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Welcome back to Total Technology Zone! This is tutorial 101, where we'll explore how to use LLaMA 3 models with LangChain. We'll demonstrate how to run these models on your personal computer or laptop, without relying on cloud services, Hugging Face, or OpenAI API. This tutorial is driven by your requests and aims to provide a straightforward guide to using LLaMA 3 models locally.
Objectives:
1. **Download and Set Up LLaMA 3**:
- Visit the official website to download LLaMA 3.
- Install and configure the model on your local machine.
2. **Running the LLaMA 3 Model**:
- Launch the model with the command `olama run llama3:8b` (for the 8 billion parameter model). Adjust this based on your system’s resources.
3. **Testing the Model**:
- Interact with the model by sending simple text prompts and verifying the responses.
4. **Using LangChain with LLaMA 3**:
- Integrate LLaMA 3 with LangChain to run complex queries and generate outputs.
- Implement a basic script to invoke the model and handle responses.
Detailed Steps:
1. **Download and Installation**:
- **Website Navigation**: Guide users to the official LLaMA 3 download page.
- **Installation Steps**: Step-by-step instructions to download and install LLaMA 3 on a local machine.
2. **Launching the Model**:
- **Command Execution**: Instructions on how to start the model using `olama run llama3:8b`.
- **Model Loading**: Explanation of the model loading process and initial setup.
3. **Initial Testing**:
- **Basic Interaction**: Send a simple prompt (e.g., 'Hi') to test the model's responsiveness.
- **Performance Insights**: Discuss how the model's performance depends on system resources.
4. **LangChain Integration**:
- **Setup LangChain**: Import necessary modules from LangChain.
- **Invoke Model**: Create a script to send prompts to the LLaMA 3 model and retrieve responses.
- **Example Queries**: Provide example queries such as 'Write a joke in English' or 'Write an article on LLM and NLP'.
Observations:
- **Performance Considerations**: Discuss the performance impact of using larger models like the 70 billion parameter version on systems with limited resources.
- **Response Times**: Highlight expected response times based on GPU and memory availability.
- **Local vs. Cloud**: Contrast the benefits and drawbacks of running models locally versus using cloud services.
Conclusion:
By the end of this tutorial, you will have a functional setup for running LLaMA 3 models on your local machine using LangChain. This setup allows for extensive experimentation and development without incurring cloud service costs.
Call to Action:
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Thank you for watching, and stay tuned for more real-world use cases and advanced tutorials. Happy learning
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