Oracle 1z0-1127-24 Oracle Cloud Infrastructure 2024 Generative AI Professional Exam Practice Test

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.

References

OCI documentation on unit hours and fine-tuning processes

Usage guidelines for dedicated AI clusters in OCI

In the OCI Generative AI service, the categories of pre-trained foundational models available include Summarization models, Generation models, and Embedding models. However, Translation models are not listed as a category of pre-trained foundational models available in OCI Generative AI service. The service focuses on providing models that support text generation, summarization, and embedding tasks.

References

OCI Generative AI service documentation

Listings and descriptions of pre-trained foundational models in OCI

The "Top p" parameter, also known as nucleus sampling, in generative AI models limits token selection based on the sum of their probabilities. It ensures that the cumulative probability of the selected tokens meets or exceeds a specified threshold p. This approach dynamically includes as many tokens as necessary to reach the desired probability sum, allowing for more diverse and contextually appropriate outputs compared to a fixed top-k selection.

References

Research papers on nucleus sampling and token selection methods

OCI Generative AI model documentation

The difference between "Top k" and "Top p" in selecting the next token in generative models lies in their selection criteria:

Top k: This method selects the next token from the top k tokens based on their probability scores. It restricts the selection to a fixed number of the most probable tokens, irrespective of their cumulative probability.

Top p: Also known as nucleus sampling, this method selects tokens based on the cumulative probability until it exceeds a certain threshold p. It dynamically adjusts the number of tokens considered, ensuring that the sum of their probabilities meets or exceeds the specified p value. This allows for a more flexible and often more diverse selection compared to Top k.

References

Research articles on sampling techniques in language models

Technical documentation for generative AI models in OCI

In the evaluation of OCI Generative AI fine-tuned models, "Loss" measures the level of incorrectness in the model's predictions. It quantifies how far the model's predictions are from the actual values. Lower loss values indicate better performance, as they reflect a smaller discrepancy between the predicted and true values. The goal during training is to minimize the loss, thereby improving the model's accuracy and reliability.

References

Articles on loss functions in machine learning

OCI Generative AI service documentation on model evaluation metrics

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

Parameter-Efficient Fine-Tuning (PEFT) is a technique used in large language model training that focuses on adjusting only a subset of the model's parameters rather than all of them. This approach involves using labeled, task-specific data to fine-tune new or a limited number of parameters. PEFT is designed to be more efficient than classic fine-tuning, which typically adjusts all the parameters of the model. By only updating a small fraction of the model's parameters, PEFT reduces the computational resources and time required for fine-tuning while still achieving significant performance improvements on specific tasks.

References

Research papers on Parameter-Efficient Fine-Tuning (PEFT)

Technical documentation on fine-tuning techniques for large language models

The Cohere Embed v3 model distinguishes itself from its predecessor in the OCI Generative AI service primarily through improved retrievals for Retrieval Augmented Generation (RAG) systems. This enhancement means that the new version of the model is better at retrieving relevant documents or passages that can be used to augment the generation of responses. The improvements likely include better embedding quality, which allows the model to find more relevant and contextually appropriate information during the retrieval phase.

References

Cohere model documentation and release notes

Technical discussions on improvements in RAG systems

LangChain Expression Language (LCEL) is a declarative language used to compose chains together in LangChain. It allows users to define the flow and interaction of different components in a clear and concise manner. By using LCEL, developers can easily specify how prompts, models, and other elements should interact, making the process of creating and managing chains more straightforward and efficient. This method is preferred due to its readability and ease of use, compared to more imperative or programmatic approaches.

References

LangChain documentation on LCEL

Examples and tutorials on using LangChain Expression Language

In LangChain, "Conversation Image Memory" is not a built-in memory type. The built-in memory types in LangChain include:

Conversation Token Buffer Memory: This memory type stores a buffer of tokens from the conversation history.

Conversation Buffer Memory: This memory type retains a buffer of conversation history, typically in the form of text.

Conversation Summary Memory: This memory type summarizes the conversation history to keep track of key points and information.

These memory types help manage and utilize conversation history in various ways to enhance the performance of conversational models.

References

LangChain documentation on memory types

Technical guides on implementing memory in conversational AI systems

The key advantage of using T-Few over Vanilla fine-tuning in the OCI Generative AI service is faster training time and lower cost. T-Few fine-tuning is designed to be more efficient by updating only a fraction of the model's parameters, which significantly reduces the computational resources and time required for fine-tuning. This efficiency translates to lower costs, making it a more economical choice for model fine-tuning.

References

Technical documentation on T-Few fine-tuning

Research articles comparing fine-tuning methods in machine learning