Explain the fundamentals of LLMs
Large Language Models (LLMs) are deep learning models that are trained to understand and generate human language. They are based on transformer architectures, which use self-attention mechanisms to process and generate sequences of text. The training process involves feeding the model large amounts of text data, allowing it to learn patterns, grammar, context, and even some level of reasoning.
Understand LLM architectures
The most common architecture for LLMs is the transformer. Key components of transformer architectures include:
- Self-Attention Mechanism: Allows the model to weigh the importance of different words in a sentence.
- Multi-Head Attention: Helps the model focus on different parts of the sentence simultaneously.
- Feed-Forward Neural Networks: Applied to each position in the sequence to transform the input.
- Positional Encoding: Adds information about the position of words in a sentence, which is crucial since transformers do not have a sense of order on their own.
Design and use prompts for LLMs
Prompts are specific inputs given to LLMs to guide their output. Effective prompt design involves:
- Clarity: Clearly stating the desired task or question.
- Context: Providing enough context for the model to understand the task.
- Constraints: Defining any constraints or formats for the output.
Understand LLM fine-tuning
Fine-tuning involves training a pre-trained LLM on a smaller, task-specific dataset. This process adjusts the model weights to improve performance on the specific task. Fine-tuning is typically faster and requires less data than training from scratch because the model already has learned a general understanding of language.
Fundamentals of Code Models, Multi-Modal, and Language Agents
Understand the fundamentals of code models
Code models are LLMs specifically trained on programming languages. They can understand, generate, and complete code snippets, and are useful for tasks like code synthesis, bug fixing, and documentation generation.
Understand the fundamentals of multi-modal models
Multi-modal models can process and generate data across different types of modalities, such as text, images, and audio. These models combine inputs from various sources to perform tasks that involve multiple types of data.
Understand the fundamentals of language agents
Language agents use LLMs to perform specific tasks autonomously. They can interact with users, gather information, and take actions based on their understanding and objectives. Language agents leverage natural language understanding and generation capabilities of LLMs to facilitate human-like interactions.
Using OCI Generative AI Service
Explain the fundamentals of OCI Generative AI service
OCI (Oracle Cloud Infrastructure) Generative AI Service provides access to advanced AI models and tools for building, deploying, and managing AI applications. It offers pre-trained models and infrastructure for custom model training and inference.
Use pretrained foundational models for Generation, Summarization, and Embedding
OCI Generative AI Service includes pre-trained models for:
- Generation: Creating new text based on a given prompt.
- Summarization: Condensing long texts into shorter, meaningful summaries.
- Embedding: Converting text into numerical vectors for various applications, such as semantic search.
Create dedicated AI clusters for fine-tuning and inference
OCI allows users to create dedicated AI clusters to fine-tune models with custom datasets and perform inference. These clusters are optimized for high-performance computing and can handle the demands of large-scale AI tasks.
Fine-tune base model with custom dataset
Users can upload their custom datasets to OCI and use the provided infrastructure to fine-tune pre-trained models. This involves training the model on the new data to improve its performance on specific tasks relevant to the user’s needs.
Create and use model endpoints for inference
OCI enables users to create endpoints for their trained models, allowing applications to send requests to these endpoints for real-time inference. This makes it easy to integrate AI capabilities into various applications.
Explore OCI Generative AI security architecture
OCI emphasizes security in its AI services, including data encryption, secure access controls, and compliance with regulatory standards. The security architecture ensures that data and models are protected throughout their lifecycle.
Building an LLM Application with OCI Generative AI Service
Understand Retrieval Augmented Generation (RAG) concepts
RAG combines retrieval-based methods with generation-based models. It retrieves relevant documents from a knowledge base and uses them to augment the input of a generative model, improving the relevance and accuracy of the generated content.
Explain vector database concepts
Vector databases store data as high-dimensional vectors. They are used for tasks like semantic search, where similarity between vectors (e.g., text embeddings) is used to find relevant data. Vector databases support efficient similarity searches.
Explain semantic search concepts
Semantic search goes beyond keyword matching by understanding the meaning of the query and the documents. It uses embeddings to represent the meaning of texts and retrieves documents based on their semantic similarity to the query.
Build LangChain models, prompts, memory, and chains
LangChain is a framework for building applications that use LLMs. It involves:
- Models: Using LLMs for various tasks.
- Prompts: Designing effective prompts for the models.
- Memory: Maintaining context over interactions.
- Chains: Creating sequences of tasks and models to accomplish complex objectives.
Build an LLM application with RAG and LangChain
To build an LLM application with RAG and LangChain:
- Integrate Retrieval: Use a vector database to retrieve relevant documents.
- Augment Input: Combine the retrieved documents with user input.
- Generate Response: Use a generative model to produce a response based on the augmented input.
- Implement Chains: Use LangChain to manage the sequence of retrieval and generation steps.
Trace and evaluate an LLM application
Tracing involves logging and monitoring the application’s performance and behavior. Evaluation includes assessing the quality, relevance, and accuracy of the model’s outputs. Tools and metrics for tracing and evaluation help improve the application’s performance.
Deploy an LLM application
Deploying an LLM application involves:
- Infrastructure Setup: Provisioning servers or cloud resources.
- Model Hosting: Hosting the trained model and exposing endpoints.
- Integration: Integrating the endpoints with the application.
- Monitoring: Continuously monitoring performance and making adjustments as needed.
By understanding these concepts, you can effectively build, deploy, and manage LLM applications using OCI Generative AI Service.
