Oracle Cloud Infrastructure 1z0-1127-24 Passing Score

Chain-of-Thought: The first prompt calculates the total number of wheels and then uses that information to determine how many sets of wheels can be bought. This sequential reasoning process aligns with the Chain-of-Thought technique.

Least-to-most: The second prompt solves a complex problem by first identifying the needed formula and then solving a simpler version before tackling the full question. This incremental approach matches the Least-to-most technique.

Step-Back: The third prompt starts by defining greenhouse gases and then explores their impact on climate change, taking a step back to establish foundational knowledge before addressing the main question.

References

Research articles on prompting techniques for language models

Documentation on effective use of prompting strategies

The "stop sequence" parameter in the OCI Generative AI Generation models is used to specify a string that signals the model to stop generating further content. When the model encounters this string during the generation process, it terminates the response. This parameter is useful for controlling the length and content of the generated text, ensuring that the output meets specific requirements or constraints.

References

OCI Generative AI service documentation

General principles of sequence generation in AI models

The architecture of dedicated AI clusters contributes to minimizing GPU memory overhead for fine-tuned model inference by sharing base model weights across multiple fine-tuned models on the same group of GPUs. This approach allows different fine-tuned models to leverage the shared base model weights, reducing the memory requirements and enabling efficient use of GPU resources. By not duplicating the base model weights for each fine-tuned model, the system can handle more models simultaneously with lower memory overhead.

References

Technical documentation on AI cluster architectures

Research articles on optimizing GPU memory utilization in model inference

The Oracle Accelerated Data Science (ADS) class that can be used to deploy a Large Language Model (LLM) application to OCI Data Science model deployment is GenerativeAI. This class provides the necessary tools and functions to work with generative AI models, including deployment, fine-tuning, and inference capabilities. It integrates with OCI Data Science to streamline the process of deploying and managing LLM applications.

References

Oracle ADS documentation

Guides on deploying AI models using Oracle Data Science services