Llama 3 8B: BIG Step for Local AI Agents! - Full Tutorial (Build Your Own Tools)
TLDRThe tutorial demonstrates the capabilities of a local AI agent using the Llama 3 8B model. The agent showcases its ability to search the web using Google, extract information, and store it in a 'vault'. It also sends an email with the collected data and performs a context check within the vault. The presenter explains the setup of the AI system, including creating custom functions like 'send email' and 'search Google', and how the system interprets and executes function calls based on user input. The agent uses the 'parse function call' to detect and execute instructions within the AI's response. The tutorial also guides on adding new functions, such as 'write to notes', to the system. The presenter is impressed with the Llama 3 8B's responsiveness and its ability to follow instructions locally, which is considered a significant step for local AI agents.
Takeaways
- 🔍 The AI agent can perform a Google search using a specific query, such as 'llama 3 human eval', and scrape URLs from sources like AI meta and The Verge.
- 📚 The scraped content is added to a 'vault' which allows the agent to search within the collected data for specific information.
- 📧 The agent has the capability to send emails, which can be used to share information, such as the number of tokens Llama 3 was trained on.
- 💡 Llama 3 was trained on up to 15 trillion tokens, showcasing its vast training data.
- 📈 Llama 3 continues to improve log linearly even after being trained on such a large number of tokens, indicating its powerful learning capabilities.
- 🛠️ The AI system uses a custom script with good instructions to function without relying on Lang chain or similar technologies.
- 📝 The system includes functions like 'send email' and 'search Google', which are utilized based on user queries and system intelligence.
- 🔗 The system can parse function calls within its responses to execute specific actions, such as searching the web or checking the context.
- 📝 The 'check context' function allows the agent to search within the vault for relevant information based on user queries.
- 📦 The AI uses a JSON-like structure to understand and execute instructions, translating it into a simple Python dictionary for processing.
- 📝 The system maintains a conversation history to keep context, which aids in providing more accurate and relevant responses.
- ✍️ A new 'write to notes' function is introduced, allowing the agent to append content to a text file named 'notes.txt'.
Q & A
What is the first tool the AI agent uses in the tutorial?
-The first tool the AI agent uses is 'search Google', which allows the agent to perform a search query on Google and collect information.
How does the AI agent collect information from the web?
-The AI agent collects information by scraping URLs from search results and embedding the content into a 'rag' system, which is then stored in the agent's 'vault'.
What is the query used to demonstrate the search functionality?
-The query used to demonstrate the search functionality is 'llama 3 human eval'.
How many tokens was Llama 3 trained on according to the context from the vault?
-Llama 3 was trained on up to 15 trillion tokens.
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 specific information, such as the number of tokens Llama 3 was trained on.
How does the AI agent determine which function to use based on user input?
-The AI agent uses a 'parse function call' structure to detect specific keywords in the user input that trigger a function call, and then it generates a function call with the appropriate parameters based on the user's query.
What is the significance of setting the temperature to zero in the context of the Llama 3 model?
-Setting the temperature to zero makes the Llama 3 model's responses more deterministic, reducing randomness in the output and making it more predictable.
How does the AI agent handle the execution of a function call?
-The AI agent uses a special function called 'parse function call' to detect a secret instruction note in the AI's response, which is then translated into a simple dictionary that the system understands. The system executes the function based on the instructions in the dictionary.
What is the 'check context' function used for?
-The 'check context' function is used to search the AI agent's 'rag' system for specific information based on a query provided by the user.
How does the AI agent keep track of the conversation history?
-The AI agent maintains a conversation history by appending all interactions between the user and the assistant to a history log, which helps to keep the context of the conversation.
What is the new function the presenter adds to the AI agent's toolkit?
-The presenter adds a function called 'Write to notes', which allows the AI agent to write or append content to a text file named 'notes.txt'.
How can users access the full code demonstrated in the tutorial?
-Users can access the full code by becoming a member of the channel, which grants them an invitation to the community GitHub and access to the community Discord.
Outlines
🔍 Introduction to AI Agent's Functionality
The video begins with an introduction to the AI agent's capabilities, emphasizing its ability to use various tools. The primary tool discussed is the search function, which uses Google to find information based on user queries. The agent is shown to scrape URLs from AI meta and The Verge, adding their content to a 'vault' for later search and retrieval. The agent is also capable of sending emails with the information found. The speaker expresses satisfaction with the performance of the 'llama 3' model, noting its efficiency in following instructions and its local operation without relying on Lang chain.
📧 Function Execution and AI System's Intelligence
The second paragraph delves into how the AI system executes function calls based on user inputs. It explains the process of searching Google for 'llama 3' from Meta AI, preparing a response that includes a natural language reply and a 'secret instruction note'. The AI system is portrayed as intelligent, capable of understanding and processing user requests to search the web. The paragraph also introduces the 'parse function call', a special function that detects and executes instructions wrapped in specific tags, showcasing the system's ability to interpret JSON-like code and execute the corresponding actions.
📝 Surveillance Part and Adding New Functions
The third paragraph focuses on the surveillance aspect of the system, which monitors the output from the chat function for specific wrapper tags that signal a function call. It outlines the process of adding new functions to the system, using the example of a 'Write to notes' function that appends content to a text file. The speaker demonstrates how to update the system message and the 'convert to open AI function' to accommodate the new function. The paragraph concludes with a live test of the newly added function, successfully writing an email address to the 'notes.txt' file.
🚀 Final Showcase and Future Content Tease
In the final paragraph, the speaker recaps the system's capabilities and shares excitement about the responsiveness of the llama 3 model. They highlight the success of the functions and express optimism about the potential of AI agents. The paragraph ends with a teaser for upcoming content featuring Gro and the Llama 370b model, and an invitation for viewers to join the channel's community for access to full code and further discussions.
Mindmap
Keywords
💡Llama 3 8B
💡Search Google
💡RAG (Retrieval-Augmented Generation)
💡Vault
💡Email Function
💡Custom Script
💡Function Call
💡Parse Function Call
💡null
💡Local AI Agents
💡AMA (Ask Me Anything)
💡Notes File
Highlights
The AI agent can use various tools, including searching Google and sending emails.
A query 'llama 3 human eval' is used to demonstrate the search functionality.
Content from AI meta and The Verge is scraped and added to the agent's vault.
The agent can fetch information, such as the number of tokens Llama 3 was trained on, from its vault.
Llama 3 was trained on up to 15 trillion tokens.
The agent successfully sent an email with the information about Llama 3's training.
Llama 38b is shown to be powerful and responsive when running locally without Lang chain.
The system has a function to send emails, search Google, and check context within its vault.
The AI system uses a custom script with good instructions to perform tasks.
The system can parse user input to generate function calls for specific actions.
The 'parse function call' acts as a detective to monitor the AI's response for secret instruction notes.
The system translates special code into a simple list or dictionary that it can understand and execute.
The AI agent can search Google for specific terms and save the top search results.
The system maintains a conversation history to keep context for future interactions.
A new function 'Write to notes' is added to the system to append content to a text file.
The system can understand and act on user inputs that contain specific keywords like 'search Google' or 'write note'.
The system demonstrates the ability to write information, such as an email address, to a notes file.
The tutorial showcases the setup and functionality of creating custom functions for the AI agent.
The AI agent's performance is considered a big step for local AI agents, highlighting the capabilities of the Llama 38b model.