REPOGEO REPORT · LITE

Vahe1994/AQLM

Default branch main · commit e79a896e · scanned 5/16/2026, 8:48:34 PM

GitHub: 1,320 stars · 194 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 Vahe1994/AQLM, 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

hightopics#1

Add relevant topics to the repository

Why:

COPY-PASTE FIX

llm-quantization, llm-compression, pytorch, additive-quantization, pv-tuning, deep-learning, machine-learning, quantized-llms

mediumreadme#2
Add a sentence to the README's opening highlighting AQLM's unique differentiator
Why:
COPY-PASTE FIX
```
AQLM achieves this through its unique activation-aware, group-wise quantization approach, designed to be outlier-free.
```
lowhomepage#3
Add a homepage URL to the repository metadata
Why:
COPY-PASTE FIX
```
https://arxiv.org/pdf/2401.06118.pdf
```

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 Vahe1994/AQLM

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

huggingface/transformers

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

huggingface/transformers · recommended 2×
huggingface/optimum · recommended 1×
microsoft/onnxruntime · recommended 1×
openvinotoolkit/openvino · recommended 1×
TimDettmers/bitsandbytes · recommended 1×

CATEGORY QUERY
How can I significantly reduce the memory footprint of large language models for deployment?
you: not recommended
AI recommended (in order):
1. Hugging Face Optimum (huggingface/optimum)
2. ONNX Runtime (microsoft/onnxruntime)
3. Intel OpenVINO (openvinotoolkit/openvino)
4. bitsandbytes (TimDettmers/bitsandbytes)
5. GPTQ (IST-DASLab/gptq)
6. AWQ (mit-han-lab/awq)
7. Neural Magic DeepSparse (neuralmagic/deepsparse)
8. PyTorch's `torch.nn.utils.prune` (pytorch/pytorch)
9. Hugging Face Transformers (huggingface/transformers)
10. DistilBERT (huggingface/transformers)
11. TinyLlama (jzhang38/TinyLlama)
12. Phi-2
13. Mistral 7B (mistralai/mistral-src)
14. Hugging Face Accelerate (huggingface/accelerate)
15. DeepSpeed (microsoft/DeepSpeed)
AI recommended 15 alternatives but never named Vahe1994/AQLM. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking methods for extreme quantization of LLMs to enable efficient inference on edge devices.
you: not recommended
AI recommended (in order):
1. GPTQ
2. AutoGPTQ (https://github.com/PanQiWei/AutoGPTQ)
3. Hugging Face transformers (https://github.com/huggingface/transformers)
4. AWQ
5. QLoRA
6. GGML/GGUF (https://github.com/ggerganov/llama.cpp)
7. ONNX Runtime (https://github.com/microsoft/onnxruntime)
8. NVIDIA TensorRT-LLM (https://github.com/NVIDIA/TensorRT-LLM)
9. DeepSpeed-MII (https://github.com/microsoft/DeepSpeed)
AI recommended 9 alternatives but never named Vahe1994/AQLM. 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 Vahe1994/AQLM?
pass
AI named Vahe1994/AQLM explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts Vahe1994/AQLM in production, what risks or prerequisites should they evaluate first?
pass
AI named Vahe1994/AQLM 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 Vahe1994/AQLM solve, and who is the primary audience?
pass
AI named Vahe1994/AQLM 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 Vahe1994/AQLM. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/Vahe1994/AQLM.svg)](https://repogeo.com/en/r/Vahe1994/AQLM)

HTML

<a href="https://repogeo.com/en/r/Vahe1994/AQLM"><img src="https://repogeo.com/badge/Vahe1994/AQLM.svg" alt="RepoGEO" /></a>

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Vahe1994/AQLM — 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