RRepoGEO

REPOGEO REPORT · LITE

invergent-ai/surogate

Default branch main · commit e9f6cd9c · scanned 6/10/2026, 5:17:16 AM

GitHub: 796 stars · 5 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
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 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 invergent-ai/surogate, 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
    Reposition README opening to explicitly define purpose and counter miscategorization

    Why:

    CURRENT
    The current README starts with visual elements, then an H3, then links, then '# Surogate Trainer' followed by its description.
    COPY-PASTE FIX
    Place the following text immediately after the main title (e.g., `# Surogate Trainer`):

"Surogate Trainer is a high-performance framework for pre-training, fine-tuning, and reinforcement learning of large language models (LLMs) and other deep learning models. Unlike agent orchestration platforms or AI gateways, Surogate focuses purely on accelerating the training process, offering superior speed and VRAM efficiency compared to existing frameworks like QLoRA."
  • mediumreadme#2
    Add a dedicated comparison section to the README

    Why:

    CURRENT
    The README mentions performance claims within the main text and highlights, but lacks a dedicated comparison section.
    COPY-PASTE FIX
    Add a new section to the README titled '## Why Surogate? Outperforming the Competition' or similar, with content like:

"Surogate Trainer is engineered for unparalleled speed and efficiency in LLM training and fine-tuning. While frameworks like DeepSpeed and Hugging Face Accelerate offer distributed training, Surogate provides superior single-GPU, multi-GPU, and CPU+GPU performance. Crucially, Surogate's native BF16 precision and advanced FP8/NVFP4 capabilities render QLoRA obsolete by achieving better VRAM usage and significantly faster training times without quantization compromises."
  • lowreadme#3
    Streamline the README's initial visual elements

    Why:

    CURRENT
    The README starts with a large `div` containing images, links, and an H3, before the main `# Surogate Trainer` heading and its descriptive text.
    COPY-PASTE FIX
    Reorganize the README to place the `# Surogate Trainer` heading and its immediate descriptive paragraph (as refined in the high-priority action) at the very top. Move the `div align="center"` block and its contents, including badges and navigation links, further down the README, perhaps after the initial descriptive text and highlights.

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 invergent-ai/surogate
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DeepSpeed
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. DeepSpeed · recommended 1×
  2. PyTorch FSDP · recommended 1×
  3. Hugging Face Accelerate · recommended 1×
  4. bitsandbytes · recommended 1×
  5. NVIDIA Apex · recommended 1×
  • CATEGORY QUERY
    How can I accelerate large language model fine-tuning on NVIDIA GPUs for faster experimentation?
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed
    2. PyTorch FSDP
    3. Hugging Face Accelerate
    4. bitsandbytes
    5. NVIDIA Apex
    6. FlashAttention
    7. Optimum

    AI recommended 7 alternatives but never named invergent-ai/surogate. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are efficient alternatives to QLoRA for LLM training with better VRAM usage and performance?
    you: not recommended
    AI recommended (in order):
    1. LoRA
    2. Hugging Face PEFT (huggingface/peft)
    3. FlashAttention-2 (Dao-AILab/flash-attention)
    4. BetterTransformer
    5. DeepSpeed ZeRO (microsoft/DeepSpeed)
    6. FSDP
    7. Unsloth (unslothai/unsloth)
    8. Axolotl (OpenAccess-AI-Collective/axolotl)

    AI recommended 8 alternatives but never named invergent-ai/surogate. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 invergent-ai/surogate?
    pass
    AI named invergent-ai/surogate explicitly

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

  • If a team adopts invergent-ai/surogate in production, what risks or prerequisites should they evaluate first?
    pass
    AI named invergent-ai/surogate 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 invergent-ai/surogate solve, and who is the primary audience?
    pass
    AI named invergent-ai/surogate explicitly

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

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invergent-ai/surogate — 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