FAST Local Live LLM Preview Window – Phi-2 / Mistral 7B Uncensored
In this video, All About AI demonstrates a fun project called the Local Real-Time LLM Preview. This project allows users to get a real-time live preview of language model outputs using local models like uncensored Phi-2 and Mistral 7B. Here’s a detailed breakdown of the video:
1. **Introduction**: The video starts with a demonstration of the project, showing how different programming languages can be used to print ‘hello’ using a real-time preview window. The project captures user input and updates the preview in real-time using local LLMs.
2. **Flowchart Explanation**: The presenter explains the flow of the project using a flowchart. The process involves capturing user input via keyboard, sending the input to a local LLM, and displaying the output in real-time. The project uses threading to run the update preview function in the background, allowing simultaneous input capture and processing.
3. **Python Code Overview**: The video dives into the Python code used for the project. Key functions include:
– `m7B_function`: Runs the local server from LM Studio using models like Phi-2 and Mistral 7B.
– `update_preview`: Updates the preview window based on user input.
– `capture_input`: Captures keyboard inputs and triggers the preview update based on spacebar presses.
4. **Live LLM Preview Tests**: The presenter conducts several tests to demonstrate the functionality of the project:
– **Test 1**: Shows how the preview updates with each spacebar press, displaying information about mountains.
– **Test 2**: Demonstrates using the project to write a concise email with a more explicit language model.
– **Test 3**: Further tests the explicit language model with different inputs, showing the real-time updates.
5. **Model Switching and System Prompts**: The video also shows how to switch models in LM Studio and use different system prompts to alter the behavior of the language model.
6. **Conclusion**: The video wraps up by highlighting the fun and simplicity of the project, encouraging viewers to try it out and join the community for more resources and support.
This project showcases the capabilities of local LLMs and provides a practical example of how to use them for real-time applications.