1 dépôt
Awesome GitHub RepositoriesFull-Stack Performance Maps
Visualizations mapping optimizations across hardware, framework, model, and service layers.
Distinct from Hardware Abstraction Layers: Existing candidates focus on specific layers (hardware or model) rather than the cross-layer performance mapping.
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Awesome Full-Stack Performance Maps GitHub Repositories
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- changyeyu/llm-rl-visualized
changyeyu/LLM-RL-Visualized
4,529Voir sur GitHubLLM-RL-Visualized is a visual reference library and collection of knowledge maps designed to explain Large Language Model and Reinforcement Learning algorithms. It provides a structured system of conceptual diagrams and taxonomies covering the intersection of language model alignment and reinforcement learning. The project distinguishes itself through detailed visual mappings of complex workflows, such as the coordination of reward models and policy optimization in reinforcement learning from human feedback. It contrasts different preference optimization architectures, such as RLHF and Direct
Maps software and hardware optimizations across service, model, framework, and hardware layers.
Python
