbsp&nbsp 📦 <a href="https://github.com/anmat-d-techroar/Annoy" target="blank">Repo</a>

This is the resource page of the our resources collection on Huggingface, we highlight your currect position with a blue block.

Dataset <table> <tr> <th>Dataset</th> <th>Link</th> </tr> <tr> <td>Annoy-PythonEdu-Rs</td> <td style="background-color: #e6f3ff; text-align: center; vertical-align: middle;"> <a href="https://huggingface.co/datasets/lovebird-ll/Annoy-PyEdu-Rs">🤗</a> </td> </tr> </table> Please also check the raw data after our processing if you are interested: lovebird-ll/Annoy-PyEdu-Rs-Raw.

Models <table> <tr> <th rowspan="2">Base Model / Training</th> <th colspan="2">Annoy</th> <th colspan="2">Annoy++</th> </tr> <tr> <th>Stage 1</th> <th>Stage 2</th> <th>Stage 1</th> <th>Stage 2</th> </tr> <tr> <td>Qwen 2.5 7B Coder</td> <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/qwen2.5-7b-coderspecstage1">🤗</a></td> <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/qwen2.5-7b-coderspec">🤗</a></td> <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/qwen2.5-7b-coderspecppstage1">🤗</a></td> <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/qwen2.5-7b-coderspecpp">🤗</a></td> </tr> <tr> <td>LLaMA 3.1 8B</td> <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/llama3.1-8bspecstage1">🤗</a></td> <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/llama3.1-8bspec">🤗</a></td> <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/llama3.1-8bspecppstage1">🤗</a></td> <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/llama3.1-8bspecpp">🤗</a></td> </tr> <tr> <td>DeepSeek v2 Lite Coder</td> <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/dsv2-lite-coderspecstage1">🤗</a></td> <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/dsv2-lite-coderspec">🤗</a></td> <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/dsv2-lite-coderspecppstage1">🤗</a></td> <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/dsv2-lite-coderspecpp">🤗</a></td> </tr> </table>

Introduction

While having full executable code theoretically allows us to generate reliable execution trajectories as responses, two challenges arise: 1) Obtaining a deterministic reverse function for input prediction is impractical; 2) Automatically constructed trajectories are constrained by pre-designed templates and lack the expressiveness and generalizability of free-form natural language reasoning. Thus, we adopt a fully LLM-based approach for synthesizing all the desired responses using DeepSeek-V2.5, as it has top-tier performance but extremely low cost compared to other advanced LLMs.

*Due to our collaborators' compliance requirements, we only release the PythonEdu-Rs subset (this page) of full dataset.