RRepoGEO

REPOGEO REPORT · LITE

microsoft/Samba

Default branch main · commit 617c7a0f · scanned 6/8/2026, 8:51:53 AM

GitHub: 961 stars · 49 forks

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 microsoft/Samba, 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
    Add a clear disambiguation statement to the README's introduction

    Why:

    CURRENT
    The README starts with the H1, then dives into architecture details.
    COPY-PASTE FIX
    This repository introduces **Samba**, a novel language model architecture, and is distinct from the Samba networking software project. It focuses on efficient, unlimited context language modeling.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    language-model, llm, state-space-models, deep-learning, ai, unlimited-context, long-context, machine-learning, transformer-alternative, efficient-llm
  • mediumreadme#3
    Emphasize core differentiators for long-context tasks in the README's initial description

    Why:

    CURRENT
    Samba is a simple yet powerful hybrid model with an **unlimited** context length. Its architecture is frustratingly simple: Samba = Mamba + MLP + Sliding Window Attention + MLP stacking at the layer level. Our largest model, `Samba-3.8B`, is trained on 3.2 trillion tokens from the Phi3 dataset, outperforming `Phi3-mini` on major benchmarks (e.g. MMLU, GSM8K and HumanEval) by a large margin. Samba can also achieve perfect **long-context** retrieval ability with minimal instruction tuning, while still maintaining its **linear complexity** with respect to sequence length.
    COPY-PASTE FIX
    Samba is a simple yet powerful hybrid model designed for **efficient, unlimited context language modeling with linear complexity**, making it ideal for long-context summarization and retrieval tasks. Its architecture combines Mamba, MLP, and Sliding Window Attention to achieve this.

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 microsoft/Samba
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hyena Hierarchy (H3)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hyena Hierarchy (H3) · recommended 1×
  2. HyenaDNA · recommended 1×
  3. FlashAttention · recommended 1×
  4. FlashAttention-2 · recommended 1×
  5. RingAttention · recommended 1×
  • CATEGORY QUERY
    What are efficient language models for processing unlimited context lengths with linear complexity?
    you: not recommended
    AI recommended (in order):
    1. Hyena Hierarchy (H3)
    2. HyenaDNA
    3. FlashAttention
    4. FlashAttention-2
    5. RingAttention
    6. RWKV
    7. Mamba
    8. Mamba-2
    9. Longformer
    10. BigBird
    11. Reformer
    12. REALM
    13. RAG
    14. Atlas

    AI recommended 14 alternatives but never named microsoft/Samba. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a language model that excels at long-context summarization and retrieval tasks.
    you: not recommended
    AI recommended (in order):
    1. Claude 3 Opus
    2. Claude 3 Sonnet
    3. GPT-4 Turbo
    4. Gemini 1.5 Pro
    5. Mistral Large
    6. Llama 3

    AI recommended 6 alternatives but never named microsoft/Samba. 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 microsoft/Samba?
    pass
    AI named microsoft/Samba explicitly

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

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

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

Embed your GEO score

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MARKDOWN (README)
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HTML
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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite