A. RAG Token does not use document retrieval but generates responses based on pre-existing knowledge only.
B. RAG Token retrieves documents oar/at the beginning of the response generation and uses those for the entire content
C. Unlike RAG Sequence, RAG Token generates the entire response at once without considering individual parts.
D. RAG Token retrieves relevant documents for each part of the response and constructs the answer incrementally.
A. Top p determines the maximum number of tokens per response.
B. Top p limits token selection based on the sum of their probabilities.
C. Top p selects tokens from the "Top k' tokens sorted by probability.
D. Top p assigns penalties to frequently occurring tokens.
A. It is not optimized for high-dimensional spaces.
B. It is based on distances and similarities in a vector space.
C. It uses simple row-based data storage.
D. A vector database stores data in a linear or tabular format.
A. Updates the weights of the base model during the fine-tuning process
B. Hosts the training data for fine-tuning custom model
C. Serves as a designated point for user requests and model responses
D. Evaluates the performance metrics of the custom model
A. It requires a large temperature setting to ensure diverse word selection.
B. It selects words bated on a flattened distribution over the vocabulary.
C. It picks the most likely word email at each step of decoding.
D. It chooses words randomly from the set of less probable candidates.
A. 10 unit hours
B. 15 unit hours
C. 40 unit hours
D. 30 unit hours
A. By incorporating additional layers to the base model
B. By restricting updates to only a specific croup of transformer Layers
C. By allowing updates across all layers of the model
D. By excluding transformer layers from the fine-tuning process entirely
