B. Temperature does not affect vocabulary size. C. Temperature introduces variability, not determinism. D. Temperature affects the probability distribution rather than enforcing the most probable word. You have trained a custom image recognition model using Oracle Cloud Infrastructure (OCI) Generative AI Service. You need to deploy this model and create an endpoint for making inference requests. What are the correct steps?
What is a characteristic of prefix -tuning for Large Language Models (LLMs)?
Your organization handles sensitive customer data and needs to comply with strict data protection regulations. You are tasked with setting up a data processing pipeline on Oracle Cloud Infrastructure (OCI) that ensures data security, compliance, and efficiency. Which combination of OCI services should you use to achieve this?
C. LLMs are specifically designed to handle unstructured text, not just structured data. D. LLMs do not necessarily require continuous manual updates; they can adapt to new data through retraining or fine -tuning as needed. You are managing a mission -critical application on Oracle Cloud Infrastructure (OCI) that requires high availability and disaster recovery. Which combination of OCI features would best ensure minimal downtime and data loss?
D. It replaces the pre-trained weights of the base model entirely: Fine-tuning typically adjusts a portion of the pre -trained model's weights, not a complete replacement. The core purpose of a custom dataset in fine -tuning is to provide the model with domain -specific information that helps it adapt its understanding of language towards your specific needs. This data can include text documents, code snippets, or other relevant information depending on the task and the chosen pre -trained model. By training the model on your custom data, you can: Improve its accuracy and performance on tasks relevant to your domain. Help the model learn the specific vocabulary and terminology used in your field. Guide the model to adapt its understanding of language to better align with the context of your custom data. Therefore, the custom dataset serves as a valuable tool for tailoring the pre-trained model's capabilities to your specific use case within OCI Generative AI. Which of the following is the MOST appropriate method for deploying an LLM application built with OCI Generative AI?
How does the Generator component in OCI Generative AI's RAG approach utilize the retrieved information for text generation?
