Report "Images Per Second" (IPS) improvements on standard consumer hardware.
Highlight the reduction in model weight (e.g., from ~300MB to ~30MB).
Use ImageNet-V2 and ImageNet-A to see if quantization introduces "hallucinations" or brittleness. 💡 Key Arguments to Develop Parameter Efficiency: clip56mp4
How does the 4-bit quantization affect the embedding space compared to FP16?
A "solid paper" on would likely examine its efficiency as a lightweight vision-language model, specifically focusing on its 4-bit quantization (P4) and how it retains performance despite having only 56 million parameters . 📄 Proposed Title: Report "Images Per Second" (IPS) improvements on standard
🏗️ Research Framework 1. Core Objective
is roughly 1/3 the size of base models; argue its viability for "Always-on" AI features. 💡 Key Arguments to Develop Parameter Efficiency: How
Determine the "accuracy tax" paid for the extreme quantization. 2. Key Research Questions