Llama-4 First Look & Hands On Testing (Code, Vision, Dialogue)
AI Summary
Video Summary: Testing Llama 4 AI Models
- Introduction to Llama 4 Models:
- Release of the Llama 4 family including three models:
- Behemoth: 288 billion active parameters, designed for intelligent teaching.
- Maverick: 17 billion active parameters, natively multimodal, with a million context length.
- Scout: 17 billion active parameters, designed to run on a single GPU (H100).
- Testing Methods:
- Initial tests conducted using Meta AI platform:
- Synthwave Game Generation: Generated Python script for a retro synthwave game.
- Initial limitations noted (e.g., naming the Pygame window).
- Improved version included power-ups and better graphics.
- Roleplay Testing: Engaged in casual inquiries to evaluate model adaptability.
- Multimodal Testing: Explored image generation and critique, also attempted OCR on game loop error.
- Performance Insight:
- Results indicate successful game generation and basic functionalities.
- Some limitations in image critique and OCR functionalities observed.
- Future Expectations:
- Anticipation for better quantized models for local testing.
- Hopes for future releases of smaller models and more innovative features (e.g., audio capabilities).