REPOGEO REPORT · LITE

rasbt/LLM-workshop-2024

Default branch main · commit cf929412 · scanned 6/25/2026, 12:29:00 PM

GitHub: 1,100 stars · 380 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)

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

AI VISIBILITY SCORE

28 /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

2 / 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 rasbt/LLM-workshop-2024, 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

highreadme#1

Reposition README opening to emphasize "coding workshop"

Why:

CURRENT

This tutorial is aimed at coders interested in understanding the building blocks of large language models (LLMs), how LLMs work, and how to code them from the ground up in PyTorch.

COPY-PASTE FIX

This 4-hour coding workshop provides a hands-on tutorial for coders to understand the building blocks of large language models (LLMs), how they work, and how to code them from the ground up in PyTorch.

mediumtopics#2

Add specific topics to clarify "workshop" and "from scratch"

Why:

CURRENT

large-language-models, llm, pytorch

COPY-PASTE FIX

large-language-models, llm, pytorch, llm-workshop, deep-learning-tutorial, build-llm-from-scratch, machine-learning-course

lowhomepage#3
Add the workshop's primary homepage URL
Why:
COPY-PASTE FIX
```
https://lightning.ai/lightning-ai/studios/llms-from-the-ground-up-workshop
```

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 rasbt/LLM-workshop-2024

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Hugging Face Transformers Library

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Hugging Face Transformers Library · recommended 2×
DeepSpeed · recommended 2×
PyTorch Documentation · recommended 1×
Deep Learning with PyTorch · recommended 1×
Attention Is All You Need · recommended 1×

CATEGORY QUERY
How can I learn to build large language models from scratch using PyTorch?
you: not recommended
AI recommended (in order):
1. PyTorch Documentation
2. Deep Learning with PyTorch
3. Attention Is All You Need
4. The Illustrated Transformer
5. The Annotated Transformer
6. NanoGPT
7. Let's build GPT: from scratch, in code, spelled out.
8. Hugging Face Transformers Library
9. PyTorch FSDP
10. DeepSpeed
11. FlashAttention
12. Hugging Face Datasets Library
13. tokenizers
AI recommended 13 alternatives but never named rasbt/LLM-workshop-2024. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are good resources for finetuning open-source large language models with PyTorch?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers Library
2. Hugging Face PEFT (Parameter-Efficient Fine-Tuning) Library
3. PyTorch Lightning
4. DeepSpeed
5. bitsandbytes
6. Axolotl
AI recommended 6 alternatives but never named rasbt/LLM-workshop-2024. 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 rasbt/LLM-workshop-2024?
pass
AI named rasbt/LLM-workshop-2024 explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts rasbt/LLM-workshop-2024 in production, what risks or prerequisites should they evaluate first?
pass
AI named rasbt/LLM-workshop-2024 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 rasbt/LLM-workshop-2024 solve, and who is the primary audience?
pass
AI did not name rasbt/LLM-workshop-2024 — 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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MARKDOWN (README)

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rasbt/LLM-workshop-2024 — 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