RRepoGEO

REPOGEO REPORT · LITE

Alpha-VLLM/LLaMA2-Accessory

Default branch main · commit 3777c439 · scanned 5/21/2026, 1:07:43 AM

GitHub: 2,805 stars · 176 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
28 /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
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 Alpha-VLLM/LLaMA2-Accessory, 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
    llm, multimodal-llm, large-language-models, llm-development, pretraining, finetuning, llama2, sphinx, deep-learning, pytorch
  • highabout#2
    Update the repository's 'About' description for clarity

    Why:

    CURRENT
    An Open-source Toolkit for LLM Development
    COPY-PASTE FIX
    An open-source toolkit for pretraining, finetuning, and deploying Large Language Models (LLMs) and multimodal LLMs, especially LLaMA2-based.
  • mediumreadme#3
    Add a clear statement about the repository's license to the README

    Why:

    COPY-PASTE FIX
    ## License 
 This project is released under [describe the actual license terms, e.g., "a custom license based on X and Y," or "the terms specified in the LICENSE file"]. Please refer to the LICENSE file for full details.

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 Alpha-VLLM/LLaMA2-Accessory
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. Lightning-AI/lightning · recommended 1×
  3. microsoft/DeepSpeed · recommended 1×
  4. huggingface/accelerate · recommended 1×
  5. openai/triton · recommended 1×
  • CATEGORY QUERY
    What open-source toolkits are available for developing and deploying large language models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. PyTorch Lightning (Lightning-AI/lightning)
    3. DeepSpeed (microsoft/DeepSpeed)
    4. Accelerate (huggingface/accelerate)
    5. OpenAI Triton (openai/triton)
    6. vLLM (vllm-project/vllm)
    7. LangChain (langchain-ai/langchain)

    AI recommended 7 alternatives but never named Alpha-VLLM/LLaMA2-Accessory. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an open-source framework for pretraining and finetuning multimodal large language models.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch Lightning
    3. DeepSpeed
    4. MMDetection
    5. OpenCLIP
    6. Fairseq

    AI recommended 6 alternatives but never named Alpha-VLLM/LLaMA2-Accessory. 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 Alpha-VLLM/LLaMA2-Accessory?
    pass
    AI named Alpha-VLLM/LLaMA2-Accessory explicitly

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

  • If a team adopts Alpha-VLLM/LLaMA2-Accessory in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Alpha-VLLM/LLaMA2-Accessory 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 Alpha-VLLM/LLaMA2-Accessory solve, and who is the primary audience?
    pass
    AI did not name Alpha-VLLM/LLaMA2-Accessory — 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?

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Alpha-VLLM/LLaMA2-Accessory — 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