RRepoGEO

REPOGEO REPORT · LITE

jzhang38/TinyLlama

Default branch main · commit bf122247 · scanned 5/12/2026, 5:28:11 AM

GitHub: 8,954 stars · 615 forks

AI VISIBILITY SCORE
62 /100
Needs work
Category recall
1 / 2
Avg rank #1.0 when recommended
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 jzhang38/TinyLlama, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    small-language-model, llm, llama, pretraining, deep-learning, machine-learning, ai, nlp, tinyllama, 1.1b-model, edge-ai, resource-constrained
  • highhomepage#2
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    [Insert the official project homepage URL here, e.g., a Hugging Face model page or a dedicated project website]
  • mediumreadme#3
    Strengthen the README's opening for unique value proposition

    Why:

    CURRENT
    The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.
    COPY-PASTE FIX
    The **TinyLlama** project is an ambitious open endeavor to pretrain a compact yet powerful **1.1B Llama model on 3 trillion tokens**, replicating the Llama 2 architecture. This makes TinyLlama a unique, resource-efficient foundational language model, ideal for applications demanding a restricted computation and memory footprint. With proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.

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
1 / 2
50% of queries surface jzhang38/TinyLlama
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
8%
Of all named tools, what % are you?
Top rival
Phi-2
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Phi-2 · recommended 2×
  2. MobileLLaMA · recommended 1×
  3. OpenLLaMA · recommended 1×
  4. DistilBERT · recommended 1×
  5. GPT-2 · recommended 1×
  • CATEGORY QUERY
    What are some efficient small language models suitable for edge devices or limited resources?
    you: #1
    AI recommended (in order):
    1. TinyLlama ← you
    2. Phi-2
    3. MobileLLaMA
    4. OpenLLaMA
    5. DistilBERT
    6. GPT-2
    Show full AI answer
  • CATEGORY QUERY
    Looking for a compact, pre-trained large language model for integration into existing projects.
    you: not recommended
    AI recommended (in order):
    1. Mistral 7B
    2. Mistral 7B Instruct
    3. Llama 2 7B
    4. Gemma 2B/7B
    5. TinyLlama 1.1B
    6. Phi-2

    AI recommended 6 alternatives but never named jzhang38/TinyLlama. 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 jzhang38/TinyLlama?
    pass
    AI did not name jzhang38/TinyLlama — 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?

  • If a team adopts jzhang38/TinyLlama in production, what risks or prerequisites should they evaluate first?
    pass
    AI named jzhang38/TinyLlama 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 jzhang38/TinyLlama solve, and who is the primary audience?
    pass
    AI named jzhang38/TinyLlama 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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jzhang38/TinyLlama — 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