🚧 Under Construction - Stay Tuned for Cutting-Edge Updates! 🛠️🔍
Welcome to LLMetaLab, your comprehensive hub for understanding and building with Large Language Models (LLMs). Here, we explore everything from foundational concepts to cutting-edge research, practical applications, and hands-on projects. Each module is designed to help you master the technologies driving LLMs, including Retrieval-Augmented Generation (RAG), model alignment, multi-modal integrations, and much more.
LLMetaLab aims to:
- 🧠 Empower Knowledge: Deliver a rich repository of resources for everyone, from AI novices to experienced practitioners.
- 🔧 Build with Purpose: Equip you with tutorials and hands-on projects for real-world LLM applications.
- 🤝 Foster Collaboration: Encourage contributions, community engagement, and shared learning.
Below is an overview of our main content areas. Feel free to explore each module to understand the depth and scope of what LLMetaLab offers.
RAG combines the power of retrieval systems with generative models to create more accurate, context-driven responses. Here’s what you’ll find:
- Concepts: Learn about RAG concepts, including its core workflow and components, like retrieval models and vector databases.
- Tutorials: Step-by-step guides to implement RAG using tools like Pinecone, Weaviate, and FAISS.
- Projects: Example projects that demonstrate RAG in real-world scenarios, like building an FAQ chatbot or a medical data Q&A system.
- Tools & Libraries: Guides to set up and leverage tools for efficient RAG system deployment.
- Benchmarks: Metrics to evaluate RAG model performance, such as retrieval accuracy and response latency.
- FAQ: Answers to common RAG-related questions and troubleshooting tips.
Progress: 🟢 Completed
Ensuring AI behaves as intended is crucial for safe deployment. We cover:
- Concepts: Learn about alignment principles, RLHF, and adversarial testing for safe AI.
- Tutorials & Projects: Guides on safe model deployment, reducing bias, and building ethically-aligned systems.
Progress: 🟠 In Progress
Make LLMs more adaptable to your specific needs through techniques like fine-tuning and parameter-efficient training.
- Concepts: Learn about different tuning methods such as LoRA and PEFT.
- Tutorials & Projects: Step-by-step guides and examples for domain-specific fine-tuning.
Progress: 🟠 In Progress
Explore how LLMs interact with data beyond text, like images, videos, and audio.
- Concepts, Tutorials & Projects: Step-by-step guides to use models like CLIP and Whisper for multi-modal applications.
Progress: 🟠 In Progress
- Causal Inference: Understand causality in AI models, complete with tutorials and project ideas.
- Explainability: Learn about making LLM outputs more interpretable through attention visualization and saliency maps.
- Scalability and Efficiency: Techniques for deploying LLMs on edge devices and improving efficiency with pruning and quantization.
- Memory-Augmented Architectures: Dive into memory-based models for enhanced conversational continuity.
Progress: 🟠 In Progress
- Healthcare: Applications in diagnostics and patient interactions.
- Legal AI Systems: Automating legal contract review and ensuring regulatory compliance.
Progress: 🟠 In Progress
- Neurosymbolic Approaches: Combining symbolic reasoning with LLMs for richer reasoning capabilities.
- Rationalization Techniques: Creating human-like, logically consistent explanations for model outputs.
Progress: 🟠 In Progress
- Ethics and Governance: Creating ethical frameworks and governance standards for responsible AI.
- Human-AI Collaboration: Enhancing how humans and AI interact effectively.
Progress: 🟠 In Progress
- Prompt Engineering: Master the art of crafting effective prompts to optimize model responses.
- Open-Source Contributions: Guidelines for engaging in community-driven projects.
Progress: 🟠 In Progress
- Model Deployment and MLOps: Best practices for deploying LLMs using Docker, Kubernetes, and CI/CD.
- Data Engineering: Building scalable data pipelines and ensuring data quality for LLMs.
- Experimentation and Evaluation: Methods for tracking model performance and comparing across iterations.
- Legal and Ethical Expertise: Understanding AI-related regulations like GDPR and ensuring ethical compliance.
Progress: 🟠 In Progress
To maximize your learning, follow this path:
- Start with the Main Repository Overview (
README.md) - Core Technological Areas (e.g., RAG, Fine-Tuning, Multi-Modal Learning)
- Supporting Tools (e.g., Prompt Engineering, MLOps)
- Advanced Research Topics (e.g., Explainability, Causal Inference)
- Industry Applications (e.g., Healthcare, Legal)
- Specialized Techniques (e.g., Neurosymbolic Approaches, Rationalization)
- Interdisciplinary Frontiers (e.g., Ethics, Human-AI Interaction)
- Practical Projects and Contributions
By following this learning path, you'll build a foundation and progress to advanced research and real-world applications, gaining comprehensive expertise in LLM technologies.
- Prerequisites: Ensure you have Python, PyTorch, Docker, etc., installed.
- Setup Guide: Follow setup_guide.md to get started.
- Contribution Guide: Learn how to contribute to LLMetaLab using our contribution_guide.md.
For a comprehensive reference of the full folder structure, see repository_structure.md.
- Please adhere to relevant licensing guidelines for responsible use.
Thank you for visiting LLMetaLab. We hope this repository serves as a valuable resource for all your LLM endeavors. If you have suggestions or want to contribute, feel free to open an issue or pull request! 🤝
Let's innovate, collaborate, and pioneer the next wave of language model technologies together. 🌍🚀