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README.md
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Compared to previous versions, the usage recommendations for MyAwesomeModel have the following changes:
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1. System prompt is supported.
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2. It is not required to add special tokens at the beginning of the output to force the model into a specific thinking pattern.
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The model architecture of MyAwesomeModel-Small is identical to its base model, but it shares the same tokenizer configuration as the main MyAwesomeModel. This model can be run in the same manner as its base model.
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### System Prompt
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We recommend using the following system prompt with a specific date.
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```
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You are MyAwesomeModel, a helpful AI assistant.
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Today is {current date}.
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```
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For example,
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```
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You are MyAwesomeModel, a helpful AI assistant.
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Today is May 28, 2025, Monday.
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```
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### Temperature
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We recommend setting the temperature parameter $T_{model}$ to 0.6.
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### Prompts for File Uploading and Web Search
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For file uploading, please follow the template to create prompts, where {file_name}, {file_content} and {question} are arguments.
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```
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file_template = \
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"""[file name]: {file_name}
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[file content begin]
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{file_content}
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[file content end]
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{question}"""
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```
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For web search enhanced generation, we recommend the following prompt template where {search_results}, {cur_date}, and {question} are arguments.
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```
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search_answer_en_template = \
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'''# The following contents are the search results related to the user's message:
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{search_results}
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In the search results I provide to you, each result is formatted as [webpage X begin]...[webpage X end], where X represents the numerical index of each article. Please cite the context at the end of the relevant sentence when appropriate. Use the citation format [citation:X] in the corresponding part of your answer. If a sentence is derived from multiple contexts, list all relevant citation numbers, such as [citation:3][citation:5]. Be sure not to cluster all citations at the end; instead, include them in the corresponding parts of the answer.
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When responding, please keep the following points in mind:
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- Today is {cur_date}.
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- Not all content in the search results is closely related to the user's question. You need to evaluate and filter the search results based on the question.
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- For listing-type questions (e.g., listing all flight information), try to limit the answer to 10 key points and inform the user that they can refer to the search sources for complete information. Prioritize providing the most complete and relevant items in the list. Avoid mentioning content not provided in the search results unless necessary.
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- For creative tasks (e.g., writing an essay), ensure that references are cited within the body of the text, such as [citation:3][citation:5], rather than only at the end of the text. You need to interpret and summarize the user's requirements, choose an appropriate format, fully utilize the search results, extract key information, and generate an answer that is insightful, creative, and professional. Extend the length of your response as much as possible, addressing each point in detail and from multiple perspectives, ensuring the content is rich and thorough.
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- If the response is lengthy, structure it well and summarize it in paragraphs. If a point-by-point format is needed, try to limit it to 5 points and merge related content.
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- For objective Q&A, if the answer is very brief, you may add one or two related sentences to enrich the content.
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- Choose an appropriate and visually appealing format for your response based on the user's requirements and the content of the answer, ensuring strong readability.
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- Your answer should synthesize information from multiple relevant webpages and avoid repeatedly citing the same webpage.
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- Unless the user requests otherwise, your response should be in the same language as the user's question.
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# The user's message is:
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{question}'''
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```
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## 5. License
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This code repository is licensed under the [MIT License](LICENSE). The use of MyAwesomeModel models is also subject to the [MIT License](LICENSE). The model series supports commercial use and distillation.
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## 6. Contact
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If you have any questions, please raise an issue on our GitHub repository or contact us at contact@MyAwesomeModel.ai.
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```
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# Annoy: This should be a paper Title
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<p align="left">
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📑 <a href="https://huggingface.co/papers/xxxx.xxxxx" target="_blank">Paper</a>    |    🌐 <a href="https://specx.github.io/" target="_blank">Project Page</a>    |    💾 <a href="https://huggingface.co/collections/blmq/specx-67a978e28fd926b56a4f55a2" target="_blank">Released Resources</a>    |    📦 <a href="https://github.com/Joe-Liu-testuser/Annoy" target="_blank">Repo</a>
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This is the resource page of the our resources collection on Huggingface, we highlight your currect position with a blue block.
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**Dataset**
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<table>
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<tr>
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<th>Dataset</th>
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<th>Link</th>
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</tr>
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<tr>
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<td>Annoy-PythonEdu-Rs</td>
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<td style="background-color: #e6f3ff; text-align: center; vertical-align: middle;">
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<a href="https://huggingface.co/datasets/blmq/Annoy-PyEdu-Rs">🤗</a>
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</td>
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</tr>
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</table>
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Please also check the raw data after our processing if you are interested: [blmq/Annoy-PyEdu-Rs-Raw](https://huggingface.co/datasets/blmq/Annoy-PyEdu-Rs-Raw).
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**Models**
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<table>
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<tr>
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<th rowspan="2">Base Model / Training</th>
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<th colspan="2">Annoy</th>
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<th colspan="2">Annoy++</th>
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</tr>
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<tr>
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<th>Stage 1</th>
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<th>Stage 2</th>
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<th>Stage 1</th>
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<th>Stage 2</th>
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</tr>
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<tr>
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<td>Qwen 2.5 7B Coder</td>
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<td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/blmq/qwen2.5-7b-coder_spec_stage1">🤗</a></td>
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<td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/blmq/qwen2.5-7b-coder_spec">🤗</a></td>
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<td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/blmq/qwen2.5-7b-coder_spec_pp_stage1">🤗</a></td>
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<td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/blmq/qwen2.5-7b-coder_spec_pp">🤗</a></td>
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</tr>
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<tr>
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<td>LLaMA 3.1 8B</td>
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<td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/blmq/llama3.1-8b_spec_stage1">🤗</a></td>
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<td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/blmq/llama3.1-8b_spec">🤗</a></td>
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<td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/blmq/llama3.1-8b_spec_pp_stage1">🤗</a></td>
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<td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/blmq/llama3.1-8b_spec_pp">🤗</a></td>
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</tr>
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<tr>
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<td>DeepSeek v2 Lite Coder</td>
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<td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/blmq/dsv2-lite-coder_spec_stage1">🤗</a></td>
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<td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/blmq/dsv2-lite-coder_spec">🤗</a></td>
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<td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/blmq/dsv2-lite-coder_spec_pp_stage1">🤗</a></td>
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<td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/blmq/dsv2-lite-coder_spec_pp">🤗</a></td>
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</tr>
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</table>
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**Introduction**
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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.
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*Due to our collaborators' compliance requirements, we only release the PythonEdu-Rs subset (this page) of full dataset.
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**License**
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The license for this dataset is ODC-BY (Open Data Commons Open Database License v1.0 with Attribution).
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