RRepoGEO

REPOGEO REPORT · LITE

dvgodoy/FineTuningLLMs

Default branch main · commit ff5bb793 · scanned 6/3/2026, 12:27:55 PM

GitHub: 830 stars · 111 forks

AI VISIBILITY SCORE
35 /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
3 / 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 dvgodoy/FineTuningLLMs, 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
    Add a concise positioning statement to the README's opening

    Why:

    CURRENT
    The README's first content after the title and book links is "## Setup".
    COPY-PASTE FIX
    Add the following sentence immediately after the book links: "This repository provides all the code notebooks and resources for 'A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face', designed to help you master LLM fine-tuning through practical examples."
  • mediumhomepage#2
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://www.amazon.com/dp/B0DV3Y1GMP
  • lowtopics#3
    Expand topics to include 'guide' and 'tutorial' keywords

    Why:

    CURRENT
    bitsandbytes, fine-tuning, finetuning, finetuning-llms, hugging-face, huggingface, large-language-models, llamacpp, lora, ollama, peft, peft-fine-tuning-llm, pytorch, transformers
    COPY-PASTE FIX
    bitsandbytes, fine-tuning, finetuning, finetuning-llms, hugging-face, huggingface, large-language-models, llamacpp, lora, ollama, peft, peft-fine-tuning-llm, pytorch, transformers, llm-fine-tuning-guide, pytorch-tutorial, huggingface-tutorial, llm-guide

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 dvgodoy/FineTuningLLMs
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 1×
  2. Accelerate · recommended 1×
  3. bitsandbytes · recommended 1×
  4. PEFT · recommended 1×
  5. LoRA · recommended 1×
  • CATEGORY QUERY
    How can I efficiently fine-tune large language models using PyTorch and Hugging Face?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Accelerate
    3. bitsandbytes
    4. PEFT
    5. LoRA
    6. QLoRA
    7. DeepSpeed
    8. PyTorch FSDP
    9. FlashAttention

    AI recommended 9 alternatives but never named dvgodoy/FineTuningLLMs. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a hands-on guide to apply LoRA for customizing open-source LLMs.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face PEFT Library (huggingface/peft)
    2. Alpaca-LoRA (chujiezheng/alpaca-lora)
    3. Unsloth (unslothai/unsloth)
    4. Lamini
    5. llama2.c (karpathy/llama2.c)
    6. QLoRA (artidoro/qlora)

    AI recommended 6 alternatives but never named dvgodoy/FineTuningLLMs. 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 dvgodoy/FineTuningLLMs?
    pass
    AI named dvgodoy/FineTuningLLMs explicitly

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

  • If a team adopts dvgodoy/FineTuningLLMs in production, what risks or prerequisites should they evaluate first?
    pass
    AI named dvgodoy/FineTuningLLMs 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 dvgodoy/FineTuningLLMs solve, and who is the primary audience?
    pass
    AI named dvgodoy/FineTuningLLMs explicitly

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

Embed your GEO score

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dvgodoy/FineTuningLLMs — 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