Tebmer/Awesome-Knowledge-Distillation-of-LLMs

• hightopics#1

  Add 'awesome-list' and 'paper-collection' topics

  原因：

  当前

  alignment, compression, data-augmentation, data-synthesis, feedback, instruction-following, kd, knowledge-distillation, large-language-model, llm, multi-modal, self-distillation, self-training, supervised-finetuning, survey

  复制粘贴的修复

  复制

  awesome-list, paper-collection, alignment, compression, data-augmentation, data-synthesis, feedback, instruction-following, kd, knowledge-distillation, large-language-model, llm, multi-modal, self-distillation, self-training, supervised-finetuning, survey

  待办完成忽略
• highlicense#2

  Add a LICENSE file to the repository

  原因：

  当前

  (no LICENSE file detected — the repo has no recognizable license)

  复制粘贴的修复

  复制

  Create a `LICENSE` file in the repository root containing the text of an appropriate open-source license (e.g., MIT, Apache-2.0, or GPL-3.0) to clarify usage terms.

  待办完成忽略
• mediumhomepage#3

  Set the repository homepage URL

  原因：

  复制粘贴的修复

  复制

  Set the GitHub repository's homepage URL to `https://arxiv.org/abs/2402.13116` (the associated survey paper).

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  How to distill knowledge from large language models to create smaller, efficient versions?

  你：未被推荐
  AI 推荐顺序：

  1. Hugging Face Transformers
  2. 🧗 Accelerate
  3. Optimum
  4. PyTorch Lightning
  5. DeepSpeed
  6. DistilBERT
  7. TensorFlow Lite
  8. ONNX Runtime
  9. OpenVINO

  AI 推荐了 9 个替代方案，却始终没点名 Tebmer/Awesome-Knowledge-Distillation-of-LLMs。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  What strategies can I use to improve smaller models with knowledge from LLMs?

  你：未被推荐
  AI 推荐顺序：

  1. Hugging Face Transformers (huggingface/transformers)
  2. PyTorch (pytorch/pytorch)
  3. TensorFlow (tensorflow/tensorflow)
  4. OpenVINO (openvinotoolkit/openvino)
  5. OpenAI API
  6. Anthropic Claude
  7. LangChain (langchain-ai/langchain)
  8. LlamaIndex (run-llama/llama_index)
  9. PyTorch Lightning (Lightning-AI/lightning)
  10. Scikit-learn (scikit-learn/scikit-learn)
  11. Cohere Embed

  AI 推荐了 11 个替代方案，却始终没点名 Tebmer/Awesome-Knowledge-Distillation-of-LLMs。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  warn

  建议：

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of Tebmer/Awesome-Knowledge-Distillation-of-LLMs?

  pass
  ⚠AI 未点名 Tebmer/Awesome-Knowledge-Distillation-of-LLMs —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts Tebmer/Awesome-Knowledge-Distillation-of-LLMs in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 Tebmer/Awesome-Knowledge-Distillation-of-LLMs

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo Tebmer/Awesome-Knowledge-Distillation-of-LLMs solve, and who is the primary audience?

  pass
  ⚠AI 未点名 Tebmer/Awesome-Knowledge-Distillation-of-LLMs —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？