Llama 3 8B: BIG Step for Local AI Agents! - Full Tutorial (Build Your Own Tools)

The tutorial video showcases the capabilities of a local AI agent using the Llama 3 8B model. The agent demonstrates the ability to perform web searches using Google, collect and embed information from web pages into a 'vault', and search within this vault. It also features a tool to send emails with the gathered information. The presenter explains how to set up custom function calls for the AI to follow, such as searching the web, checking context, and sending emails. The AI's understanding and response to instructions are highlighted, showing its effectiveness in executing tasks based on user queries. Additionally, the video includes a demonstration on how to add new functions, such as writing to a notes file, and emphasizes the impressive performance of the Llama 3 8B model for local AI applications without relying on LangChain. The tutorial is designed to guide users on how to build their own tools and understand the code logic that combines with the AI model for creating intelligent systems.

Takeaways

• 🔍 The AI agent can perform a Google search using a specific query, such as 'llama 3 human eval', and scrape URLs from websites like AI meta and The Verge.
• 📧 The agent has the capability to send emails with the information it gathers, as demonstrated by sending an email to the presenter's address.
• 📈 Llama 3 was trained on up to 15 trillion tokens, showcasing the model's vast training data.
• 💾 The content from web pages is stored in a 'vault' from where it can be searched and utilized.
• 📝 The system uses a custom script with good instructions to perform tasks without relying on Lang chain.
• 🛠️ The AI has a 'check context' function that allows it to search its internal knowledge base, the 'rag' system, for specific information.
• 📚 The agent can create and manage a text file called 'notes.txt' where it can write or append content as instructed by the user.
• 🔗 The system can parse function calls from the AI's response, which are wrapped in specific tags, and execute the corresponding actions.
• 📈 The AI's performance in following instructions and executing tasks is considered a significant step forward for local AI agents.
• 📦 The system is designed to be modular, allowing users to add their own functions to extend its capabilities.
• ⚙️ The AI uses a combination of natural language processing and structured data (like JSON) to understand and act on user instructions.

Q & A

What is the first tool the AI agent uses in the tutorial?

The first tool the AI agent uses is the search Google function to collect information from the web.

How does the AI agent collect and store information from the web?

The AI agent scrapes URLs from search results, collects the content, and embeds it into a 'rag' system, which is then used for further searches.

What is the purpose of the 'send mail' tool in the AI agent's toolkit?

The 'send mail' tool allows the AI agent to send emails containing the information it has found or processed.

How many tokens was the Llama 3 model trained on according to the context from the vault?

The Llama 3 model was trained on up to 15 trillion tokens.

What is the significance of the 'parse function call' in the AI system?

The 'parse function call' acts as a detective within the system, monitoring the AI's response for a secret instruction note wrapped in function call tags, and executing the appropriate function based on the instructions inside.

How does the AI system understand and respond to the user's request for a web search?

The AI system identifies keywords like 'search Google' in the user's input, prepares a function call with the user's query, and then executes the search Google function to find and return the top search results.

What is the role of the 'check context' function in the AI agent's operations?

The 'check context' function is used to search the rag system for specific information based on user queries, allowing the AI to retrieve relevant data from its stored context.

How does the AI agent handle adding new functions to its toolkit?

The AI agent can be programmed to add new functions by updating its system message to recognize specific user input commands, defining the function in the code, and integrating it into the chat function for execution.

What is the 'Write to notes' function used for in the tutorial?

The 'Write to notes' function is used to append content provided by the user into a text file named 'notes.txt'.

How does the AI agent maintain context during interactions?

The AI agent maintains context by appending all interactions from both the user and the assistant to a conversation history, which helps in keeping track of the ongoing discussion.

What is the significance of using the Llama 38b model in the AI agent's setup?

The Llama 38b model is significant because it demonstrates the ability to follow instructions effectively and perform complex tasks like function calling without relying on Lang chains, showcasing its power for local AI agents.

Outlines

00:00

🔍 Introduction to AI Agent's Search and Email Tools

The video begins with an introduction to the AI agent's capabilities, focusing on the search tool that utilizes Google to find information. The agent is shown to collect data from web pages, such as AI meta and The Verge, and store it in a 'vault'. The content is then searchable within the system. The agent also demonstrates the ability to send emails with the gathered information. The speaker expresses satisfaction with the performance of the Llama 3 model and mentions plans to delve into the code and logic behind the AI agent's functionality in the video.

05:01

📚 Function Execution and AI's Intelligent Response

The second paragraph explains the execution of functions within the AI system. When a user requests a search, the AI interprets the request and prepares a function call. The system uses the Llama 38b model to understand and execute the search query effectively. The AI provides a natural language response and a 'secret instruction note' that guides the system to perform the desired action. The 'parse function call' acts as a detective, searching for the instruction note and translating it into a format the system can understand, leading to the execution of the search and subsequent actions like saving information and notifying the user.

10:04

📝 Surveillance Part and Adding New Functions

The third paragraph delves into the surveillance aspect of the system, where the 'function call' variable takes the chat function's output and processes it through 'parse function call'. The system is shown to maintain a conversation history for context. The speaker demonstrates the system's responsiveness to user inputs, such as searching for local models and checking the context for specific information. The paragraph also guides on how to add new functions, using 'Write to notes' as an example, which involves appending content to a text file and updating the system message and function list accordingly.

15:05

📧 Testing New Functions and Wrapping Up

The final paragraph showcases the system's ability to perform new functions, such as searching Google for an email address and writing it to a notes file. The speaker tests the 'Write to notes' function by commanding the AI to write an email address to a text file, which is then verified as successful. The video concludes with an invitation for viewers to access the full code by joining the channel's community, promising more examples and a video featuring Gro and the Llama 370b model in the near future.

Keywords

💡Llama 3 8B

Llama 3 8B refers to a specific model of an AI agent that is capable of performing various tasks such as searching the internet, sending emails, and accessing a 'rag' (presumably a database or information repository). In the video, it is showcased as a significant step for local AI agents due to its ability to follow instructions and perform complex tasks without relying on large-scale infrastructure like Lang Chain.

💡Search Google

This is a function within the AI system that allows it to search the internet using Google. The AI uses this function to find and collect information from specified queries, which is then stored in its 'vault' or database. For example, the script mentions using 'Search Google' to look up 'llama 3 human eval' and then storing the results in the system's memory.

💡Vault

The term 'vault' in this context refers to the AI system's internal database where it stores and retrieves information. The script describes how web page content is 'embedded' into the vault after being scraped from URLs, allowing the AI to later search and access this information.

💡Function Call

A 'function call' is a command that the AI system uses to execute specific actions. The script details how the AI creates and understands these function calls, which are formatted in a special way with 'wrapper tags' to trigger the desired action. For instance, when the AI needs to search Google or send an email, it generates a function call with the appropriate parameters.

💡Parse Function Call

This is a special function within the AI system that acts as a 'detective', constantly monitoring the AI's responses for 'secret instruction notes' wrapped in function call tags. When it detects these notes, it translates the instructions into a format the system can understand and then executes the corresponding function. It plays a crucial role in the AI's ability to follow instructions and perform tasks.

💡Local AI Agents

Local AI agents are AI models that operate on a local level, without the need for extensive cloud-based infrastructure. The video discusses the advancements in these agents, particularly the Llama 3 8B model, which is capable of performing sophisticated tasks locally. This is seen as a significant development for the field of AI.

💡Dolphin Tre Version

The 'Dolphin Tre Version' refers to a specific iteration or version of the Llama 3 model that the AI system is using. The script mentions that the system is 'very happy' with this version, indicating it has favorable performance characteristics for the tasks it's designed to perform.

💡RAG (Retrieval-Augmented Generation)

While not explicitly defined in the script, RAG is likely an acronym for 'Retrieval-Augmented Generation', a technique used in AI where a model first retrieves relevant information from a database and then uses that information to generate its response. In the context of the video, the AI uses RAG to search its vault for specific information.

💡Email Function

The 'Email Function' is a capability of the AI system that allows it to send emails. The script demonstrates this function by showing how the AI can be instructed to send an email with specific information, such as the number of tokens Llama 3 was trained on, to a given email address.

💡Custom Script

A 'custom script' refers to a piece of software code that is written for a specific purpose. In the video, the AI system uses a custom script to perform its various functions. This script is designed to follow instructions and interact with the AI model to execute tasks like searching the web, sending emails, and managing the vault.

💡Token

In the context of AI and machine learning, a 'token' typically refers to a unit of data, such as a word or a character, that the model uses to process and understand language. The script mentions that Llama 3 was trained on up to 15 trillion tokens, highlighting the vast amount of data the model has been exposed to during its training.

Highlights

The agent has been equipped with tools to search Google, send emails, and check context from a knowledge vault.

A query 'llama 3 human eval' was used to demonstrate the agent's ability to search and retrieve information.

Content from AI meta and The Verge was scraped and added to the vault for context-based searches.

Llama 3 was trained on up to 15 trillion tokens, showcasing its vast training data.

The agent successfully sent an email with the information about Llama 3's training tokens.

The tutorial explains how to set up a custom script with the Llama 38b model to follow instructions without using Lang chain.

The system can execute function calls based on user instructions, such as searching Google or checking context.

A 'parse function call' acts as a detective to identify and execute secret instruction notes from the AI's response.

The AI system translates special code into a simple Python dictionary that the system can understand and act upon.

The system appends all assistant and user messages to a conversation history to maintain context.

The agent can search for and retrieve AMA models using Google, showcasing its ability to find specific information.

The 'check context' function allows the agent to query the knowledge vault for specific information.

The system can trigger an email function to send information to a specified email address.

The Llama 38b model's responsiveness to instructions is considered a significant step for local AI agents.

The tutorial demonstrates adding a new function called 'Write to notes' to append content to a text file.

The system message is updated to include logic for the new 'Write to notes' function.

The 'Write to notes' function was successfully tested and appended content to 'notes.txt'.

The video concludes with an invitation to join a community GitHub for full code access and further learning.