Base vs. Instruct Models: When to Use Which

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-model-families-AI-and-base-vs-instruct-models-creators

1. What is the core idea behind "Base vs. Instruct Models: When to Use Which"?

   1. Why base models still matter and when instruct-tuned models are wrong.
   2. Choose modality-specific tools when general models underperform
   3. trade-offs
   4. Maintain a refusal-test corpus per category

2. Which term best describes a foundational idea in "Base vs. Instruct Models: When to Use Which"?

   1. instruct model
   2. base model
   3. RLHF
   4. raw completion

3. A learner studying Base vs. Instruct Models: When to Use Which would need to understand which concept?

   1. base model
   2. RLHF
   3. instruct model
   4. raw completion

4. Which of these is directly relevant to Base vs. Instruct Models: When to Use Which?

   1. base model
   2. instruct model
   3. raw completion
   4. RLHF

5. Which of the following is a key point about Base vs. Instruct Models: When to Use Which?

   1. Use base models for completion-style, deterministic tasks.
   2. Use instruct for assistants, dialogue, and tool use.
   3. Fine-tune base when you need a domain-specific model.
   4. Choose modality-specific tools when general models underperform

6. What is one important takeaway from studying Base vs. Instruct Models: When to Use Which?

   1. Easily make instruct models stop being chatty.
   2. Use base models conversationally without scaffolding.
   3. Choose modality-specific tools when general models underperform
   4. trade-offs

7. What is the key insight about "Model selection guide" in the context of Base vs. Instruct Models: When to Use Which?

   1. Choose modality-specific tools when general models underperform
   2. trade-offs
   3. For task <T>, output: model type (base/instruct), reason, fine-tuning recommendation, eval suite to use.
   4. Maintain a refusal-test corpus per category

8. What is the key insight about "Base model outputs surprise users" in the context of Base vs. Instruct Models: When to Use Which?

   1. Choose modality-specific tools when general models underperform
   2. trade-offs
   3. Maintain a refusal-test corpus per category
   4. Base models will continue prompts in unexpected ways. Never expose them directly to end users without wrapping.

9. Which statement accurately describes an aspect of Base vs. Instruct Models: When to Use Which?

   1. Instruct models are the default for chat, but base models win for completion-style tasks and custom tuning.
   2. Choose modality-specific tools when general models underperform
   3. trade-offs
   4. Maintain a refusal-test corpus per category

10. Which best describes the scope of "Base vs. Instruct Models: When to Use Which"?

    1. It is unrelated to model-families workflows
    2. It focuses on Why base models still matter and when instruct-tuned models are wrong.
    3. It applies only to the opposite beginner tier
    4. It was deprecated in 2024 and no longer relevant

11. Which section heading best belongs in a lesson about Base vs. Instruct Models: When to Use Which?

    1. Choose modality-specific tools when general models underperform
    2. trade-offs
    3. What AI does well here
    4. Maintain a refusal-test corpus per category

12. Which section heading best belongs in a lesson about Base vs. Instruct Models: When to Use Which?

    1. Choose modality-specific tools when general models underperform
    2. trade-offs
    3. Maintain a refusal-test corpus per category
    4. What AI cannot do

13. Which of the following is a concept covered in Base vs. Instruct Models: When to Use Which?

    1. base model
    2. instruct model
    3. RLHF
    4. raw completion

14. Which of the following is a concept covered in Base vs. Instruct Models: When to Use Which?

    1. base model
    2. instruct model
    3. RLHF
    4. raw completion

15. Which of the following is a concept covered in Base vs. Instruct Models: When to Use Which?

    1. base model
    2. instruct model
    3. RLHF
    4. raw completion