rights reserved. 1. What are Large Language Models? 2. Overview of Amazon SageMaker Studio Lab 3. Account creation 4. Prompt engineering basics (hands on) 5. SageMaker vs SageMaker Studio Lab (demo) Today’s session:
rights reserved. 3 How LLMs differ from other ML language models 3 Text generation Summarization Information extraction Q&A Chatbot Pre-train Adapt Tasks Unlabeled data LLM Text generation Summarization Information extraction Q&A Chatbot Train Deploy Tasks ML model ML model ML model ML model ML model Labeled data Labeled data Labeled data Labeled data Labeled data
rights reserved. A J U P Y T E R N O T E B O O K S E R V I C E T O H E L P C U S T O M E R S M A S T E R T H E I R S K I L L S What is Amazon SageMaker Studio Lab Create an account with an email address – free As many compute sessions as you need – CPU (8 hrs)/GPU (4 hrs) Access any notebook on GitHub No setup or configuration required 15 GBs to save your work projects. Migrate to SageMaker Studio when ready Amazon SageMaker Studio Lab A no-charge, no-configuration service that enable data scientists to learn and experiment with machine learning
rights reserved. Why Studio Lab for this exercise? Access to variety of open source models Privacy – it’s your environment, you’re in control Ability to experiment with non-tuned and tuned models, compare, learn No cost environment Start with Studio Lab, graduate to SageMaker to work with bigger models and more data
rights reserved. Signup for Studio Lab account https://docs.aws.amazon.com/sagemaker/latest/dg/studio-lab-onboard.html 1. Go to https://studiolab.sagemaker.aws/ 2. Request account Use referral code: reInvent23-875F6 3. Confirm email 4. Sign into the account 5. git clone https://github.com/aws/studio-lab-examples.git 6. The notebook location: /studio-lab-examples/large-language-models/prompting-mistral7B.ipynb !! Use Referral Code !!
rights reserved. Prompt Engineering: A lightspeed introduction • What is a Prompt? ü Text input provided to an AI system to elicit a response • What is Prompt Engineering? ü Using NLP techniques to craft prompts that steer FMs/LLMs towards desired responses • Why is this important? ü Enables fine-grained and strategic control over models’ behavior ü Targets desired capabilities ü Mitigates risks Prompt Response LLM Input Output *NLP = Natural Language Processing FM = Foundation Model LLM = Large Language Model
rights reserved. Prompt Engineering: Zero-shot, One-shot, Few-shot Zero shot learning Generation: Dear Mark, I would like to invest in your company with a minimum investment of $100,000. John Write an email that kindly declines the offer: Summarization: Summarize this article…. OUTPUT: Feds will not increase Code Generation: “Create a sql to find all users who live in WA and have more than 3 cars” Few-shot learning Generation: List the Country of origin of food. Pizza comes from Italy Burger comes from USA Curry comes from Classification: Tweet: "I hate it when my phone battery dies.”: Sentiment: Negative Tweet: "My day has been great”: Sentiment: Positive Tweet: "This is the link to the article”: Sentiment: Neutral Tweet: "This new music video was incredibile” Sentiment: Translation: sea otter is loutre de mer <> cheese is <> One-shot learning Generation : Task is to generate airport codes Text: "I want to fly form Los Angeles to Miami” Airport codes: LAX, MIA Text: "I want to fly from Dallas to San Francisco” Airport codes: Classification : Tweet: “ great pen” output GOOD Tweet: “great show” output Source : https://huggingface.co/blog/few-shot-learning-gpt-neo-and-inference-api
rights reserved. Tips for designing prompts • Be clear and concise • Include context if needed • Use directives for the desired response type • Consider the output in the prompt • Provide an example response • Use simple language • Test and experiment 13
rights reserved. Choosing the right strategy for your GenAI task, with AWS 15 Embeddings- based search or RAG (search + LLM) Specific, or up-to-date info Task requires specific info? Simple task? Generic or historical info Zero-shot Prompting or similar (FM) Simple, no need for examples Specific style? More complex, requires examples CoT, Fine-Tuning, or similar (FM + dataset) Few-shot Prompting or similar (LLM + examples) Specific style, skill, pattern, or reasoning Relatively straight forward or generic style Amazon Kendra Amazon SageMaker Any vector DB e.g.: OpenSearch, RDS, Marketplace (Pinecone) Open-source (FAISS, Chroma, etc.) Amazon SageMaker Amazon Bedrock Amazon Bedrock Amazon SageMaker Amazon Bedrock Amazon SageMaker Amazon Bedrock *RAG = Retrieval Augmented Generation *CoT = Chain of Thoughts Real- time required? ReAct or similar (LLM + Agent) Amazon SageMaker Amazon Bedrock *ReAct = Reasoning & Acting Relatively static information (e.g. docs, web scrapping) Dynamic information (e.g. DBs, APIs, live internet) Note, combinations are possible. E.g.: Fine-Tuning + RAG See “RAG vs Finetuning” from Heiko Hotz (https://medium.com/p/94654b1eaba7)
rights reserved. SageMaker Studio Lab vs SageMaker PREPARE SageMaker Ground Truth Label training data for machine learning SageMaker Data Wrangler Aggregate and prepare data for machine learning SageMaker Processing Built-in Python, BYO R/Spark SageMaker Feature Store Store, update, retrieve, and share features SageMaker Clarify Detect bias and understand model predictions BUILD SageMaker Studio Notebooks & Notebook Instances Jupyter notebooks with elastic compute and sharing Built-in and bring- your-own algorithms Dozens of optimized algorithms or bring your own Local mode Test and prototype on your local machine SageMaker Autopilot Automatically create machine learning models with full visibility SageMaker JumpStart Pre-built solutions for common use cases TRAIN & TUNE Managed training Distributed infrastructure management SageMaker Experiments Capture, organize, and compare every step Automatic model tuning Hyperparameter optimization Distributed training libraries Training for large datasets and models SageMaker Debugger Debug and profile training runs Managed Spot training Reduce training cost by 90% DEPLOY & MANAGE Managed deployment Fully managed, ultra low latency, high throughput Kubernetes and Kubeflow Integration Simplify Kubernetes-based machine learning Multi-model endpoints Reduce cost by hosting multiple models per instance SageMaker Model Monitor Maintain accuracy of deployed models SageMaker Edge Manager Manage and monitor models on edge devices SageMaker Pipelines Workflow orchestration and automation Amazon SageMaker SageMaker Studio Integrated development environment (IDE) for ML
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