Day 2 Blog: Mastering the Dimensions of LLM Engineering

Welcome to Day 2 of my journey in mastering LLM (Large Language Model) engineering! Today, we’ll delve deeper into three critical dimensions of LLM engineering: Models, Tools, and Techniques. This blog will also cover practical applications and an upgrade to our Day 1 project to enhance our understanding and skills.

You Will Master These 3 Dimensions of LLM Engineering

1. Models

• Definition: Models are the backbone of LLM engineering. They process input data to generate meaningful output.

• Types:

  • Open-Source Models: Free to use and modify. Examples include GPT-3 and BERT.

  • Closed-Source Models: Proprietary models, often requiring a subscription or payment to use. Examples include OpenAI’s GPT-4.

  • Multi-Modal Models: These models handle multiple types of data, such as text, images, and audio. Examples include DALL-E and CLIP.

• Architecture: The design and structure of the model, including layers, attention mechanisms, and embeddings.

• Selecting Models: Choosing the right model based on the task, data availability, and computational resources.

2. Tools

• Definition: Tools are software and libraries that facilitate the use, training, and deployment of LLMs.

• Examples:

  • Hugging Face: A popular platform for deploying and using transformers.

  • Langchain: A library for building applications with language models.

  • Radio: Tools for audio data processing.

  • Weights & Biases: A platform for tracking machine learning experiments.

  • Moal: Tools for model monitoring and analysis.

3. Techniques

• Definition: Techniques are methods used to optimize and interact with LLMs.

• Examples:

  • APIs: Interfaces for interacting with LLMs programmatically.

  • Multi-Shot Prompting: Providing multiple examples in prompts to guide the model’s response.

  • RAG (Retrieval-Augmented Generation): Combining retrieval of relevant documents with generation to enhance output.

  • Fine-Tuning: Adjusting a pre-trained model on a specific dataset for a particular task.

  • Agentization: Turning models into agents that can interact with environments and perform tasks autonomously.

Targeted for a Wide Range of Background Experience

• Too Easy: Hang in there! This course will build every day. There are challenging and fun projects coming up.

• Too Challenging: Take your time with the practicals, use extra guides in week 1, and ask for help.

• Just Right: Excellent!

Three Ways to Use Models

1. Chat Interfaces

• Example: ChatGPT

• Description: Interactive platforms where users can input queries and receive generated responses.

2. Cloud APIs

• Frameworks: Langchain, Managed AI Cloud Services

• Examples: Amazon Bedrock, Google Vertex, Azure ML

• Description: Services that provide AI capabilities over the cloud. These platforms manage the infrastructure, making it easier to integrate AI into applications.

3. Direct Inference

• Tools: HuggingFace Transformers Library with Ollama

• Description: Running models locally on your machine. This approach offers more control and avoids API costs.

Upgrade Day 1 Project

Let’s upgrade our Day 1 project to summarize a webpage using an open-source model running locally via Ollama instead of OpenAI. This technique can be used for all subsequent projects if you prefer not to use paid APIs.

Benefits:

1. No API Charges: Open-source models are free to use.
2. Data Privacy: Your data doesn’t leave your local machine.

Disadvantages:

1. Less Power: Open-source models may have less computational power compared to frontier models.

Example Code with Detailed Comments

Here’s the enhanced code to summarize a webpage using an open-source model with Ollama:

Github Repo : Link