capability is essential for scaling up the deployment of multiple fine -tuned models across a GPU cluster. RDMA allows data to be transferred directly between the memory of different computers without involving the CPU, leading to significantly reduced latency and higher throughput. This efficiency is particularly important in the context of fine -tuning and deploying large language models, where the speed and efficiency of data transfer can impact overall performance and scalability. By enabling fast and efficient communication, a dedicated RDMA cluster network supports the deployment of multiple fine-tuned models on the same GPU cluster, enhancing both flexibility and scalability in handling various AI workloads. Reference: Oracle Cloud Infrastructure (OCI) documentation on RDMA cluster networks Technical resources on the benefits of RDMA in high -performance computing environments Which role docs a "model end point" serve in the inference workflow of the OCI Generative AI service?
compared to RAG Token, as it does not adjust the retrieval process during the generation of the response. Reference: Research articles on Retrieval -Augmented Generation (RAG) techniques Documentation on advanced language model inference methods Which component of Retrieval -Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?
Explanation: 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. Reference: Research articles on sampling techniques in language models Technical documentation for generative AI models in OCI Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?
C. Support for tokenizing longer sentences Although tokenization is crucial for handling text, the Cohere Embed v3 model's distinguishing feature is its improvements in retrieval and embedding for RAG systems, rather than specifically enhancing tokenization for longer sentences. D. Emphasis on syntactic clustering of word embeddings Syntactic clustering is an important aspect of word embeddings but is not the key improvement highlighted for the Cohere Embed v3 model. The model’s major advancements are in its retrieval capabilities, making it more effective for RAG applications. What is the purpose of the "stop sequence" parameter in the OCI Generative AI Generation models?
the next in the sequence. This feature helps in understanding the model's decision -making process and the relative probabilities of different tokens. Reference: Technical documentation on language model token generation Research articles on probability distributions in generative models Given the following code: Prompt Template (input_variable[‘’rhuman_input",'city’’], template -template) Which statement is true about Promt Template in relation to input_variables?
➢ TOTAL QUESTIONS: 420 In LangChain, which retriever search type is used to balance between relevancy and diversity?
