Source : Nvdia Blogs
Foundation models are large neural networks trained on massive amounts of data in an unsupervised manner to gain a broad knowledge base and natural language abilities.
They serve as the foundation for various AI applications like text generation, code analysis, image/video creation, speech recognition, etc.
Notable examples are OpenAI's GPT models (including GPT-4), Google's Gemma/CodeGemma, Meta's Llama 2, and Stable Diffusion image/video generators.
Foundation models can be fine-tuned and customized for specialized tasks through transfer learning instead of training new models from scratch.
There are language models (LLMs) focused on text, image generators, and multimodal models handling text+images together.
Techniques like retrieval-augmented generation (RAG) enhance their capabilities by allowing data retrieval during inference.
Apps built on foundation models can now run locally on NVIDIA GPUs rather than cloud services for data privacy and low latency.
Running locally enables customization by connecting to personal data sources while ensuring privacy and security.
Use cases span gaming, customer service, creative projects by leveraging these powerful yet lightweight foundation models.
The key emphasis is on the versatility, accessibility and localized deployment benefits of foundation models underpinning the new wave of generative AI applications across industries
RAG vs Fine-Tuning
The main differences in RAG vs. fine-tuning lie in complexity, architectural design, use cases, and model customization. That said, the choice between the two LLM learning approaches should be based on the available resources, the need for customization, and the nature of the data. This way, you can tailor your preferred LLM learning technique to their specific needs.
It’s important to note that RAG and fine-tuning are not rivals. Although both LLM learning approaches have their strengths and weaknesses, combining them may be the best solution for your organization. Fine-tuning a model for a particular task and then enhancing its performance with retrieval-based mechanisms may be exactly what you need for a successful LLM project.
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Enhancing you RAG performance using HyDE
HyDE” – Hypothetical Document Embeddings. It’s like a clever assistant for search engines, enhancing their ability to find information with precision
1) Generating hypothetical documents
2) Unsupervised encoding
3) Retrieval process
Advantages of HyDE
HyDE offers several benefits:
1. Zero-shot retrieval: HyDE doesn’t need a sizable dataset of labeled samples to function “out of the box.”
2. Cross-lingual: It is appropriate for multilingual search applications since it functions well in a variety of languages.
3. Flexibility: HyDE’s methodology enables it to adjust to various jobs without requiring a great deal of fine-tuning.