REPOGEO REPORT · LITE
horseee/LLM-Pruner
Default branch main · commit 128a07d9 · scanned 6/30/2026, 2:47:44 PM
GitHub: 1,130 stars · 134 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 horseee/LLM-Pruner, 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.
- highreadme#1Add a benefit-oriented statement to the README's introduction
Why:
COPY-PASTE FIXAdd this line directly after the `<h3>On the Structural Pruning of Large Language Models<h3>` tag: "A powerful and efficient tool for structurally pruning Large Language Models (LLMs) to achieve significant size reduction and faster inference, supporting models like Llama-3/3.1, Llama-2, BLOOM, and more."
- mediumreadme#2Highlight LLM-Pruner's core differentiators in the README
Why:
COPY-PASTE FIXWithin the "Why LLM-Pruner" section, or a new "Key Features" section, add a sentence like: "LLM-Pruner stands out by performing *structural pruning* of FFN neurons and attention heads, often achieving significant compression *without requiring extensive fine-tuning* to recover performance, a key advantage over many other compression methods."
- lowtopics#3Add relevant optimization topics
Why:
CURRENTbaichuan, bloom, chatglm, compression, language-model, llama, llama-2, llama3, llm, neurips-2023, pruning, pruning-algorithms, vicuna
COPY-PASTE FIXbaichuan, bloom, chatglm, compression, language-model, llama, llama-2, llama3, llm, neurips-2023, pruning, pruning-algorithms, vicuna, model-optimization, inference-optimization, llm-optimization
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 1×
- AWQ · recommended 1×
- bitsandbytes · recommended 1×
- Hugging Face Transformers · recommended 1×
- DistilBERT · recommended 1×
- CATEGORY QUERYHow can I reduce the size of large language models for faster inference?you: not recommendedAI recommended (in order):
- GPTQ
- AWQ
- bitsandbytes
- Hugging Face Transformers
- DistilBERT
- TinyBERT
- PyTorch's torch.nn.utils.prune
- NVIDIA's APEX
- ALBERT
- Longformer
- Reformer
- Performer
- MobileNet
- EfficientNet
- FlashAttention
AI recommended 15 alternatives but never named horseee/LLM-Pruner. This is the gap to close.
Show full AI answer
- CATEGORY QUERYSeeking a method to structurally prune LLMs to optimize their deployment and performance.you: not recommendedAI recommended (in order):
- Hugging Face Optimum (huggingface/optimum)
- 🤗 Transformers (huggingface/transformers)
- PyTorch Pruning (pytorch/pytorch)
- DeepSpeed (microsoft/DeepSpeed)
- NVIDIA Apex (NVIDIA/apex)
- TensorFlow Model Optimization Toolkit (tensorflow/model-optimization)
- OpenVINO (openvinotoolkit/openvino)
- ONNX Runtime (microsoft/onnxruntime)
AI recommended 8 alternatives but never named horseee/LLM-Pruner. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesspass
- 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 horseee/LLM-Pruner?passAI named horseee/LLM-Pruner explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts horseee/LLM-Pruner in production, what risks or prerequisites should they evaluate first?passAI named horseee/LLM-Pruner 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 horseee/LLM-Pruner solve, and who is the primary audience?passAI named horseee/LLM-Pruner 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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horseee/LLM-Pruner — 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