RRepoGEO

REPOGEO REPORT · LITE

marella/ctransformers

Default branch main · commit ed02cf4b · scanned 5/23/2026, 12:57:01 PM

GitHub: 1,888 stars · 143 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
63 /100
Needs work
Category recall
1 / 2
Avg rank #3.0 when recommended
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 marella/ctransformers, 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
    Strengthen README's opening to highlight local, efficient LLM inference

    Why:

    CURRENT
    Python bindings for the Transformer models implemented in C/C++ using GGML library.
    COPY-PASTE FIX
    CTransformers provides Python bindings for efficient, local inference of Transformer models using the C/C++ GGML library, enabling high-performance LLM execution on consumer hardware.
  • mediumhomepage#2
    Add homepage URL to About section

    Why:

    COPY-PASTE FIX
    https://github.com/marella/ctransformers
  • lowtopics#3
    Expand topics to include core technologies and use cases

    Why:

    CURRENT
    ai, ctransformers, llm, transformers
    COPY-PASTE FIX
    ai, ctransformers, llm, transformers, ggml, inference, local-llm, quantization

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
1 / 2
50% of queries surface marella/ctransformers
Avg rank
#3.0
Lower is better. #1 = top recommendation.
Share of voice
6%
Of all named tools, what % are you?
Top rival
llama.cpp
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. llama.cpp · recommended 1×
  2. llama-cpp-python · recommended 1×
  3. ONNX Runtime · recommended 1×
  4. TensorRT-LLM · recommended 1×
  5. OpenVINO · recommended 1×
  • CATEGORY QUERY
    How can I run quantized large language models efficiently in Python with a C++ backend?
    you: #3
    AI recommended (in order):
    1. llama.cpp
    2. llama-cpp-python
    3. ctransformers ← you
    4. ONNX Runtime
    5. TensorRT-LLM
    6. OpenVINO
    7. optimum-intel
    8. Apache TVM
    9. MLC LLM
    Show full AI answer
  • CATEGORY QUERY
    What's a good library for local LLM inference in Python, optimized for performance?
    you: not recommended
    AI recommended (in order):
    1. llama.cpp (ggerganov/llama.cpp)
    2. llama-cpp-python (abetlen/llama-cpp-python)
    3. Ollama (ollama/ollama)
    4. transformers (huggingface/transformers)
    5. bitsandbytes (TimDettmers/bitsandbytes)
    6. AutoGPTQ (PanQiWei/AutoGPTQ)
    7. vLLM (vllm-project/vllm)
    8. MLX (ml-explore/mlx)
    9. ONNX Runtime (microsoft/onnxruntime)

    AI recommended 9 alternatives but never named marella/ctransformers. 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 marella/ctransformers?
    pass
    AI named marella/ctransformers explicitly

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

  • If a team adopts marella/ctransformers in production, what risks or prerequisites should they evaluate first?
    pass
    AI named marella/ctransformers 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 marella/ctransformers solve, and who is the primary audience?
    pass
    AI named marella/ctransformers 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 marella/ctransformers. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/marella/ctransformers.svg)](https://repogeo.com/en/r/marella/ctransformers)
HTML
<a href="https://repogeo.com/en/r/marella/ctransformers"><img src="https://repogeo.com/badge/marella/ctransformers.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

marella/ctransformers — 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