Model Fine-tuning Quiz & Flashcards on LangChain

Questions and Answers

In LangChain, which retriever search type balances between relevancy and diversity?

Similarity score threshold

Cosine similarity

Top k

MMR (correct)

What is the primary benefit of using a dedicated RDMA cluster network during model fine-tuning and inference?

Reduced latency in model inference (correct)

Deployment of multiple fine-tuned models

Increased GPU memory requirements for model deployment

Improved model accuracy

What is the main function of a model endpoint in the OCI Generative AI service?

Serves as a designated point for user requests and model responses (correct)

Hosts the training data for fine-tuning custom models

Evaluates the performance metrics of the custom model

Updates the weights of the base model during fine-tuning

What is a key characteristic of Parameter-Efficient Fine-tuning (PEFT) in Large Language Model training?

Involves only a few or new parameters and uses labeled, task-specific data Signup and view all the answers

How does RAG Token technique differ from RAG Sequence when generating a model's response?

RAG Token retrieves relevant documents for each part of the response and constructs the answer incrementally Signup and view all the answers

What is the primary advantage of using RDMA cluster networks in model deployment?

Reduced latency in model inference Signup and view all the answers

What is the role of a model endpoint in the inference workflow of the OCI Generative AI service?

Serves as a designated point for user requests and model responses Signup and view all the answers

What is a key difference between PEFT and classic fine-tuning in Large Language Model training?

PEFT involves only a few or new parameters and uses labeled, task-specific data Signup and view all the answers

Which component of Retrieval-Augmented Generation (RAG) is responsible for evaluating and prioritizing the retrieved information?

Ranker Signup and view all the answers

What is the primary difference between Top k and Top p in selecting the next token in the OCI Generative AI Generation models?

Top k selects based on position, while Top p selects based on cumulative probability Signup and view all the answers

What is the effect of the 'Top p' parameter in the OCI Generative AI Generation models?

Limits token selection based on the sum of their probabilities Signup and view all the answers

What does the 'temperature' parameter control in the OCI Generative AI Generation models?

Randomness of the model's output, affecting its creativity Signup and view all the answers

What is the key difference between the Cohere Embed v3 model and its predecessor in the OCI Generative AI service?

Improved retrievals for Retrieval-Augmented Generation (RAG) systems Signup and view all the answers

What is the primary function of the Ranker component in a Retrieval-Augmented Generation (RAG) system?

Evaluate and prioritize the retrieved information Signup and view all the answers

What does the Encoder-decoder component do in a Retrieval-Augmented Generation (RAG) system?

Generate text based on the retrieved information Signup and view all the answers

What is the purpose of the Retriever component in a Retrieval-Augmented Generation (RAG) system?

Retrieve relevant information from a knowledge base Signup and view all the answers

Study Notes

Retrieval-Augmented Generation (RAG)

RAG Token technique differs from RAG Sequence in generating a model's response by retrieving relevant documents for each part of the response and constructing the answer incrementally.
RAG component that evaluates and prioritizes the information retrieved by the retrieval system is the Ranker.

Parameter-Efficient Fine-tuning (PEFT)

PEFT is a distinguishing feature of Large Language Model training that involves only a few or new parameters and uses labeled, task-specific data.

LangChain

LangChain uses MMR (Maximum Marginal Relevance) search type to balance between relevancy and diversity.

OCI Generative AI Service

Model end point serves as a designated point for user requests and model responses in the inference workflow.
Dedicated RDMA cluster network increases GPU memory requirements for model deployment during model fine-tuning and inference.

Generation Models

Top-p and Top-k differ in selecting the next token; Top-p selects based on the cumulative probability of the top tokens, whereas Top-k considers the sum of probabilities of the top tokens.
Top-p parameter limits token selection based on the sum of their probabilities.
Temperature parameter controls the randomness of the model's output, affecting its creativity.

Cohere Embed v3 Model

Cohere Embed v3 model is distinguished from its predecessor by its improved retrievals for Retrieval Augmented Generation (RAG) systems.

Studying That Suits You

Use AI to generate personalized quizzes and flashcards to suit your learning preferences.

Description

Test your knowledge on LangChain retriever search types and model fine-tuning with RDMA cluster networks. Evaluate your understanding of model deployment and inference.

LangChain and Model Fine-tuning Quiz