Tebmer/Awesome-Knowledge-Distillation-of-LLMs

• highreadme#1

  Reposition the repository's purpose statement to appear immediately after the main H1 title

  Why:

  CURRENT

  The current README places the survey's title and author list before the repository's description.

  COPY-PASTE FIX

  Copy

  Move the paragraph starting with "*A collection of papers related to knowledge distillation of large language models (LLMs)...*" to be directly under the `# Awesome Knowledge Distillation of LLM Papers` heading, before the `<h2 align="center"> A Survey on Knowledge Distillation of Large Language Models </h2>` section.

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• highlicense#2

  Add a LICENSE file to the repository

  Why:

  COPY-PASTE FIX

  Copy

  Create a `LICENSE` file in the repository root with the chosen open-source license text (e.g., MIT, Apache-2.0, GPL-3.0).

  OpenDoneDismiss
• mediumhomepage#3

  Add a homepage URL to the repository's About section

  Why:

  COPY-PASTE FIX

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  Set the homepage URL in the repository settings to `https://arxiv.org/abs/2402.13116`.

  OpenDoneDismiss

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  How can I reduce the computational cost of large language models for efficient deployment?

  you: not recommended
  AI recommended (in order):

  1. bitsandbytes
  2. ONNX Runtime
  3. NVIDIA TensorRT
  4. Hugging Face Transformers
  5. PaddlePaddle PaddleSlim
  6. PyTorch
  7. TensorFlow
  8. NVIDIA TensorRT
  9. FlashAttention
  10. Mamba
  11. vLLM
  12. TGI (Text Generation Inference)
  13. ONNX Runtime
  14. NVIDIA TensorRT
  15. Google's Draft-and-Verify
  16. Hugging Face Transformers
  17. NVIDIA GPUs (A100, H100)
  18. Google TPUs
  19. AWS Inferentia

  AI recommended 19 alternatives but never named Tebmer/Awesome-Knowledge-Distillation-of-LLMs. This is the gap to close.

  Show full AI answer
• CATEGORY QUERY

  What techniques exist to transfer knowledge from large language models to smaller models?

  you: not recommended
  AI recommended (in order):

  1. Hugging Face Transformers
  2. accelerate
  3. DistilBERT
  4. TinyBERT
  5. ONNX Runtime
  6. PyTorch Quantization (torch.quantization)
  7. TensorFlow Lite
  8. NVIDIA TensorRT
  9. PyTorch Pruning (torch.nn.utils.prune)
  10. TensorFlow Model Optimization Toolkit
  11. NVIDIA Apex
  12. Fairseq
  13. LoRA (Low-Rank Adaptation)
  14. PEFT (Parameter-Efficient Fine-tuning)
  15. AutoKeras
  16. Google Cloud AutoML
  17. NNI (Neural Network Intelligence) by Microsoft

  AI recommended 17 alternatives but never named Tebmer/Awesome-Knowledge-Distillation-of-LLMs. This is the gap to close.

  Show full AI answer

• Metadata completeness

  warn

  Suggestion:

• README presence

  pass

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

  pass
  ⚠AI did not name Tebmer/Awesome-Knowledge-Distillation-of-LLMs — likely talking about a different project

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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

  pass
  ✓AI named Tebmer/Awesome-Knowledge-Distillation-of-LLMs explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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

  pass
  ⚠AI did not name Tebmer/Awesome-Knowledge-Distillation-of-LLMs — likely talking about a different project

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?