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REPOGEO REPORT · LITE

afshinea/stanford-cme-295-transformers-large-language-models

Default branch main · commit e6a2bd27 · scanned 5/19/2026, 6:47:51 PM

GitHub: 4,405 stars · 623 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
15 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
0 / 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 afshinea/stanford-cme-295-transformers-large-language-models, 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

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

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's core purpose statement

    Why:

    CURRENT
    # Transformers & LLMs cheatsheet for Stanford's CME 295
Available in العربية - Čeština - English - Español - فارسی - Français - Italiano - 日本語 - 한국어 - ไทย - Türkçe - 中文

## Goal
This repository aims at summing up in the same place all the important notions that are covered in Stanford's CME 295 Transformers & Large Language Models course.
    COPY-PASTE FIX
    # Transformers & LLMs cheatsheet for Stanford's CME 295
This repository provides a comprehensive study guide and VIP cheatsheet for the key concepts covered in Stanford's CME 295 Transformers & Large Language Models course.
Available in العربية - Čeština - English - Español - فارسی - Français - Italiano - 日本語 - 한국어 - ไทย - Türkçe - 中文
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://cme295.stanford.edu

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 afshinea/stanford-cme-295-transformers-large-language-models
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 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 2×
  2. pytorch/pytorch · recommended 2×
  3. Attention Is All You Need · recommended 1×
  4. The Illustrated Transformer · recommended 1×
  5. The Illustrated GPT-2 · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive overview of Transformer and LLM architectures?
    you: not recommended
    AI recommended (in order):
    1. Attention Is All You Need
    2. The Illustrated Transformer
    3. The Illustrated GPT-2
    4. Hugging Face Transformers (huggingface/transformers)
    5. Stanford CS224N: Natural Language Processing with Deep Learning
    6. Transformers for Natural Language Processing

    AI recommended 6 alternatives but never named afshinea/stanford-cme-295-transformers-large-language-models. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective optimization techniques for large language models and Transformer-based applications?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Quantization (pytorch/pytorch)
    2. ONNX Runtime (microsoft/onnxruntime)
    3. TensorRT (NVIDIA/TensorRT)
    4. Hugging Face Optimum (huggingface/optimum)
    5. 🤗 Accelerate (huggingface/accelerate)
    6. NNCF (openvinotoolkit/nncf)
    7. sparseml (neuralmagic/sparseml)
    8. PyTorch Pruning (pytorch/pytorch)
    9. NVIDIA Nsight Systems (NVIDIA/nsight-systems)
    10. Hugging Face Transformers (huggingface/transformers)
    11. PaddlePaddle PaddleSlim (PaddlePaddle/PaddleSlim)
    12. TensorFlow Model Optimization Toolkit (tensorflow/model-optimization)
    13. DistilBERT
    14. TinyBERT
    15. Longformer (allenai/longformer)
    16. Reformer
    17. Performer
    18. OpenVINO Toolkit (openvinotoolkit/openvino)
    19. DeepSpeed (microsoft/DeepSpeed)
    20. TorchServe (pytorch/serve)
    21. NVIDIA GPUs
    22. TPUs
    23. Intel CPUs
    24. Triton Inference Server (triton-inference-server/server)
    25. FastAPI (tiangolo/fastapi)
    26. Flask (pallets/flask)

    AI recommended 26 alternatives but never named afshinea/stanford-cme-295-transformers-large-language-models. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    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 afshinea/stanford-cme-295-transformers-large-language-models?
    pass
    AI did not name afshinea/stanford-cme-295-transformers-large-language-models — 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 afshinea/stanford-cme-295-transformers-large-language-models in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name afshinea/stanford-cme-295-transformers-large-language-models — 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?

  • In one sentence, what problem does the repo afshinea/stanford-cme-295-transformers-large-language-models solve, and who is the primary audience?
    pass
    AI did not name afshinea/stanford-cme-295-transformers-large-language-models — 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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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite