RRepoGEO

REPOGEO REPORT · LITE

johnsmith0031/alpaca_lora_4bit

Default branch winglian-setup_pip · commit d983b127 · scanned 6/9/2026, 8:47:37 AM

GitHub: 535 stars · 84 forks

AI VISIBILITY SCORE
23 /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
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 johnsmith0031/alpaca_lora_4bit, 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
  • highabout#1
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    Enable LoRA fine-tuning of large language models (LLMs) with 4-bit quantization, significantly reducing memory usage for training on consumer GPUs.
  • mediumreadme#2
    Strengthen the README's opening sentence for clarity

    Why:

    CURRENT
    # Alpaca Lora 4bit
Made some adjust for the code in peft and gptq for llama, and make it possible for lora finetuning with a 4 bits base model. The same adjustment can be made for 2, 3 and 8 bits.
    COPY-PASTE FIX
    # Alpaca Lora 4bit
Enable efficient LoRA fine-tuning of large language models (LLMs) with 4-bit quantization, making it possible to train models like Alpaca on consumer-grade GPUs. This project adapts code from PEFT and GPTQ-for-LLaMa to significantly reduce memory usage, with support for 2, 3, and 8-bit quantization.

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 johnsmith0031/alpaca_lora_4bit
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/peft
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/peft · recommended 1×
  2. TimDettmers/bitsandbytes · recommended 1×
  3. OpenAccess-AI-Collective/axolotl · recommended 1×
  4. unslothai/unsloth · recommended 1×
  5. Lightning-AI/lit-gpt · recommended 1×
  • CATEGORY QUERY
    How can I finetune large language models with 4-bit quantization to save memory?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face PEFT (huggingface/peft)
    2. bitsandbytes (TimDettmers/bitsandbytes)
    3. Axolotl (OpenAccess-AI-Collective/axolotl)
    4. Unsloth (unslothai/unsloth)
    5. Lit-GPT (Lightning-AI/lit-gpt)
    6. LLaMA-Factory (hiyouga/LLaMA-Factory)

    AI recommended 6 alternatives but never named johnsmith0031/alpaca_lora_4bit. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools allow LoRA finetuning of quantized language models on resource-constrained GPUs?
    you: not recommended
    AI recommended (in order):
    1. QLoRA
    2. Hugging Face PEFT
    3. Unsloth
    4. Axolotl
    5. Lit-GPT

    AI recommended 5 alternatives but never named johnsmith0031/alpaca_lora_4bit. 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 johnsmith0031/alpaca_lora_4bit?
    pass
    AI named johnsmith0031/alpaca_lora_4bit explicitly

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

  • If a team adopts johnsmith0031/alpaca_lora_4bit in production, what risks or prerequisites should they evaluate first?
    pass
    AI named johnsmith0031/alpaca_lora_4bit 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 johnsmith0031/alpaca_lora_4bit solve, and who is the primary audience?
    pass
    AI did not name johnsmith0031/alpaca_lora_4bit — 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?

Embed your GEO score

Drop this badge into the README of johnsmith0031/alpaca_lora_4bit. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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johnsmith0031/alpaca_lora_4bit — 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