RRepoGEO

REPOGEO REPORT · LITE

mustafaaljadery/gemma-2B-10M

Default branch main · commit cb97c2f6 · scanned 6/8/2026, 12:13:01 AM

GitHub: 933 stars · 64 forks

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
1 / 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 mustafaaljadery/gemma-2B-10M, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's opening to highlight the problem solved

    Why:

    CURRENT
    # Gemma 2B - 10M Context

Gemma 2B with recurrent local attention with context length of up to 10M. Our implementation uses **<32GB** of memory!
    COPY-PASTE FIX
    # Gemma 2B - 10M Context: Long Context LLMs on Limited Memory

Gemma 2B with recurrent local attention, enabling an unprecedented 10M context length while running on **less than 32GB** of memory. This project solves the critical challenge of deploying large language models with massive context windows efficiently on consumer-grade hardware.
  • highlicense#2
    Add a LICENSE file to clarify usage rights

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the root of the repository, choosing a standard open-source license such as Apache-2.0 or MIT, and include its 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 mustafaaljadery/gemma-2B-10M
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ggerganov/llama.cpp
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ggerganov/llama.cpp · recommended 1×
  2. vllm-project/vllm · recommended 1×
  3. ollama/ollama · recommended 1×
  4. LM Studio · recommended 1×
  5. huggingface/transformers · recommended 1×
  • CATEGORY QUERY
    Need a local LLM solution supporting extremely long context windows efficiently.
    you: not recommended
    AI recommended (in order):
    1. llama.cpp (ggerganov/llama.cpp)
    2. vLLM (vllm-project/vllm)
    3. Ollama (ollama/ollama)
    4. LM Studio
    5. Transformers (huggingface/transformers)
    6. bitsandbytes (TimDettmers/bitsandbytes)
    7. AutoGPTQ (PanQiWei/AutoGPTQ)

    AI recommended 7 alternatives but never named mustafaaljadery/gemma-2B-10M. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to deploy large language models with massive context on limited memory?
    you: not recommended
    AI recommended (in order):
    1. GPTQ
    2. AWQ
    3. AutoGPTQ
    4. vLLM
    5. PagedAttention
    6. DeepSpeed Inference
    7. ZeRO-Offload
    8. ZeRO-Infinity
    9. FlashAttention-2
    10. Llama.cpp
    11. llama-cpp-python
    12. Ollama

    AI recommended 12 alternatives but never named mustafaaljadery/gemma-2B-10M. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 mustafaaljadery/gemma-2B-10M?
    pass
    AI did not name mustafaaljadery/gemma-2B-10M — 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?

  • If a team adopts mustafaaljadery/gemma-2B-10M in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mustafaaljadery/gemma-2B-10M 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 mustafaaljadery/gemma-2B-10M solve, and who is the primary audience?
    pass
    AI did not name mustafaaljadery/gemma-2B-10M — 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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mustafaaljadery/gemma-2B-10M — 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