RRepoGEO

REPOGEO REPORT · LITE

X-LANCE/SLAM-LLM

Default branch main · commit a68e78f9 · scanned 6/20/2026, 8:13:51 PM

GitHub: 1,038 stars · 115 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 X-LANCE/SLAM-LLM, 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
    Clarify the 'SLAM' acronym in the README's opening

    Why:

    CURRENT
    SLAM-LLM is a deep learning toolkit that allows researchers and developers to train custom multimodal large language model (MLLM), focusing on Speech, Language, Audio, Music processing.
    COPY-PASTE FIX
    SLAM-LLM (Speech, Language, Audio, Music Large Language Model) is a deep learning toolkit that allows researchers and developers to train custom multimodal large language model (MLLM), focusing on Speech, Language, Audio, Music processing.
  • highhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    [Your project's official website or documentation URL]
  • mediumreadme#3
    Emphasize 'specialized toolkit' and 'recipes' in the README's opening

    Why:

    CURRENT
    SLAM-LLM is a deep learning toolkit that allows researchers and developers to train custom multimodal large language model (MLLM), focusing on Speech, Language, Audio, Music processing. We provide detailed recipes for training and high-performance checkpoints for inference.
    COPY-PASTE FIX
    SLAM-LLM (Speech, Language, Audio, Music Large Language Model) is a specialized deep learning toolkit designed for researchers and developers to efficiently train custom multimodal large language models (MLLMs). It provides detailed, ready-to-use recipes and high-performance checkpoints specifically for Speech, Language, Audio, and Music processing tasks.

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 X-LANCE/SLAM-LLM
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 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 2×
  2. tensorflow/tensorflow · recommended 2×
  3. keras-team/keras · recommended 2×
  4. huggingface/accelerate · recommended 1×
  5. huggingface/datasets · recommended 1×
  • CATEGORY QUERY
    How to train custom multimodal large language models for speech and music tasks?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Accelerate (huggingface/accelerate)
    3. Datasets (huggingface/datasets)
    4. PEFT (huggingface/peft)
    5. PyTorch Lightning (Lightning-AI/pytorch-lightning)
    6. TensorFlow (tensorflow/tensorflow)
    7. Keras (keras-team/keras)
    8. fairseq (facebookresearch/fairseq)
    9. Jukebox (openai/jukebox)
    10. MusicLM

    AI recommended 10 alternatives but never named X-LANCE/SLAM-LLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a deep learning toolkit to develop MLLMs for audio and language applications.
    you: not recommended
    AI recommended (in order):
    1. PyTorch (pytorch/pytorch)
    2. Hugging Face Transformers (huggingface/transformers)
    3. ĸ Audiocraft (facebookresearch/audiocraft)
    4. TensorFlow (tensorflow/tensorflow)
    5. Keras (keras-team/keras)
    6. TensorFlow Audio
    7. JAX (google/jax)
    8. Flax (google/flax)
    9. Haiku (deepmind/dm-haiku)
    10. PaddlePaddle (PaddlePaddle/Paddle)
    11. PaddleSpeech (PaddlePaddle/PaddleSpeech)
    12. PaddleNLP (PaddlePaddle/PaddleNLP)
    13. MXNet (apache/mxnet)
    14. GluonNLP (apache/gluon-nlp)
    15. GluonCV (apache/gluon-cv)

    AI recommended 15 alternatives but never named X-LANCE/SLAM-LLM. 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 X-LANCE/SLAM-LLM?
    pass
    AI named X-LANCE/SLAM-LLM explicitly

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

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

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

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X-LANCE/SLAM-LLM — 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