RRepoGEO

REPOGEO REPORT · LITE

run-llama/finetune-embedding

Default branch main · commit da96b601 · scanned 6/2/2026, 10:42:40 AM

GitHub: 527 stars · 73 forks

AI VISIBILITY SCORE
30 /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
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 run-llama/finetune-embedding, 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
    Rephrase 'outdated' statement in README to clarify current relevance

    Why:

    CURRENT
    UPDATE 9/10/2023: We've included embedding finetuning abstractions into the LlamaIndex repo, so this repo is technically outdated! Please check out our embedding fine-tuning guides in the core documentation.
    COPY-PASTE FIX
    This repository serves as a practical demonstration and reference implementation for fine-tuning embedding models for RAG with synthetic data. While core abstractions are now integrated into LlamaIndex, this repo provides a step-by-step walkthrough and experimental results, complementing the official LlamaIndex documentation.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    rag, embedding-finetuning, synthetic-data, llama-index, llm, machine-learning, natural-language-processing
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root, specifying the terms under which the project is distributed. For example, if an Apache-2.0 license is desired, the file should contain the full Apache-2.0 license text.

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 run-llama/finetune-embedding
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
sentence-transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. sentence-transformers · recommended 1×
  2. GPL · recommended 1×
  3. Hugging Face Transformers · recommended 1×
  4. Ragas · recommended 1×
  5. Haystack · recommended 1×
  • CATEGORY QUERY
    How can I fine-tune an embedding model for RAG without access to labeled datasets?
    you: not recommended
    AI recommended (in order):
    1. sentence-transformers
    2. GPL
    3. Hugging Face Transformers

    AI recommended 3 alternatives but never named run-llama/finetune-embedding. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for ways to boost RAG retrieval performance using synthetic data generation techniques.
    you: not recommended
    AI recommended (in order):
    1. Ragas
    2. Haystack
    3. GPT-4
    4. Claude 3
    5. Llama 3
    6. Snorkel Flow
    7. nlpaug
    8. TextAttack
    9. OpenAI Evals

    AI recommended 9 alternatives but never named run-llama/finetune-embedding. 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 run-llama/finetune-embedding?
    pass
    AI named run-llama/finetune-embedding explicitly

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

  • If a team adopts run-llama/finetune-embedding in production, what risks or prerequisites should they evaluate first?
    pass
    AI named run-llama/finetune-embedding 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 run-llama/finetune-embedding solve, and who is the primary audience?
    pass
    AI named run-llama/finetune-embedding 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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MARKDOWN (README)
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run-llama/finetune-embedding — 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