Hacker's Guide to Language Models

Introduction

• Presenter: Jeremy Howard from fast.ai
• Purpose: Code-first approach for understanding and using language models
• Prerequisite: Basics of deep learning (recommended course: course.fast.ai)

What is a Language Model?

• Predicts the next word in a sentence or fills in missing words.
• Examples:
  • Text DaVinci 003 by OpenAI
  • Nat.dev platform for experimenting with language models
• Tokens: Sub-word units, whole words, punctuation, or numbers used to train language models.
• Tokenization: Process of converting text into tokens using tools like 'tiktoken'.

ULMFit Algorithm

• Steps:
  1. Language Model Training (Pre-training): Predict the next word in sentences using a large corpus (e.g., Wikipedia).
  2. Language Model Fine-Tuning: Adjust model using a dataset closer to the final task.
  3. Classifier Fine-Tuning: Optimize for the end task using methods like reinforcement learning from human feedback (RLHF).
• Purpose: Compress world knowledge into neural network parameters.

Instruction Tuning and Fine-Tuning

• Instruction Tuning: Uses datasets like OpenAker and Flan for question-answer pairs.
• Classifier Fine-Tuning: Involves human feedback to improve model responses.
• Importance: Pre-trained models are fine-tuned to perform specific tasks better.

Using GPT-4

• Recommendation: Best language model as of September 2023.
• Usage: Pay for GPT-4 through OpenAI for high-quality outputs.

Common Misconceptions

• Incorrect claims that GPT-4 can't reason or solve specific problems.
• Empirical tests showing GPT-4's strong performance on tasks presented as limitations.

Custom Instructions

• Purpose: Enhance accuracy by priming the model for high-quality information.
• Examples: Contextual instructions to improve reasoning and output quality.

Limitations

• GPT-4 can't provide accurate information about itself, URLs, or post-September 2021 data.
• Hallucinations: Model confidently provides incorrect information when it lacks knowledge.

Advanced Features and Tools

• Advanced Data Analysis: GPT-4 can write and test code, though with some limitations.
• Google's Bard: Can OCR texts directly within the prompt.

Practical Implementations

• API Usage: Using OpenAI API for repetitive and programmatic tasks.
• Rate Limits Handling: Use Bing to generate code for handling API rate limits.

Building a Code Interpreter

• Function Calling: Pass functions to GPT-4 using JSON schema for custom tasks.
• Example: Python function to calculate factorials.
• Enhanced Functionality: Create custom functions for complex queries and tasks.

Running Language Models Locally

• GPU Requirements: Use Kaggle, Colab, or rent server GPUs for local setup.
• Library: Hugging Face’s 'transformers' library for model implementation.

Working with Hugging Face Models

• Model Examples: Llama2, the Bloke’s GPTQ versions for optimized performance.
• Instruction Tuning: Importance of following specific prompt formats for different models.

Retrieval Augmented Generation (RAG)

• Overview: Enhances response quality by providing contextual information to the model from external documents.
• Vector Databases: Use sentence transformers to match queries with relevant documents.
• Examples: H2O GPT, private GPT setups.

Fine-Tuning Custom Models

• Use Cases: Create a model fine-tuned to specific tasks (e.g., converting natural language to SQL queries).
• Libraries: Hugging Face’s 'datasets' and fine-tuning tools like 'Axolotl'.

Running Models on Macs

• Tools: MLC and llama.cpp for running models on Apple hardware.
• Performance: Running quantized 7B models effectively.

Community and Support

• Resources: fast.ai Discord channel for generative AI discussions and support.
• Conclusion: Exciting but complex field; collaboration is crucial for navigating challenges.

Thank you for listening!