30 Questions
Which chapter of the book discusses post-training optimizations on generative AI models?
Chapter 8
What are some techniques discussed in Chapter 8 for post-training optimizations on generative AI models?
Pruning, quantization, and distillation
What are some considerations when deploying LLMs?
Compute budget and storage costs
What is the intended experience when interacting with a deployed model?
Faster inference speed
What is an example of a post-deployment task for LLMs?
Tuning deployment configurations
What is one factor to consider when selecting compute resources for deployment?
Model performance
What is the purpose of distillation in post-training optimizations?
To improve model accuracy
Which technique aims to reduce the size of a generative AI model for deployment optimization?
All of the above
What is the primary benefit of reducing the size of a generative AI model for deployment?
All of the above
Which technique converts a model's weights from high-precision to lower precision?
Quantization
What does pruning aim to eliminate from a model?
All of the above
Which type of pruning removes entire columns or rows of the weight matrices?
Structured pruning
Which technique trains a smaller student model from a larger teacher model?
Distillation
What is the name of the popular distilled student model introduced in the text?
DistilBERT
Which method aims to transform a model's weights to a lower precision representation with the goal of reducing the model's size and compute requirements for hosting LLMs?
Quantization
Which quantization method is performed post-training to optimize for deployment?
Post-training quantization (PTQ)
What is the purpose of post-training quantization (PTQ)?
To reduce the model's size and memory footprint
Which quantization approach has a higher impact on model performance?
Quantization of both model weights and activations
What is the purpose of the calibration step in dynamic range post training quantization?
To identify the dynamic range of values on input
What is the trade-off often associated with quantization?
Reduced model performance
What is the purpose of distillation in model training?
To reduce the model's size and number of computations
Which predictions are compared against the ground truth labels to calculate the student loss?
Hard predictions
What is the combination of distillation loss and student loss used for?
Updating the student models' weights
What is the purpose of distillation in the context of teacher-student models?
To transfer information from teacher to student
Why may distillation be less effective for generative decoder models compared to encoder models like BERT?
The output space is relatively large for decoder models
What are the two types of predictions compared to calculate the student loss?
Hard predictions and ground truth hard labels
What is the combination of distillation loss and student loss used to minimize?
The combination of losses
What type of models may benefit more from distillation compared to generative decoder models?
Encoder models like BERT
What is the difference between the student loss and the distillation loss?
The student loss compares hard predictions with ground truth hard labels
How are the student models' weights updated using the combination of distillation loss and student loss?
Using standard backpropagation
Test your knowledge on model optimization techniques like pruning and quantization. Learn how these methods can help reduce model size and improve computational efficiency.
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