Skip to main content
grasp.study
About
Explore
Pricing
Log in
Try it for free
Back to course
👋
Welcome to a Grasp lesson
Create a free account
to keep studying and create your own study path.
Large Language Models
·
Module 12
Model Evaluation and Deployment
1
Perplexity: A Metric for Language Model Evaluation
Define perplexity and explain its use and limitations as a metric for language model evaluation.
2
Benchmarking AI: MMLU and HELM
Describe the purpose of standardized evaluation benchmarks like MMLU and HELM for assessing broad model capabilities.
Model Quantization: Size, Speed, and Performance Tradeoffs
Explain model quantization (e.g., INT8, 4-bit) and analyze its tradeoffs between model size, inference speed, and performance.
4
Knowledge Distillation: Training Smaller, Smarter Models
Describe knowledge distillation and how a smaller student model is trained to mimic a larger teacher model for efficiency gains.
5
LLM Production Infrastructure: Latency, Throughput, and Memory
Identify key infrastructure considerations for serving LLMs in production, including latency, throughput, and memory management.
End of course,
Back to course
Previous module
Model Alignment and Preference Optimization
Next module