Exploring and comparing different LLMs [Pt 2] | Generative AI for Beginners

Summary

In this episode of 'Dive into AI for Beginners' at Microsoft Learn, Pablo Lopez and Carlota Cuchó explore the basics of foundation models versus LLMs, discuss classifications of language models, and delve into Azure AI. They cover open-source versus proprietary models, the importance of embeddings, and the use of language models for generation tasks. Carlota introduces Azure AI Studio, explaining how it can be used to test and manage AI applications, emphasizing the iterative process of model selection, testing, and deployment.

Takeaways

• 😀 Foundation models are the basis for deploying new solutions and are pre-trained, generalized, adaptable, large, and self-supervised.
• 🔍 Large Language Models (LLMs) are a subset of foundation models, fulfilling all the characteristics mentioned, but not all foundation models are LLMs as some may not use language.
• 🌐 Open-source language models have expanded significantly, offering access to parts of the training process, but they may lack continuous updates and support.
• 🔒 Proprietary models, often provided by cloud services, offer constant updates and security but limit the ability to fine-tune the models.
• 📚 Language models can be categorized based on their function, such as embedding conversion, text generation, or specialized tasks like code optimization.
• 🌐 Azure AI Studio is a platform for developing, testing, and managing AI applications, integrating data technologies and a wide range of language models.
• 🔎 The Model Catalog in Azure AI Studio allows users to find foundation models using filters like provider, task, license, and model name.
• 🛠️ Model Benchmark in Azure AI Studio enables the comparison of different models based on predefined performance metrics like accuracy and coherence.
• 📈 Prompt engineering, retrieval augmented generation, and fine-tuning are complementary techniques for optimizing LLMs, each with its own advantages and use cases.
• 💡 Fine-tuning is a process that customizes an LLM for a specific task, updating its weights and biases, and is ideal for strict latency requirements or high-quality data availability.
• 🚀 Training your own LLM from scratch is a complex task requiring extensive data, expertise, and computational power, suitable only for very domain-specific use cases.

Q & A

Who are the hosts of the session?

The hosts are Pablo Lopez and Carlota Cucho, both Cloud Advocates focused on artificial intelligence at Microsoft.

What is the main focus of the session?

The main focus is on understanding and comparing different large language models (LLMs) and foundation models, as well as exploring Azure AI.

What are foundation models?

Foundation models are large models pre-trained on a vast amount of data and designed to perform multiple tasks. They are generalized, adaptable, large, and self-supervised.

What distinguishes LLMs from foundation models?

LLMs are a subset of foundation models focused on language processing. While all LLMs are foundation models, not all foundation models are LLMs since some foundation models may not use language.

What are the differences between open-source and proprietary language models?

Open-source models are accessible and modifiable but may lack extensive support and updates. Proprietary models are easier to use and often more reliable, but they limit customization and fine-tuning.

What is the significance of embeddings in language models?

Embeddings convert data into a format that can be easily processed by language models, enabling them to understand and generate text, interact with various systems, and enhance tasks like search and retrieval.

How does Azure AI Studio support the use of foundation models?

Azure AI Studio provides a platform for developing, testing, and managing AI applications, integrating various models, tools for responsible AI development, and facilities for prompt engineering, evaluation, and monitoring.

What options are available for deploying models in Azure AI Studio?

Models can be deployed using real-time endpoints in your Azure subscription or as a 'pay as you go' service through REST APIs, offering flexibility in managing the infrastructure.

What is retrieval-augmented generation (RAG) in the context of AI models?

RAG is a technique that augments language model prompts with external data, expanding the model's knowledge and improving performance without needing extensive fine-tuning.

Why might a business choose to fine-tune a language model?

Fine-tuning customizes a language model for specific tasks, improving its performance and accuracy using high-quality data and ground truth labels. It is ideal for scenarios with strict latency requirements.