3 repositorios
Awesome GitHub RepositoriesLLM Application Development Curricula
Structured educational materials for mastering the creation of large language model applications.
Distinct from LLM Application Development: Focuses on the educational curriculum aspect rather than the development frameworks themselves.
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Awesome LLM Application Development Curricula GitHub Repositories
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- datawhalechina/llm-universe
datawhalechina/llm-universe
13,269Ver en GitHubllm-universe is a structured learning resource and technical guide focused on the development of large language model applications. It serves as a curriculum for mastering model orchestration, the creation of autonomous conversational agents, and the implementation of retrieval-augmented generation systems. The project provides detailed instructions on connecting model APIs with memory and tools to create execution chains. It specifically covers the construction of retrieval pipelines, including the process of cleaning raw documents, generating embeddings, and integrating vector databases to
Serves as a comprehensive structured learning resource for mastering LLM application development.
Jupyter Notebooklangchainrag - afshinea/stanford-cme-295-transformers-large-language-models
afshinea/stanford-cme-295-transformers-large-language-models
4,509Ver en GitHubEste proyecto es una guía de estudio y referencia técnica integral para aprender las arquitecturas y métodos de entrenamiento de Transformers y modelos de lenguaje grandes (LLM). Sirve como una visión general técnica para entender cómo las redes neuronales procesan datos y cómo alinear el comportamiento del modelo con objetivos de rendimiento específicos. El repositorio proporciona guías especializadas en varias áreas clave del desarrollo de modelos. Esto incluye referencias detalladas para arquitecturas de transformadores, frameworks de implementación para generación aumentada por recuperación (RAG) y flujos de trabajo de agentes, y guías técnicas para la optimización y ajuste fino (fine-tuning) de modelos. El contenido cubre una amplia gama de capacidades, incluyendo ajuste fino supervisado, adaptación de bajo rango (LoRA) y alineación basada en preferencias. También aborda la eficiencia del modelo mediante cuantización, destilación y arquitecturas de mezcla de expertos (MoE), junto con el estudio de mecanismos de auto-atención y flash attention. Los recursos y muestras se proporcionan para el desarrollo en C#, JavaScript y Python.
Provides structured educational materials for developing retrieval-augmented generation and reasoning models.
- curiousily/get-things-done-with-prompt-engineering-and-langchain
curiousily/Get-Things-Done-with-Prompt-Engineering-and-LangChain
1,242Ver en GitHubThis project is an educational collection of Jupyter notebooks and guides focused on building applications with the LangChain framework. It serves as a practical resource for developers learning to implement prompt engineering, retrieval-augmented generation, and autonomous agent workflows to create intelligent, context-aware systems. The repository distinguishes itself by providing hands-on tutorials for connecting language models to private datasets and external tools. It covers the end-to-end process of designing structured input templates, orchestrating multi-step task sequences, and main
Provides structured educational materials for mastering the development of language model applications using custom data and automated workflows.
Jupyter Notebookartificial-intelligencechatgptdeep-learning
