REPOGEO REPORT · LITE

MAC-AutoML/MindPipe

Default branch main · commit 7ea160ab · scanned 6/7/2026, 8:08:24 AM

GitHub: 1,008 stars · 24 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)

4 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 MAC-AutoML/MindPipe, 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 project scope in README to counter miscategorization

Why:

COPY-PASTE FIX

Add this sentence immediately after the H1/tagline in the README: "MindPipe is a dedicated model compression and evaluation framework for LLMs and VLMs, focusing on quantization and pruning techniques for efficient deployment on GPUs and NPUs. It is not a general-purpose AutoML or MLOps platform."

highlicense#2

Add a LICENSE file to the repository

Why:

CURRENT

(no LICENSE file detected — the repo has no recognizable license)

COPY-PASTE FIX

Create a LICENSE file in the repository root with a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0) that best suits the project's goals.

mediumtopics#3

Expand repository topics with more specific keywords

Why:

CURRENT

automatic-compression, compression, deployment, evaluation, huawei-ascend-npus, large-language-models, large-vision-language-models, llama, llava, minicpm, nvidia-gpus, pruning, quantization, qwen

COPY-PASTE FIX

Add the following topics: `model-compression`, `llm-compression`, `vlm-compression`, `deep-learning-compression`, `model-optimization`, `quantization-aware-training`, `post-training-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

0 / 2

0% of queries surface MAC-AutoML/MindPipe

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

ONNX Runtime

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

ONNX Runtime · recommended 2×
bitsandbytes · recommended 1×
AWQ · recommended 1×
GPTQ · recommended 1×
Hugging Face Transformers (Trainer API) · recommended 1×

CATEGORY QUERY
How to effectively compress large language models for efficient deployment on various hardware?
you: not recommended
AI recommended (in order):
1. bitsandbytes
2. AWQ
3. GPTQ
4. Hugging Face Transformers (Trainer API)
5. PyTorch (torch.nn.utils.prune)
6. Hugging Face Optimum
7. vLLM
8. TensorRT
9. ONNX Runtime
10. llama.cpp
AI recommended 10 alternatives but never named MAC-AutoML/MindPipe. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What tools help unify quantization and pruning for large vision and language models on diverse accelerators?
you: not recommended
AI recommended (in order):
1. OpenVINO Toolkit
2. NVIDIA TensorRT
3. NVIDIA AMMO (Automated Mixed Model Optimization) toolkit
4. ONNX Runtime
5. PyTorch
6. TensorFlow
7. TensorFlow Model Optimization Toolkit
8. DeepSparse
AI recommended 8 alternatives but never named MAC-AutoML/MindPipe. 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 MAC-AutoML/MindPipe?
pass
AI named MAC-AutoML/MindPipe explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts MAC-AutoML/MindPipe in production, what risks or prerequisites should they evaluate first?
pass
AI named MAC-AutoML/MindPipe 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 MAC-AutoML/MindPipe solve, and who is the primary audience?
pass
AI named MAC-AutoML/MindPipe 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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MAC-AutoML/MindPipe — 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