RRepoGEO

REPOGEO REPORT · LITE

CStanKonrad/long_llama

Default branch main · commit bfcb8d1d · scanned 6/26/2026, 8:02:56 PM

GitHub: 1,466 stars · 84 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
22 /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
1 / 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 CStanKonrad/long_llama, 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 for LLMs and long context

    Why:

    COPY-PASTE FIX
    large-language-models, llm, long-context, transformers, open-source-llm, deep-learning, nlp, focused-transformer
  • mediumreadme#2
    Add a concise introductory sentence to the README

    Why:

    COPY-PASTE FIX
    LongLLaMA is an open-source large language model designed to process exceptionally long text contexts, built upon OpenLLaMA and fine-tuned with the Focused Transformer (FoT) method.
  • lowreadme#3
    Add a 'Key Features' section to the README

    Why:

    COPY-PASTE FIX
    ## Key Features
    
    *   **Extended Context Window:** Processes significantly longer text sequences than standard Llama models.
    *   **Focused Transformer (FoT) Method:** Leverages an efficient fine-tuning approach for context scaling.
    *   **Open-Source Foundation:** Built upon OpenLLaMA, providing a transparent and adaptable base.
    *   **Multiple Model Variants:** Offers different sizes (e.g., 3B, 7B) and instruction-tuned versions.

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 CStanKonrad/long_llama
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Mistral Large
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Mistral Large · recommended 2×
  2. Claude 3 Opus / Claude 3 Sonnet / Claude 3 Haiku · recommended 1×
  3. GPT-4 Turbo · recommended 1×
  4. Gemini 1.5 Pro · recommended 1×
  5. Llama 2 · recommended 1×
  • CATEGORY QUERY
    What large language models excel at understanding and generating text from very long contexts?
    you: not recommended
    AI recommended (in order):
    1. Claude 3 Opus / Claude 3 Sonnet / Claude 3 Haiku
    2. GPT-4 Turbo
    3. Gemini 1.5 Pro
    4. Mistral Large
    5. Llama 2

    AI recommended 5 alternatives but never named CStanKonrad/long_llama. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need an efficient open-source LLM capable of processing extremely lengthy documents or conversations.
    you: not recommended
    AI recommended (in order):
    1. Mistral Large
    2. Mixtral 8x22B
    3. Hugging Face Transformers
    4. vLLM
    5. LongRoPE
    6. Llama-2-7B-LongRoPE
    7. Yarn-Llama-2-7B-128k
    8. Yarn-Llama-2-13B-128k
    9. Qwen-1.5-7B-Chat-128K
    10. Qwen-1.5-14B-Chat-64K
    11. Gemma-7B-IT
    12. Mistral-7B-v0.2

    AI recommended 12 alternatives but never named CStanKonrad/long_llama. 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 CStanKonrad/long_llama?
    pass
    AI did not name CStanKonrad/long_llama — 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 CStanKonrad/long_llama in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name CStanKonrad/long_llama — 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?

  • In one sentence, what problem does the repo CStanKonrad/long_llama solve, and who is the primary audience?
    pass
    AI named CStanKonrad/long_llama explicitly

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

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CStanKonrad/long_llama — 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