RRepoGEO

REPOGEO REPORT · LITE

predibase/llm_distillation_playbook

Default branch main · commit 6ccc4707 · scanned 6/2/2026, 6:18:19 AM

GitHub: 628 stars · 56 forks

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
1 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface predibase/llm_distillation_playbook, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    llm-distillation, large-language-models, best-practices, machine-learning, nlp, model-optimization, model-compression, deep-learning
  • highreadme#2
    Add a concise introductory sentence to the README

    Why:

    COPY-PASTE FIX
    This playbook provides a comprehensive guide and best practices for effectively distilling large language models, complete with runnable code examples.
  • mediumlicense#3
    Add a LICENSE file to clarify usage rights

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with your chosen open-source license (e.g., Apache-2.0, MIT).

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

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface predibase/llm_distillation_playbook
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 3×
  2. NVIDIA TensorRT · recommended 2×
  3. pytorch/pytorch · recommended 2×
  4. Hugging Face Transformers · recommended 1×
  5. Trainer · recommended 1×
  • CATEGORY QUERY
    What are the best practices for effectively distilling large language models for deployment?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Trainer
    3. DistilBERT
    4. TinyBERT
    5. PyTorch-Lightning
    6. TensorFlow Keras
    7. Hugging Face Optimum
    8. ONNX Runtime
    9. Intel OpenVINO
    10. NVIDIA TensorRT
    11. PyTorch Quantization API
    12. TensorFlow Lite (TFLite)
    13. SparseML
    14. NVIDIA Apex
    15. PyTorch Pruning API
    16. TensorFlow Model Optimization Toolkit
    17. PyTorch
    18. TensorFlow
    19. JAX
    20. MobileBERT
    21. ALBERT
    22. DeBERTa-v3
    23. Core ML

    AI recommended 23 alternatives but never named predibase/llm_distillation_playbook. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I reduce the size and inference cost of large language models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Optimum (huggingface/optimum)
    2. Intel's Neural Compressor (intel/neural-compressor)
    3. NVIDIA's TensorRT
    4. NVIDIA TensorRT
    5. OpenVINO (openvinotoolkit/openvino)
    6. SparseML (neuralmagic/sparseml)
    7. PyTorch's `torch.nn.utils.prune` (pytorch/pytorch)
    8. Hugging Face Transformers (huggingface/transformers)
    9. DistilBERT (huggingface/transformers)
    10. DistilRoBERTa (huggingface/transformers)
    11. PyTorch (pytorch/pytorch)
    12. TensorFlow (tensorflow/tensorflow)
    13. Hugging Face PEFT library (huggingface/peft)
    14. Google Gemma
    15. Meta Llama 3
    16. Microsoft Phi-3-mini

    AI recommended 16 alternatives but never named predibase/llm_distillation_playbook. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of predibase/llm_distillation_playbook?
    pass
    AI did not name predibase/llm_distillation_playbook — 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 predibase/llm_distillation_playbook in production, what risks or prerequisites should they evaluate first?
    pass
    AI named predibase/llm_distillation_playbook 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 predibase/llm_distillation_playbook solve, and who is the primary audience?
    pass
    AI did not name predibase/llm_distillation_playbook — 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?

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predibase/llm_distillation_playbook — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite