REPOGEO REPORT · LITE
Cornell-RelaxML/quip-sharp
Default branch main · commit 1d8f873e · scanned 6/2/2026, 4:08:14 AM
GitHub: 594 stars · 51 forks
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 Cornell-RelaxML/quip-sharp, 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.
- highabout#1Add a clear, concise description for the repository
Why:
COPY-PASTE FIXQuIP# is a state-of-the-art post-training weight-only quantization method for Large Language Models (LLMs), achieving extreme compression (<= 4 bits per weight) with high performance. Note: This codebase is no longer under active development; see QTIP for our latest work.
- hightopics#2Add relevant topics to improve categorization
Why:
COPY-PASTE FIXllm-quantization, large-language-models, deep-learning, machine-learning, quantization, compression, pytorch, cuda
- mediumreadme#3Clarify project status for AI parsing in the README
Why:
CURRENT## 🚨 Our latest method, QTIP, uses trellis quantization to achieve even higher quality quantized models. This codebase is no longer under active development.
COPY-PASTE FIX## 🚨 Important: This codebase is no longer under active development. Our latest method, QTIP, uses trellis quantization to achieve even higher quality quantized models.
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.
- pytorch/pytorch · recommended 3×
- GPTQ · recommended 1×
- AWQ · recommended 1×
- QLoRA · recommended 1×
- SpQR · recommended 1×
- CATEGORY QUERYHow to achieve state-of-the-art extreme compression for large language model weights?you: not recommendedAI recommended (in order):
- GPTQ
- AWQ
- QLoRA
- SpQR
- SparseGPT
- Hugging Face's `transformers` library with `Trainer`
- DistilBERT
- ALBERT
- MobileNetV3/V2
- TinyLlama
AI recommended 10 alternatives but never named Cornell-RelaxML/quip-sharp. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhat methods enable fast inference with highly quantized large language models on GPUs?you: not recommendedAI recommended (in order):
- NVIDIA TensorRT (NVIDIA/TensorRT)
- vLLM (vllm-project/vllm)
- llama.cpp (ggerganov/llama.cpp)
- Hugging Face Optimum (huggingface/optimum)
- ONNX Runtime (microsoft/onnxruntime)
- Intel OpenVINO (openvinotoolkit/openvino)
- DeepSpeed-MII (microsoft/DeepSpeed-MII)
- PyTorch (pytorch/pytorch)
- torch.compile (pytorch/pytorch)
- torch.quantization (pytorch/pytorch)
AI recommended 10 alternatives but never named Cornell-RelaxML/quip-sharp. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenessfail
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 Cornell-RelaxML/quip-sharp?passAI named Cornell-RelaxML/quip-sharp explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts Cornell-RelaxML/quip-sharp in production, what risks or prerequisites should they evaluate first?passAI named Cornell-RelaxML/quip-sharp 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 Cornell-RelaxML/quip-sharp solve, and who is the primary audience?passAI named Cornell-RelaxML/quip-sharp 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 Cornell-RelaxML/quip-sharp. 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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Cornell-RelaxML/quip-sharp — 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