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LLM-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
This project is an educational course and set of instructional materials for building large language models from scratch using Python. It provides a step-by-step guide and practical tutorials focused on the internal mechanics of transformer architectures and pre-training workflows. The repository features a framework for implementing and comparing diverse model families, including Llama, GLM, and RWKV. It uses a configuration-driven assembly approach to analyze the structural differences and internal mechanisms of these various architectures. The codebase covers the complete development pipe
The main features of mistralai/cookbook are: Courses and Tutorials, Learning Resources.
Projects with overlapping indexed features include: datawhalechina/llms-from-scratch-cn — This project is an educational course and set of instructional materials for building large language models from… iusztinpaul/hands-on-llms — 🦖 𝗟𝗲𝗮𝗿𝗻 about 𝗟𝗟𝗠𝘀, 𝗟𝗟𝗠𝗢𝗽𝘀, and 𝘃𝗲𝗰𝘁𝗼𝗿 𝗗𝗕𝘀 for free by designing, training, and deploying a… andysingal/llm-course. changyeyu/llm-rl-visualized — LLM-RL-Visualized is a visual reference library and collection of knowledge maps designed to explain Large Language… fareedkhan-dev/all-rag-techniques. karminski/one-small-step — One Small Step is an educational resource that explains core AI and large language model concepts through short,…