RRepoGEO

REPOGEO REPORT · LITE

facebookresearch/metaseq

Default branch main · commit f7ffa5fd · scanned 6/19/2026, 8:47:39 PM

GitHub: 6,547 stars · 718 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
35 /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
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 facebookresearch/metaseq, 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 specific topics for LLM training and optimization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    large-language-models, llm-training, distributed-training, transformer-models, deep-learning, pytorch, fairseq, llm-optimization, llm-inference
  • highabout#2
    Update the repository description for clarity

    Why:

    CURRENT
    Repo for external large-scale work
    COPY-PASTE FIX
    A codebase for efficiently training and optimizing very large language models (LLMs) at scale, based on fairseq, with extensive integrations for deployment.
  • highreadme#3
    Strengthen the README's opening sentence to highlight core purpose

    Why:

    CURRENT
    A codebase for working with [Open Pre-trained Transformers](projects/OPT), originally forked from fairseq.
    COPY-PASTE FIX
    Metaseq is a specialized codebase for efficiently training and optimizing very large language models (LLMs) at scale, originally forked from fairseq and designed for projects like Open Pre-trained Transformers (OPT).

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 facebookresearch/metaseq
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
triton-inference-server/server
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. triton-inference-server/server · recommended 1×
  2. vllm-project/vllm · recommended 1×
  3. openvinotoolkit/openvino · recommended 1×
  4. NVIDIA/TensorRT-LLM · recommended 1×
  5. ray-project/ray · recommended 1×
  • CATEGORY QUERY
    How to efficiently deploy and serve large language models for inference?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Triton Inference Server (triton-inference-server/server)
    2. vLLM (vllm-project/vllm)
    3. OpenVINO (openvinotoolkit/openvino)
    4. TensorRT-LLM (NVIDIA/TensorRT-LLM)
    5. Ray Serve (ray-project/ray)
    6. KServe (kserve/kserve)
    7. DeepSpeed-MII (microsoft/DeepSpeed)

    AI recommended 7 alternatives but never named facebookresearch/metaseq. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help train and optimize large transformer models on various hardware?
    you: not recommended
    AI recommended (in order):
    1. PyTorch FSDP
    2. DeepSpeed (microsoft/deepspeed)
    3. Hugging Face Accelerate (huggingface/accelerate)
    4. NVIDIA Apex (nvidia/apex)
    5. TensorFlow Distributed Strategy API
    6. NVIDIA Nsight Systems / Nsight Compute
    7. Optimum (huggingface/optimum)

    AI recommended 7 alternatives but never named facebookresearch/metaseq. 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 facebookresearch/metaseq?
    pass
    AI named facebookresearch/metaseq explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of facebookresearch/metaseq. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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facebookresearch/metaseq — 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