LLM FREE: Como rodar no seu PC Agentes de IA CrewAI e Ollama

CANAL SANDECO

27 Aug 202414:07

Summary

TLDRProfessor Sandeco Macedo teaches viewers how to run a language model locally on their computers to reduce costs associated with AI agents. He guides through downloading 'olama' software and the Lhama 3.1 model, then demonstrates integrating it with Crew AI to create a love letter agent. The video highlights the benefits of local deployment, such as cost reduction and data protection, and encourages viewers to join a WhatsApp group for further support.

Takeaways

😀 The video is a tutorial by Professor Sandeco Macedo, aimed at teaching viewers how to run a large language model (LLM) locally on their computers.
💻 The tutorial focuses on reducing costs associated with AI agents by using Crew AI with the LLM on a local machine.
🌐 The video mentions downloading necessary software from hama.com for Windows, Linux, or Mac systems.
📚 Sandeco is a professor and researcher at the Federal University of Goiás and an ambassador for Campus in Brazil, promoting the use of intelligent agents in daily life.
🔧 The video provides a step-by-step guide to installing the LLM, starting with downloading the software and proceeding to model installation.
📈 Sandeco demonstrates downloading the 'Lhama 3.1' model, which is one of several available options, highlighting the flexibility in model choice.
💬 The video includes an interactive session where Sandeco converses with the LLM, showcasing its capabilities in understanding and responding to queries.
🔗 Sandeco explains how to connect the LLM with Crew AI, mentioning the installation of 'leng chain' for integration.
💌 A practical example is given where Sandeco instructs the LLM to write a love letter, emphasizing the application of AI in creative tasks.
🔑 The video highlights the benefits of running an LLM locally, such as significantly reduced costs and enhanced data privacy.

Q & A

What is the main purpose of the video?
-The main purpose of the video is to guide viewers on how to run a large language model (LLM) locally on their computer to reduce costs associated with using cloud-based AI services.
Who is the presenter of the video?
-The presenter of the video is Professor Sandeco Macedo, a professor and researcher at the Federal University of Goiás, and an ambassador for Campus Party in Brazil.
What is the first step to run an LLM locally as described in the video?
-The first step is to download the necessary software from hama.com and install it on the user's computer.
Which operating systems are supported for the installation of the LLM software?
-The software supports Windows, Linux, and Mac operating systems.
What model of LLM does the video focus on?
-The video focuses on the Llama 3.1 model, specifically the 2.8b version.
How can viewers access the free mentorship on the book 'Crew Ai' mentioned in the video?
-Viewers can access the free mentorship by following the link provided in the video description.
What is the command used to check if the Llama software is installed correctly?
-The command used to check the installation of Llama is 'olama --version'.
How is the Llama model downloaded and installed in the video?
-The Llama model is downloaded using the command 'olama pull' followed by the exact model name, and then installed by running the downloaded file.
What is the benefit of running an LLM locally as mentioned in the video?
-Running an LLM locally reduces costs significantly and provides data protection by keeping sensitive data within the user's machine.
How does the video demonstrate the integration of Llama with Crew AI?
-The video demonstrates integration by showing the process of installing the 'leng-chain-olama' package, importing necessary libraries, and setting up an agent within the Crew AI framework to perform a task, such as writing a love letter.
What task does the agent created in the video perform?
-The agent created in the video is tasked with writing a love letter to a specified recipient, expressing eternal love and deep affection.

Outlines

00:00

💻 Installing LLM on Local Machine

The speaker, Professor Sandeco Macedo, introduces himself as a professor and researcher at the Federal University of Goiás, and an ambassador for Campus in Brazil. He aims to teach viewers how to use intelligent agents in their daily lives. He addresses a common question about running a large language model (LLM) on one's own computer without using the chat API. He proceeds to demonstrate the process in a free mentorship video about his recently launched book 'Crew AI'. The first step is to access the website 'olama.com' to download the software. The video shows the process of downloading the software for Windows, Linux, or Mac. After downloading, the file is installed through a standard Windows installation process. The next step involves downloading the 'Lhama 3.1' model from the website, which is one of several available models. The process of downloading and installing the model is shown, including using command prompts to check for installation and to pull the model.

05:02

🤖 Interacting with the LLM and Preparing for Integration

Once the LLM is installed, Professor Sandeco demonstrates how to interact with it by running it through the command prompt and conversing with it. He tests the LLM's understanding by asking it to explain 'Transformers,' which initially leads to a humorous misunderstanding as the LLM thinks he's referring to the animated series. He corrects this and asks about 'Transformers' in the context of neural networks, which the LLM correctly identifies as a model for processing sequential data like text, voice, and images. The professor then moves on to discuss how to connect the LLM with 'Crew AI' by installing 'leng chain olama' using either 'pip' or 'poet' commands. He provides a step-by-step guide on importing necessary libraries into code and setting up an agent within 'Crew AI' to write a love letter. The process involves defining the agent's task, connecting to the LLM server running locally, and specifying the model used.

10:04

💌 Writing a Love Letter with Crew AI and LLM

Professor Sandeco continues by detailing the process of creating a Crew within 'Crew AI' that includes the writer agent tasked with composing a love letter. He emphasizes the importance of setting the correct model and server connection details for the LLM. The video then shows the execution of the Crew, which involves inputting the recipient's name and desired sentiments to be included in the love letter. The professor corrects a variable name error and successfully runs the Crew, resulting in the creation of a love letter saved as 'amor.md'. He reviews the letter, which is filled with expressions of eternal love and deep affection. The video concludes with the professor highlighting the benefits of running an LLM locally, such as cost reduction and data protection. He invites viewers to join a WhatsApp group for support and ends with a warm farewell, encouraging viewers to subscribe to his channel.

Mindmap

Keywords

💡LLM

LLM stands for Large Language Model, which refers to advanced AI models designed to understand and generate human-like text based on vast amounts of data. In the context of the video, the speaker discusses running an LLM locally on one's computer to reduce costs associated with AI agents, indicating the practical application of these models in daily tasks.

💡Crew AI

Crew AI seems to be a platform or framework mentioned in the video for integrating and managing AI agents. The speaker talks about configuring Crew AI with an LLM on a local computer, suggesting that it's a tool for enhancing productivity or efficiency through intelligent automation.

💡Local Installation

Local installation refers to the process of downloading and setting up software directly on one's own computer rather than relying on cloud-based services. The video script emphasizes the benefits of installing an LLM locally, such as reduced costs and data protection, showcasing a step-by-step guide on how to do so.

💡Hama

Hama appears to be a specific tool or platform mentioned for downloading models, possibly related to AI or machine learning. The script describes downloading 'olama' from 'hama.com', indicating it as a resource for obtaining necessary components for setting up an LLM locally.

💡Model Download

Model download is a process mentioned in the script where the speaker selects and downloads an LLM model, specifically 'Lhama 3.1', from a repository. This step is crucial for equipping the local setup with the necessary AI capabilities to function as intended.

💡Command Prompt

The command prompt is a Windows-based command-line interface that allows users to execute commands directly. In the video, the speaker uses the command prompt to install and interact with the LLM, demonstrating a technical approach to setting up and using AI tools locally.

💡Data Protection

Data protection is a concept discussed in the video, highlighting the benefits of running an LLM locally. By keeping data and processing on one's own machine, the speaker implies increased control and security over sensitive information, which is a significant advantage over cloud-based solutions.

💡Cost Reduction

Cost reduction is a key benefit mentioned for running an LLM locally. The video suggests that by installing and using AI models on one's own computer, the ongoing costs associated with cloud-based AI services can be significantly reduced, making it a more economical solution.

💡Leng Chain

Leng Chain seems to be a component or tool used in the video for connecting the LLM with the Crew AI platform. The speaker discusses installing 'leng chain olama' to facilitate this connection, indicating it as a necessary step for integrating the local LLM with the broader AI system.

💡Agent

In the context of the video, an 'agent' refers to a functional component within the Crew AI system that performs specific tasks. The speaker configures an 'agent' to write a love letter, illustrating how these agents can be programmed to automate various tasks using the capabilities of the LLM.

💡Sequential Process

A sequential process is mentioned in relation to the setup and execution of tasks within the Crew AI system. The speaker describes a 'sequential basic process' for creating and executing an agent's task, suggesting a step-by-step approach to task automation using the AI system.

Highlights

Professor Sandeco Macedo introduces the concept of running a Large Language Model (LLM) locally to reduce costs associated with AI agents.

Macedo is a professor and researcher at the Federal University of Goiás and an ambassador for Campus in Brazil.

He aims to teach the everyday use of intelligent agents through a free mentorship on his book 'Crew AI'.

The video provides a step-by-step guide to downloading and installing the LLM on a local computer.

Downloading the LLM is done through the hama.com website, with options for Windows, Linux, and Mac.

Macedo demonstrates downloading the Lhama 3.1 model, an 8b model, and explains that other models are also available.

He shows how to verify the installation of the LLM using the command prompt on Windows.

Macedo explains how to interact with the LLM through the command line, showcasing a conversation with the model.

He humorously notes the LLM's confusion between the term 'Transformers' in the context of neural networks and the animated series.

Macedo proceeds to explain the process of connecting the LLM with Crew AI using the leng chain.

He details the installation of leng chain for Lhama, either through Pipe or Poet, depending on the user's preference.

The video includes a coding tutorial on how to import necessary libraries and set up an agent using Crew AI.

Macedo illustrates creating an agent tasked with writing a love letter, demonstrating the practical application of the LLM.

He emphasizes the importance of setting up a local server connection to the LLM for Crew AI to interact with it.

The video concludes with a live demonstration of the LLM writing a love letter, showcasing the successful integration of Crew AI and the LLM.

Macedo highlights the benefits of running an LLM locally, such as reduced costs and enhanced data protection.

The video ends with a call to action for viewers to subscribe to the channel and join a WhatsApp group for support on Crew AI.