A curated list of practical guide resources of LLMs (LLMs Tree, Examples, Papers)
Die Hauptfunktionen von mooler0410/llmspracticalguide sind: Awesome List, LLM Development, LLM Development and Research, Research Collections, AI Development Resources, Educational Resources, Learning and Reference, Research and Surveys.
Open-Source-Alternativen zu mooler0410/llmspracticalguide sind unter anderem: hannibal046/awesome-llm — This project serves as a comprehensive, static directory of external resources dedicated to the study and application… mlabonne/llm-course — This project is a comprehensive educational curriculum and engineering handbook focused on the lifecycle of large… plexpt/awesome-chatgpt-prompts-zh — This project is a community-driven library of structured text inputs designed to guide large language models into… awesome-stable-diffusion/awesome-stable-diffusion. humanloop/awesome-chatgpt — Curated list of awesome tools, demos, docs for ChatGPT and GPT-3. papers-we-love/papers-we-love — Papers We Love is a community-driven repository and learning network dedicated to the study and discussion of…
This project serves as a comprehensive, static directory of external resources dedicated to the study and application of large language models. It functions as a centralized discovery point for developers and researchers, aggregating foundational academic papers, technical documentation, and specialized tools within a structured, version-controlled knowledge base. The repository distinguishes itself through a multi-level classification system that organizes diverse technical domains, ranging from model training frameworks and inference optimization to AI safety and hallucination detection. By
This project is a comprehensive educational curriculum and engineering handbook focused on the lifecycle of large language models. It serves as a structured knowledge base for machine learning practitioners, covering the fundamental mathematical and architectural principles of transformer-based sequence modeling, as well as the practical implementation of supervised instruction fine-tuning and preference-based model alignment. The repository distinguishes itself by providing a deep dive into advanced model composition and optimization techniques. It details methodologies for weight-space mode
This project is a community-driven library of structured text inputs designed to guide large language models into specific roles, behaviors, and operational modes. It functions as a comprehensive repository of prompt engineering resources, providing reusable templates that allow users to override default model tendencies and enforce domain-specific response patterns through instruction-following logic. The collection distinguishes itself by offering specialized persona-based directives that constrain model output to simulate professional experts or functional technical environments. By utiliz