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Hannibal046/Awesome-LLM

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Awesome LLM

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Features

  • Model Evaluation Frameworks - Locating specialized tools and methodologies for detecting hallucinations, ensuring model security, and aligning system behavior with human preferences.
  • Resource Directories - The project functions as a static directory of external references, providing a centralized discovery point for distributed technical documentation.
  • Open Models - Open LLM — a named example documented in this learning resource.
  • Application Frameworks - Finding practical tools, frameworks, and datasets to build, evaluate, and deploy custom language model applications for specific industry use cases.
  • Training Frameworks - LLM Training Frameworks — a named example documented in this learning resource.
  • Artificial Intelligence Curated Lists - Awesome-Code-LLM — a named example documented in this learning resource.
  • Artificial Intelligence Resource Collections - A comprehensive collection of categorized resources, research papers, and development tools focused on the advancement of large language models.
  • Curated Lists - Awesome-LLM-Inference — a named example documented in this learning resource.
  • Taxonomy Systems - Resources are organized into a multi-level classification system to facilitate navigation through diverse technical domains and research topics.
  • Training and Optimization Utilities - Identifying resources for fine-tuning, compressing, and aligning large language models to improve performance while managing computational and data requirements.
  • LLM Learning Resources - LLM Tutorials and Courses — a named example documented in this learning resource.
  • Prompt Engineering Resources - Accessing curated collections of effective prompts and reasoning techniques to improve the quality and reliability of model outputs for complex tasks.
  • Evaluation Frameworks - LLM Evaluation: — a named example documented in this learning resource.
  • Inference Engines - LLM Inference — a named example documented in this learning resource.
  • Research Papers - Discovering foundational papers, academic reading lists, and emerging research trends to stay current with the latest developments in artificial intelligence.
  • Research Papers - Milestone Papers — a named example documented in this learning resource.
  • Community Curation Platforms - Information is maintained and updated through collaborative contributions from external users via pull requests and issue tracking.
  • Human Preference Datasets - RWKV-howto - possibly useful materials and tutorial for learning RWKV. - ModelEditingPapers - A paper & resource list on model editing for large language models.
  • Language Model Research Lists - Awesome Language Model Analysis — a named example documented in this learning resource.
  • Prompt Engineering Lists - Instruction-Tuning-Papers - A trend starts from Natrural-Instruction (ACL 2022), FLAN (ICLR 2022) and T0 (ICLR 2022). - LLM Reading List - A paper & resource list of large language models. - Reasoning using Langu
  • Regional Language Model Lists - LLM4Opt - Applying Large language models (LLMs) for diverse optimization tasks (Opt) is an emerging research area. This is a collection of references and papers of LLM4Opt.
  • Security Research Lists - Awesome-Align-LLM-Human - A collection of papers and resources about aligning large language models (LLMs) with human.
  • Technical Documentation Indexes - A structured directory providing developers and researchers with direct access to essential documentation, training frameworks, and evaluation benchmarks.
  • Web Application Lists - Awesome Llm Webapps — a named example documented in this learning resource.

    • 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 maintaining a community-driven curation model, the directory ensures that its collection of tutorials, datasets, and prompt engineering techniques remains current with emerging research trends and industry developments.

    Beyond its core indexing capabilities, the project covers a broad spectrum of practical resources, including guidance on model alignment, human preference datasets, and domain-specific applications such as healthcare and code generation. The entire knowledge base is structured as a hierarchical collection of links and summaries, providing a collaborative hub for mastering natural language processing.