This repository provides a collection of Jupyter notebooks designed to accompany the "Building LLMs for Production" book by Towards AI. It serves as a practical guide for developers and researchers looking to implement Retrieval-Augmented Generation (RAG) and other advanced LLM techniques in production environments. The notebooks cover a wide range of topics from foundational Transformer architectures to fine-tuning and evaluation.

How It Works

The notebooks demonstrate practical implementation of various LLM concepts using popular libraries like LangChain and LlamaIndex. They cover core RAG components such as text splitting, prompt engineering, and agent-based systems. The content progresses from basic LLM application building to advanced topics like multi-modal data handling, fine-tuning strategies (QLoRA, RLHF), and performance benchmarking.

Quick Start & Requirements

• Install: Typically requires pip install -r requirements.txt (file not provided in README).
• Prerequisites: Python environment, Jupyter Notebook/Lab. Specific library versions are not detailed.
• Resources: Assumes access to compute resources for running LLM operations, potentially including GPUs for fine-tuning and inference.
• Links: No direct links to setup guides or demos are provided in the README.

Highlighted Details

• Comprehensive coverage of RAG implementation with LangChain and LlamaIndex.
• Notebooks dedicated to prompt engineering, including few-shot learning and output parsing.
• Examples for building LLM-powered agents and multi-modal applications.
• Detailed sections on LLM fine-tuning techniques (LIMA, QLoRA, RLHF) and dataset creation.
• Includes notebooks on RAG metrics, evaluation, and LangSmith integration.

Maintenance & Community

• Maintained by Towards AI.
• No specific community links (Discord, Slack) or roadmap details are present in the README.

Licensing & Compatibility

• The README does not specify a license.

Limitations & Caveats

The README does not provide a requirements.txt file, making precise environment setup challenging. Specific hardware requirements (e.g., GPU memory for fine-tuning) are not detailed, and the setup time is not estimated. The lack of explicit licensing information may pose a barrier for commercial use.