RRepoGEO

REPOGEO REPORT · LITE

AnswerDotAI/fsdp_qlora

Default branch main · commit 05ed9f2a · scanned 6/27/2026, 6:38:30 PM

GitHub: 1,549 stars · 201 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
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 AnswerDotAI/fsdp_qlora, 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
    Strengthen README's opening statement to highlight core benefits

    Why:

    CURRENT
    # fsdp_qlora

Training LLMs with Quantized LoRA + FSDP.
    COPY-PASTE FIX
    # fsdp_qlora

**fsdp_qlora provides a reference implementation for highly memory-efficient and scalable fine-tuning of large language models (LLMs) by combining Quantized LoRA (QLoRA) with PyTorch's Fully Sharded Data Parallel (FSDP).** This approach enables training large models like Llama-2 70B on limited hardware resources, such as dual 24GB GPUs.
  • mediumhomepage#2
    Add a homepage URL

    Why:

    COPY-PASTE FIX
    (Provide a relevant URL, e.g., to the project's main page or a detailed blog post)

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 AnswerDotAI/fsdp_qlora
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DeepSpeed
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. DeepSpeed · recommended 2×
  2. PyTorch FSDP · recommended 2×
  3. Hugging Face Transformers · recommended 1×
  4. PEFT · recommended 1×
  5. LoRA · recommended 1×
  • CATEGORY QUERY
    What are the best approaches for training large language models on limited hardware resources?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PEFT
    3. LoRA
    4. QLoRA
    5. bitsandbytes
    6. DeepSpeed
    7. PyTorch FSDP
    8. Gradient Checkpointing
    9. FlashAttention
    10. xFormers
    11. Llama 2
    12. Mistral

    AI recommended 12 alternatives but never named AnswerDotAI/fsdp_qlora. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking tools to scale parameter-efficient language model fine-tuning across multiple accelerators.
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed
    2. PyTorch FSDP
    3. Hugging Face Accelerate
    4. Colossal-AI
    5. Megatron-LM (NVIDIA)

    AI recommended 5 alternatives but never named AnswerDotAI/fsdp_qlora. 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 AnswerDotAI/fsdp_qlora?
    pass
    AI named AnswerDotAI/fsdp_qlora explicitly

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

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

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

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AnswerDotAI/fsdp_qlora — 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