Llama Model Family Quiz

Answers and explanations appear as you take the quiz.

1. How does Meta describe access to Llama model weights, and what governs use?
   • Weights are publicly released for eligible versions, subject to each version’s license and use terms
   • Every version is public domain with unrestricted use
   • Only Meta employees can access any Llama weights
   • The weights are downloadable without any terms
2. What does the acronym 'Llama' stand for?
   • Linear Learning And Multimodal Architecture
   • Large Language Model Meta AI
   • Layered Logic And Machine Analysis
   • Local Language Management Application
3. Which Llama release first added a license permitting broad commercial use?
   • The original Llama 1
   • Llama 405 only
   • Llama 2
   • None of them allow commercial use
4. What architecture underlies Llama models?
   • A convolutional image classifier
   • A decision tree ensemble
   • A recurrent network with no attention
   • A decoder-only transformer predicting the next token
5. How are Llama 'chat' or instruction-tuned variants made to follow user prompts?
   • Through supervised fine-tuning and reinforcement learning from human feedback
   • By increasing the screen resolution
   • By deleting the base model entirely
   • By translating all prompts to Latin first
6. In what deployment setup can running Llama locally avoid sending prompts to a hosted inference service?
   • Whenever a model is downloaded, even when an app sends prompts to cloud tools
   • When inference runs on-device or on a private local server without a prompt-forwarding service
   • Only when the model is fine-tuned rather than used for inference
   • When the application stores prompts in a shared hosted API
7. Which attention design did Meta adopt in Llama 3 8B and 70B to improve inference efficiency?
   • Residual vector quantization
   • Spatially-adaptive normalization
   • Grouped-query attention (GQA)
   • A recurrent state-space scan
8. What inputs can Llama 3.2 Vision models accept?
   • Audio waveforms only
   • Video frames only, with no text
   • Sensor data without language or images
   • Text and images