Podcast
Questions and Answers
Which is the main characteristic of greedy decoding in the context of language model word prediction?
Which is the main characteristic of greedy decoding in the context of language model word prediction?
- It requires a large temperature setting to ensure diverse word selection.
- It picks the most likely word to emit at each step of decoding. (correct)
- It chooses words randomly from the set of less probable candidates.
- It selects words based on a flattened distribution over the vocabulary.
In Lang Chain, which retriever search type is used to balance between relevancy and diversity?
In Lang Chain, which retriever search type is used to balance between relevancy and diversity?
- Top k
- MMR (correct)
- Similarity
- Similarity score Threshold
An AI development company is working on an advanced AI assistant capable of handling queries in a seamless manner. Their goal is to create an assistant that can analyze images provided by users and generate descriptive text, as well as take text descriptions and produce accurate visual representations. Considering the capabilities, which type of model would the company likely focus on integrating into their AI assistant?
An AI development company is working on an advanced AI assistant capable of handling queries in a seamless manner. Their goal is to create an assistant that can analyze images provided by users and generate descriptive text, as well as take text descriptions and produce accurate visual representations. Considering the capabilities, which type of model would the company likely focus on integrating into their AI assistant?
- Language model that operates on a token-by-token output basis
- Large Language Model based agent that focuses on generating textual responses
- Retrieval-Augmented Generation (RAG) model that uses text as input and output (correct)
- Diffusion model that specializes in producing complex outputs
What does "k-shot prompting" refer to when using Large Language Models for task-specific applications?
What does "k-shot prompting" refer to when using Large Language Models for task-specific applications?
Analyze the user prompts provided to a language model. Which scenario exemplifies prompt injection (jailbreaking)?
Analyze the user prompts provided to a language model. Which scenario exemplifies prompt injection (jailbreaking)?
Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?
Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?
Given the following code: prompt - PromptTemplate(Input_variables={"human input", "city"}, template=template) Which statement is true about PromptTemplate in relation to input_variables
Given the following code: prompt - PromptTemplate(Input_variables={"human input", "city"}, template=template) Which statement is true about PromptTemplate in relation to input_variables
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?
Which is a cost-related benefit of using vector databases with Large Language Models (LLMs)?
Which is a cost-related benefit of using vector databases with Large Language Models (LLMs)?
How does the integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models (LLMs) fundamentally alter their responses?
How does the integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models (LLMs) fundamentally alter their responses?
How do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language processing?
How do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language processing?
What issue might arise from using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service?
What issue might arise from using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service?
How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?
How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?
Which is a key characteristic of the annotation process used in T-Few fine-tuning?
Which is a key characteristic of the annotation process used in T-Few fine-tuning?
What does "Loss" measure in the evaluation of OCI Generative AI fine-tuned models?
What does "Loss" measure in the evaluation of OCI Generative AI fine-tuned models?
When should you use the T-Few fine-tuning methods for training a model?
When should you use the T-Few fine-tuning methods for training a model?
Which is a key advantage of using T-Few over Vanilla fine-tuning in the OCI Generative AI service?
Which is a key advantage of using T-Few over Vanilla fine-tuning in the OCI Generative AI service?
How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?
How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?
Which statement best describes the role of encoder and decoder models in natural language processing?
Which statement best describes the role of encoder and decoder models in natural language processing?
Which role does a "model endpoint" serve in the inference workflow of the OCI Generative AI service?
Which role does a "model endpoint" serve in the inference workflow of the OCI Generative AI service?
What does a dedicated RDMA cluster network do during model fine-tuning and inference?
What does a dedicated RDMA cluster network do during model fine-tuning and inference?
Which Oracle Accelerated Data Science (ADS) class can be used to deploy a Large Language Model (LLM) application to OCI Data Science model deployment?
Which Oracle Accelerated Data Science (ADS) class can be used to deploy a Large Language Model (LLM) application to OCI Data Science model deployment?
How does the Retrieval-Augmented Generation (RAG) Token technique differ from RAG Sequence when generating a model's response?
How does the Retrieval-Augmented Generation (RAG) Token technique differ from RAG Sequence when generating a model's response?
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?
Which is NOT a typical use case for LangSmith Evaluators?
Which is NOT a typical use case for LangSmith Evaluators?
What is the primary purpose of LangSmith Tracing?
What is the primary purpose of LangSmith Tracing?
You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training. How many unit hours are required for fine-tuning if the cluster is active for 10 hours?
You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training. How many unit hours are required for fine-tuning if the cluster is active for 10 hours?
How does the architecture of dedicated AI clusters contribute to minimizing GPU memory overhead for TFew fine-tuned model inference?
How does the architecture of dedicated AI clusters contribute to minimizing GPU memory overhead for TFew fine-tuned model inference?
Which statement is true about LangChain Expression Language (LCEL)?
Which statement is true about LangChain Expression Language (LCEL)?
Given a block of code: qa Conversational Retrieval Chain. from_11m (11m, retriever=retv, memory=memory) when does a chain typically interact with memory during execution?
After user input but before chain execution, and again after core logic but before output Only after the output has been generated Continuously throughout the entire chain execution process Before user input and after chain execution. Given the following code: prompt Prompt Template (input_variables= ["human_input", "city"], templatetemplate=template) Which statement is true about Prompt Template in relation to input_variables?
Given a block of code: qa Conversational Retrieval Chain. from_11m (11m, retriever=retv, memory=memory) when does a chain typically interact with memory during execution? After user input but before chain execution, and again after core logic but before output Only after the output has been generated Continuously throughout the entire chain execution process Before user input and after chain execution. Given the following code: prompt Prompt Template (input_variables= ["human_input", "city"], templatetemplate=template) Which statement is true about Prompt Template in relation to input_variables?
Given a block of code: qa Conversational Retrieval Chain. from_11m (11m, retriever=retv, memory=memory) when does a chain typically interact with memory during execution?
Given a block of code: qa Conversational Retrieval Chain. from_11m (11m, retriever=retv, memory=memory) when does a chain typically interact with memory during execution?
Which is NOT a built-in memory type in LangChain?
Which is NOT a built-in memory type in LangChain?
What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?
What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?
Which statement describes the difference between "Top k" and "Top p" in selecting the next token in the OCI Generative AI Generation models?
Which statement describes the difference between "Top k" and "Top p" in selecting the next token in the OCI Generative AI Generation models?
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?
What does a higher number assigned to a token signify in the "Show Likelihoods" feature of the language model token generation?
What does a higher number assigned to a token signify in the "Show Likelihoods" feature of the language model token generation?
What is the purpose of the "stop sequence" parameter in the OCI Generative AI Generation models?
What is the purpose of the "stop sequence" parameter in the OCI Generative AI Generation models?
Why is normalization of vectors important before indexing in a hybrid search system?
Why is normalization of vectors important before indexing in a hybrid search system?
Which is a distinguishing feature of "Parameter-Efficient Fine-tuning (PEFT)" as opposed to classic "Finetuning" in Large Language Model training?
Which is a distinguishing feature of "Parameter-Efficient Fine-tuning (PEFT)" as opposed to classic "Finetuning" in Large Language Model training?
Flashcards
Greedy Decoding
Greedy Decoding
A decoding method that chooses the most likely word at each step, without considering future possibilities.
K-shot Prompting
K-shot Prompting
Training a large language model with a few examples to improve its performance on specific tasks.
Multimodal Models
Multimodal Models
Combining vision and language capabilities in an AI model.
Lang Chain Retriever
Lang Chain Retriever
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Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG)
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Vanilla Fine-tuning
Vanilla Fine-tuning
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T-Few Transformer Layers
T-Few Transformer Layers
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T-Few Fine-tuning Annotation
T-Few Fine-tuning Annotation
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Loss
Loss
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RAG Token Technique
RAG Token Technique
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Model Endpoint
Model Endpoint
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RDMA Clusters
RDMA Clusters
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Oracle Accelerated Data Science (ADS)
Oracle Accelerated Data Science (ADS)
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LangSmith Tracing
LangSmith Tracing
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Memory Interaction Timing
Memory Interaction Timing
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Cohere Embed v3
Cohere Embed v3
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Temperature Parameter
Temperature Parameter
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Top K Selection
Top K Selection
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Top P Selection
Top P Selection
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Stop Sequence Parameter
Stop Sequence Parameter
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Vector Normalization
Vector Normalization
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Parameter-Efficient Fine-tuning (PEFT)
Parameter-Efficient Fine-tuning (PEFT)
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Study Notes
Language Model and Decoding Techniques
- Greedy decoding selects the most probable word at each step, optimizing for immediate relevance rather than considering future words.
- K-shot prompting allows a large language model (LLM) to generalize from a few examples, enhancing performance on specific tasks.
AI Assistant Development
- The advanced AI assistant aims to analyze images and generate descriptive text, as well as create accurate visual representations from text descriptions.
- Integration of multimodal models, combining vision and language capabilities, is likely to be the focus for this AI assistant.
Retrieve and Enhance Techniques
- Lang Chain uses a retriever search type to balance between relevancy and diversity in query responses.
- Retrieval-Augmented Generation (RAG) fundamentally alters model responses by integrating external information retrieval for inference accuracy.
Fine-Tuning and Optimization
- Vanilla fine-tuning may suffer from overfitting when using small datasets, potentially compromising model performance.
- Utilizing T-Few transformer layers streamlines the fine-tuning process, improving efficiency and adaptation to specific tasks.
- T-Few fine-tuning employs a distinct annotation process characterized by reduced sample sizes for effective learning.
Loss and Model Evaluation
- "Loss" quantifies the difference between predicted and actual outputs, serving as a crucial metric in evaluating model performance.
- RAG Token technique differs from RAG Sequence, focusing on token-level information integration for concise responses.
Storage and Data Privacy
- Fine-tuned customer models in OCI Generative AI service are securely stored to ensure data privacy and protection against unauthorized access.
Memory and Interaction in Models
- The model endpoint is integral to the inference workflow, serving as an interface between user queries and model responses.
- Dedicated RDMA clusters optimize model fine-tuning and inference by enhancing data transfer speeds and resource management.
Model Deployment and Evaluation
- Oracle Accelerated Data Science (ADS) facilitates deploying LLM applications within the OCI Data Science environment.
- LangSmith Tracing aims to monitor and ensure the quality of model outputs, aiding in debugging and evaluation processes.
Memory Types and Interaction
- Typical memory interaction occurs after user input but before execution, and again after logic is processed but before generating output.
- Cohere Embed v3 model showcases improvements over its predecessor in embeddings, increasing accuracy and contextual understanding.
Model Generation Parameters
- The "temperature" parameter controls the randomness of predictions in generation models; lower values yield more deterministic outputs.
- "Top k" selects from the top k probable tokens, whereas "Top p" (nucleus sampling) chooses from a dynamic pool of probable tokens based on cumulative probability.
- The "stop sequence" parameter determines where the model should cease generation, ensuring coherent response lengths.
Vector Normalization and Fine-Tuning
- Normalization of vectors is essential for indexing in hybrid search systems, enhancing retrieval accuracy.
- Parameter-Efficient Fine-tuning (PEFT) significantly reduces the number of trainable parameters compared to classic fine-tuning methods, increasing efficiency in training large models.
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