Annoy-PyEdu-Rs / README.md
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# Annoy: This should be a paper Title
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πŸ“‘ <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 πŸ’Ύ <a href="https://huggingface.co/collections/mm-tool/specx-67a978e28fd926b56a4f55a2" target="_blank">Released Resources</a> &nbsp&nbsp | &nbsp&nbsp πŸ“¦ <a href="https://github.com/mcpllmbench-ops/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/mm-tool/Annoy-PyEdu-Rs">πŸ€—</a>
</td>
</tr>
</table>
Please also check the raw data after our processing if you are interested: [mm-tool/Annoy-PyEdu-Rs-Raw](https://huggingface.co/datasets/mm-tool/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/mm-tool/qwen2.5-7b-coder_spec_stage1">πŸ€—</a></td>
<td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/mm-tool/qwen2.5-7b-coder_spec">πŸ€—</a></td>
<td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/mm-tool/qwen2.5-7b-coder_spec_pp_stage1">πŸ€—</a></td>
<td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/mm-tool/qwen2.5-7b-coder_spec_pp">πŸ€—</a></td>
</tr>
<tr>
<td>LLaMA 3.1 8B</td>
<td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/mm-tool/llama3.1-8b_spec_stage1">πŸ€—</a></td>
<td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/mm-tool/llama3.1-8b_spec">πŸ€—</a></td>
<td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/mm-tool/llama3.1-8b_spec_pp_stage1">πŸ€—</a></td>
<td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/mm-tool/llama3.1-8b_spec_pp">πŸ€—</a></td>
</tr>
<tr>
<td>DeepSeek v2 Lite Coder</td>
<td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/mm-tool/dsv2-lite-coder_spec_stage1">πŸ€—</a></td>
<td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/mm-tool/dsv2-lite-coder_spec">πŸ€—</a></td>
<td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/mm-tool/dsv2-lite-coder_spec_pp_stage1">πŸ€—</a></td>
<td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/mm-tool/dsv2-lite-coder_spec_pp">πŸ€—</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.
**License**
The license for this dataset is CC-BY-4.0.
**License**
The license for this dataset is The Stack v2 License.
**License**
The license for this dataset is Apache 2.0.