Practice Training Llms: Data, Objectives, And Scaling Laws (15.4) - Modern Topics – LLMs & Foundation Models
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Training LLMs: Data, Objectives, and Scaling Laws

Practice - Training LLMs: Data, Objectives, and Scaling Laws

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does CLM stand for and what does it do?

💡 Hint: Think about how a sentence continues.

Question 2 Easy

Name one challenge when selecting data sources for training LLMs.

💡 Hint: Consider what might impact fairness in models.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does Causal Language Modeling (CLM) focus on?

Predicting masked words
Generating next words
Summarizing text

💡 Hint: It's about continuing a sentence.

Question 2

True or False: Masked Language Modeling is used in GPT models.

True
False

💡 Hint: Think about which model uses masking.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Design an LLM training pipeline that addresses potential biases in the dataset while ensuring diverse data representation.

💡 Hint: Consider diversity as a strategy to enhance model fairness.

Challenge 2 Hard

Evaluate the trade-offs between model size and training time for an LLM. How can infrastructure be optimized to manage these trade-offs?

💡 Hint: Think about the efficiency of computation as model sizes increase.

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Reference links

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