decodingai-magazine/llm-twin-course
Llm Twin Course
This project is an educational curriculum and set of technical guides for building production-ready large language model and retrieval augmented generation systems. It provides instructional materials and hands-on lessons focused on model specialization, LLMOps, and the implementation of vector databases.
The course covers the development of retrieval augmented generation systems, including tutorials on creating data pipelines that crawl, chunk, and embed content into vector stores. It includes training guides for the deployment, monitoring, and maintenance of language models in production environments.
The material explores language model fine-tuning through parameter-efficient tuning and the creation of instruction datasets. It also covers the implementation of LLMOps pipelines, including experiment tracking and the use of model registries to monitor performance.
Features
- Retrieval Augmented Generation Systems - Provides a complete framework for building retrieval augmented generation systems connected to private data.
- LLM Fine-Tuning - Covers LLM fine-tuning using both full-parameter and parameter-efficient methods.
- Specialization Workflows - Provides methods for specializing models using curated instruction-response pairs.
- Language Model Fine-Tuning - Provides a framework for creating instruction datasets and applying parameter-efficient tuning.
- Model Registries - Implements a model registry for version-controlling weights and tracking training hyperparameters.
- Parameter Efficient Fine-Tuning - Teaches parameter-efficient fine-tuning to adapt models to specific domains with minimal compute.
- Vector Store Retrievals - Implements vector store retrieval mechanisms to provide relevant document context for LLM generation.
- Real-Time Feature Computation - Ships a streaming process for real-time feature computation, including text embedding for vector stores.
- Real-time Embedding Pipelines - Implements a real-time pipeline for cleaning, chunking, and embedding content into vector stores.
- Vector Database Integrations - Implements integration layers for streaming real-time data into high-dimensional vector databases.
- LLMOps Platforms - Provides a production-ready LLMOps pipeline for training, deploying, and monitoring language models.
- RAG Application Development Courses - Offers a comprehensive educational curriculum for building production-ready LLM and RAG systems.
- LLM Performance Monitoring - Includes tools for monitoring LLM performance and evaluating output quality in production.
- Fine-Tuning Courseware - Provides instructional materials on creating datasets and applying parameter-efficient tuning.
- Observability And Monitoring - Provides instructional guides on using registries and monitoring tools to track training experiments and evaluate model outputs.
- Data Processing and ETL - Provides a data pipeline that cleans and normalizes raw social media data for database storage.
- Social Media Data Pipelines - Provides automated pipelines for the collection and storage of social media data.
- Implementation Guides - Provides a practical guide for indexing and retrieving real-time data via vector databases.
- LLMOps Guides - Provides hands-on lessons for the deployment, monitoring, and maintenance of models in production.
- RAG Implementation Guides - Provides a step-by-step technical guide for building RAG data pipelines.
- Model Inference APIs - Exposes specialized models via scalable inference APIs integrated with retrieval mechanisms.
