REPOGEO REPORT · LITE
Vahe1994/AQLM
Default branch main · commit e79a896e · scanned 6/27/2026, 6:27:32 PM
GitHub: 1,321 stars · 194 forks
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.
2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).
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.
- hightopics#1Add specific topics for LLM compression and quantization
Why:
COPY-PASTE FIXllm-quantization, llm-compression, large-language-models, pytorch, deep-learning, machine-learning, additive-quantization, pv-tuning
- mediumreadme#2Emphasize AQLM's core differentiator in the README introduction
Why:
CURRENT# AQLM Official PyTorch implementation for Extreme Compression of Large Language Models via Additive Quantization
COPY-PASTE FIX# AQLM Official PyTorch implementation for Extreme Compression of Large Language Models via Additive Quantization, uniquely leveraging **learned codebooks (vector quantization)** for groups of weights to achieve superior performance at very low bitrates.
- lowhomepage#3Add a homepage URL
Why:
COPY-PASTE FIXhttps://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.
- GPTQ · recommended 2×
- AWQ · recommended 2×
- LoRA · recommended 2×
- AutoGPTQ · recommended 1×
- Hugging Face Transformers · recommended 1×
- CATEGORY QUERYHow can I significantly reduce the memory footprint of large language models for efficient inference?you: not recommendedAI recommended (in order):
- GPTQ
- AutoGPTQ
- Hugging Face Transformers
- AWQ
- bitsandbytes
- GGML/GGUF
- llama.cpp
- DeepMind's Speculative Decoding
- LoRA
- QLoRA
- PEFT
- FlashAttention
- xFormers
- FlashAttention-2
- Hugging Face Trainer
- Hugging Face Optimum
- DeepSpeed
- Accelerate
AI recommended 18 alternatives but never named Vahe1994/AQLM. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhat techniques allow for extreme compression of large language models while maintaining accuracy?you: not recommendedAI recommended (in order):
- GPTQ
- AWQ
- LLM.int8()
- SparseGPT
- DistilBERT
- TinyLlama
- LoRA
- Compresso
- ALBERT
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 completenesswarn
Suggestion:
- README presencepass
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?passAI 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?passAI 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?passAI 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.
Live preview[](https://repogeo.com/en/r/Vahe1994/AQLM)<a href="https://repogeo.com/en/r/Vahe1994/AQLM"><img src="https://repogeo.com/badge/Vahe1994/AQLM.svg" alt="RepoGEO" /></a>Subscribe to Pro for deep diagnoses
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