Choosing Local Model for Vision

Final Quiz

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Assessment

1. What distinguishes a vision-language model from a text-only language model? A. It can process visual input and produce text-based understanding or structured output B. It requires an internet connection for every inference C. It can only generate captions D. It guarantees factual visual answers

2. What should be defined first when selecting a local vision model? A. The model with the largest parameter count B. The specific use case, required output, constraints, and acceptable errors C. The most popular leaderboard D. The longest available context window

3. Which resource most directly constrains whether a GPU can hold a local vision model and its active workload? A. Monitor resolution B. Disk label C. Available VRAM or unified memory D. Keyboard polling rate

4. When can a smaller model be the better operational choice? A. Only when image quality does not matter B. Only when no accelerator is present C. Never; larger models always produce better systems D. When it meets quality needs with better latency, stability, and hardware headroom

5. What does model quantization primarily change? A. Numerical precision, memory use, and often inference performance B. The physical image resolution captured by a camera C. The model license D. The number of benchmark images

6. Which statement about Q8 and Q4 quantization is most accurate? A. Q8 is always faster and more accurate B. Q8 is not automatically better; the complete configuration must be tested on the target task C. Q4 cannot process images D. Quantization has no effect on memory use

7. Why should image-token or visual-context settings be included in a benchmark configuration? A. They replace the need for representative images B. They only affect output formatting C. They can affect visual detail, memory use, latency, and model results D. They guarantee correct OCR

8. How can thinking or reasoning mode affect perception-heavy tasks? A. It always improves object counting B. It removes visual hallucinations C. It has no effect on output behavior D. It can add unsupported interpretation and reduce performance on simple visual extraction

9. What is a visual hallucination? A. A claim about an object, text, or relationship that is not supported by the image B. A temporary drop in GPU clock speed C. A lossless image format D. A required preprocessing step

10. Why should the same model configuration be run more than once during evaluation? A. Repeated runs increase the model size B. Repeated runs reveal inconsistency, intermittent failures, and stability differences C. Repeated runs eliminate the need for a rubric D. Repeated runs guarantee production readiness

11. What makes a local vision benchmark representative? A. It uses only attractive sample images B. It tests one image with several prompts C. It reflects normal, difficult, and costly-error cases from the intended workflow D. It copies a public leaderboard exactly

12. Which metric captures whether a model successfully returns usable output across the test set? A. Parameter count B. Download size C. Prompt length D. Completion rate

13. What should be recorded to make a local model benchmark reproducible? A. Model, adapter, quantization, runtime, prompt, image settings, generation settings, and hardware B. Only the model family name C. Only the final accuracy score D. Only the operating-system version

14. Which deployment practice best supports responsible use of local vision models? A. Retain every image indefinitely B. Apply purpose limits, access controls, retention rules, monitoring, and human review for important decisions C. Automatically act on every model response D. Ignore model and runtime updates

15. Which statement best summarizes local vision model selection? A. The largest model is the safest default B. One public benchmark can permanently identify the best model C. Choose the tested configuration that meets task quality, reliability, hardware, privacy, and operational needs D. Quantization and runtime settings can be ignored