Repository associated with paper titled "CLP: Finetuning Vision-Language-Action Models Requires Fewer Layers Than You Think"
Contract and claim-boundary tooling for empirical AI-system experiments. Turns retained evidence into falsifiable, condition-specific decisions.
Why more compute doesn't mean faster AI : The Roofline Model explained, with an interactive calculator and Python workload analyzer
Tracking State-of-the-Art AI Models and Performance is an open-source dataset documenting AI advancements from the 1950s to today. It includes model details, organizations, compute requirements, and benchmarks. Researchers and developers can analyze trends, compare models, and contribute updates. The dataset is open for collaboration $ fostering AI
An open protocol for purposeful, respectful, and resource-conscious AI use.
Benchmarking GenAI compute efficiency through context compression and model selection.
Adaptive inference algorithm for transformers inspired by quantum collapse (SR framework)
White paper and specification for the Decentralized Universal Compute Protocol (DUCP).
Add a description, image, and links to the compute-efficiency topic page so that developers can more easily learn about it.
To associate your repository with the compute-efficiency topic, visit your repo's landing page and select "manage topics."