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When you create a fine-tuning dedicated AI cluster and it is active for 10 hours, the number of unit hours required for fine-tuning is equal to the duration for which the cluster is active. Therefore, if the cluster is active for 10 hours, it requires 10 unit hours. This calculation assumes that the unit hour measurement directly corresponds to the active time of the cluster.

Reference

OCI documentation on unit hours and fine-tuning processes

Usage guidelines for dedicated AI clusters in OCI

An AI development company aiming to create an assistant capable of analyzing images and generating descriptive text, as well as converting text descriptions into accurate visual representations, would likely focus on integrating a diffusion model. Diffusion models are advanced generative models that specialize in producing complex outputs, including high-quality images from textual descriptions and vice versa.

Reference

Research papers on diffusion models and their applications

Technical documentation on generative models for image and text synthesis

StreamlitChatMessageHistory is a chat message storage tool in Streamlit, used to manage message history within LLM-powered applications.

Key Features of StreamlitChatMessageHistory:

Stores chat messages within Streamlit's session state.

Not persistent across sessions; resets when the session is closed.

Specific to Streamlit applications, not designed for all LLM applications.

Why Option (D) is Incorrect:

StreamlitChatMessageHistory is designed for Streamlit-based apps.

It is not suitable for all LLM applications, particularly those requiring persistent storage.

Why Other Options Are Correct:

(A) True: Each session has its own instance of StreamlitChatMessageHistory.

(B) True: It is not persisted across sessions.

(C) True: It stores messages in the Streamlit session state.

Oracle Generative AI Reference:

While Oracle AI supports various LLM applications, StreamlitChatMessageHistory is limited to Streamlit-based chat interfaces.

A string prompt template is a mechanism used to structure prompts dynamically by inserting variables. These templates are commonly used in LLM-powered applications like chatbots, text generation, and automation tools.

How Prompt Templates Handle Variables:

They support an unlimited number of variables or can work without any variables.

Variables are typically denoted by placeholders such as {variable_name} or {{variable_name}} in frameworks like LangChain or Oracle AI.

Users can dynamically populate these placeholders to generate different prompts without rewriting the entire template.

Example of a Prompt Template:

Without variables: 'What is the capital of France?'

With one variable: 'What is the capital of {country}?'

With multiple variables: 'What is the capital of {country}, and what language is spoken there?'

Why Other Options Are Incorrect:

(B) is false because templates can work with one or no variables.

(C) is false because templates rely on variables for dynamic input.

(D) is false because templates can handle multiple placeholders.

Oracle Generative AI Reference:

Oracle integrates prompt engineering capabilities into its AI platforms, allowing developers to create scalable, reusable prompts for various AI applications.

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.

Reference

Technical documentation on AI cluster architectures

Research articles on optimizing GPU memory utilization in model inference