Function Calling with Local Models - Ollama, Llama3 & Phi-3

The video discusses the use of local models like Llama 3 and Phi-3 for function calling tasks, emphasizing the benefits of running agents locally without relying on cloud services. The speaker showcases examples of setting up and utilizing these models for structured outputs and JSON responses, highlighting their potential for various tasks and encouraging viewer engagement for further exploration.

In the video, the speaker discusses using local models such as Ollama and the smaller Llama 3 8 billion model instead of relying on cloud services like Groq. The focus is on exploring function calling with these local models to build agents without the need for cloud resources. The speaker highlights the benefits of running agents locally, allowing them to operate continuously without token limitations or costs associated with cloud services.

The speaker delves into the concept of function calling and structured outputs using models like Llama 3 and Phi-3. They reference a function calling leaderboard that ranks models based on their ability to perform function calling tasks effectively. The discussion revolves around utilizing models like Llama 3 8 billion and Phi-3 for structured outputs and JSON responses, showcasing their performance compared to larger proprietary models available in the cloud.

The video demonstrates setting up Llama 3 with Ollama, showing examples of generating articles and extracting JSON responses. The importance of specifying the model format as JSON for accurate responses is emphasized. The speaker provides code snippets and explanations on how to extract structured data using Pydantic classes and JSON output parsers, showcasing the versatility of these local models for various tasks.

Furthermore, the speaker introduces Ollama functions, a new feature in LangChain that enables binding tools to models for function calling. They present examples of using Llama 3 and Phi-3 models for function calling tasks, showcasing their ability to generate function call responses based on given inputs. The Phi-3 model, although smaller, demonstrates its capability to handle structured outputs and function calling tasks effectively.

In conclusion, the video highlights the potential of using local models like Llama 3 and Phi-3 for building agents and performing function calling tasks without relying on cloud services. The speaker plans to explore building agents and RAG systems on these local models in future videos. They encourage viewer engagement by welcoming comments and questions regarding the usage of local models, tools for agents, and related topics.