Questions and Answers
In LangChain, which retriever search type balances between relevancy and diversity?
What is the primary benefit of using a dedicated RDMA cluster network during model fine-tuning and inference?
What is the main function of a model endpoint in the OCI Generative AI service?
What is a key characteristic of Parameter-Efficient Fine-tuning (PEFT) in Large Language Model training?
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How does RAG Token technique differ from RAG Sequence when generating a model's response?
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What is the primary advantage of using RDMA cluster networks in model deployment?
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What is the role of a model endpoint in the inference workflow of the OCI Generative AI service?
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What is a key difference between PEFT and classic fine-tuning in Large Language Model training?
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Which component of Retrieval-Augmented Generation (RAG) is responsible for evaluating and prioritizing the retrieved information?
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What is the primary difference between Top k and Top p in selecting the next token in the OCI Generative AI Generation models?
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What is the effect of the 'Top p' parameter in the OCI Generative AI Generation models?
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What does the 'temperature' parameter control in the OCI Generative AI Generation models?
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What is the key difference between the Cohere Embed v3 model and its predecessor in the OCI Generative AI service?
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What is the primary function of the Ranker component in a Retrieval-Augmented Generation (RAG) system?
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What does the Encoder-decoder component do in a Retrieval-Augmented Generation (RAG) system?
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What is the purpose of the Retriever component in a Retrieval-Augmented Generation (RAG) system?
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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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Test your knowledge on LangChain retriever search types and model fine-tuning with RDMA cluster networks. Evaluate your understanding of model deployment and inference.