Retraining vs RAG vs Context: Your Local Data on LLMs!
Dave demonstrates adding knowledge files to language models, comparing retraining, retrieval augmented generation, and context documents, while showcasing model performance on different hardware.
MAIN POINTS FROM TRANSCRIPT
- Explanation of adding documents to models using retraining, retrieval augmented generation, and context window.
- Demonstration of local and online model integration with user documents.
- Performance comparison between 1 billion and 70 billion parameter models on powerful hardware.
TAKEAWAYS
- Models can be enhanced with personal documents for improved knowledge and response accuracy.
- Smaller models can run efficiently on high-performance hardware, achieving over 300 tokens per second.
- Larger models require significantly more resources but offer increased capabilities.