LangChain and Model Fine-tuning Quiz
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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?

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

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

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

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

    <p>Reduced latency in model inference</p> Signup and view all the answers

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

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

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

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

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

    <p>Ranker</p> 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?

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

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

    <p>Limits token selection based on the sum of their probabilities</p> Signup and view all the answers

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

    <p>Randomness of the model's output, affecting its creativity</p> 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?

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

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

    <p>Evaluate and prioritize the retrieved information</p> Signup and view all the answers

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

    <p>Generate text based on the retrieved information</p> Signup and view all the answers

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

    <p>Retrieve relevant information from a knowledge base</p> 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.

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    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.

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