A Hackers Guide to Large Language Models

updated 29 Sep 2023

In this conversation, we delved into the intricacies of fine-tuning language models, exploring various options and tools such as Llama.cpp and Axolotl for model optimization. The user demonstrated the process of running language models on Macs. Additionally, retrieval augmented generation, a technique incorporating document search to enhance model responses, was explored. The user emphasized the collaborative nature of the field, encouraging participation in platforms like the Fast AI Discord channel. Overall, the discussion provided insights into the evolving landscape of language model usage, offering practical tips and considerations for enthusiasts and practitioners alike.

A Hackers Guide to Large Language Models

Intro & Basic Ideas of Large Language Models

All right, let me break it down for you. Jeremy Howard from fast.ai is giving a code-first approach to understanding language models. He starts by explaining what a language model is—it predicts the next word in a sentence. He uses OpenAI's language model, text DaVinci 003, as an example.

The core idea behind language models, like GPT-4, comes from a 2017 algorithm called ULM fit, developed by Howard. The process involves three steps: language model training (pre-training), language model fine-tuning, and classifier fine-tuning. The language model is initially trained on a large dataset (like Wikipedia) to predict the next word in a sentence. Fine-tuning is then done on a more specific dataset related to the final task. Classifier fine-tuning refines the model further, often using reinforcement learning from human feedback.

Howard emphasizes that language models are a form of compression—they learn a lot about the world to predict words effectively. While some argue about their limitations, he recommends using GPT-4 as the current best language model. The transcript touches on how GPT-4 can be used for various tasks and the importance of being an effective user of language models.

Limitations and Capabilities of GPT-4

  1. Examples of GPT-4's Abilities:

  2. Training and Awareness:

  3. Challenges and Limitations:

  4. Custom Instructions and Editing:

  5. Advanced Data Analysis:

In summary, while GPT-4 demonstrates impressive capabilities, users need to be aware of its limitations and actively guide it to produce accurate and meaningful responses. The transcript emphasizes the importance of understanding the training process and using custom instructions to improve the model's performance.

AI Applications in Code Writing, Data Analysis & OCR

  1. AI Applications in Code Writing, Data Analysis & OCR:

  2. Example of Code Writing:

  3. Challenges in Code Interpretation:

  4. OCR Example:

  5. Data Analysis and Table Creation:

  6. Cost and API Comparison:

  7. API Usage for Programmability:

Practical Tips on Using OpenAI API

  1. Introduction to OpenAI API:

  2. Simple Example of API Usage:

  3. Choice between GPT-4 and GPT 3.5 Turbo:

  4. Follow-Up Conversations:

  5. Creating Functions for API Interaction:

  6. Analogies and Creativity:

  7. Awareness of Usage and Rate Limits:

  8. Exploring Additional Capabilities:

Creating a Code Interpreter with Function Calling

Certainly, here are the main ideas and key points from this transcript:

  1. Introduction to Function Calling:

  2. Creating a Simple Function - Sums:

  3. Function Calling in OpenAI API:

  4. Requesting Calculation Using the Custom Function:

  5. Creating a More Powerful Function - Python Execution:

  6. Asking for Factorial Calculation:

  7. Executing Python Code and Getting the Result:

  8. Formatting the Result in Chat Format:

In summary, the transcript explores the functionality of function calling in the OpenAI API, starting with a simple function (sums) and progressing to a more powerful function (python) capable of executing Python code. The importance of safety checks and clear documentation for functions is highlighted throughout the discussion.

Using Local Language Models & GPU Options

  1. Function Role Response:

  2. Custom Functions Usage:

  3. Building a Code Interpreter from Scratch:

  4. Benefits of In-House Processing:

  5. GPU Options for Local Processing:

  6. Renting GPU Services:

  7. Choosing GPUs for Language Models:

  8. Using Transformers Library:

Fine Tuning Models & Decoding Tokens

  1. Model Selection:

  2. Fine-Tuning Process:

  3. 16-Bit vs. 8-Bit Representation:

  4. Tokenization and Decoding:

  5. Performance Optimization:

  6. Results and Efficiency:

Testing and Optimizing Models

  1. Optimizing Model Precision:

  2. Optimized Model Versions:

  3. Performance Comparisons:

  4. Combining Techniques:

  5. Fine-Tuning for Specific Tasks:

  6. Prompt Formatting:

  7. Scaling Up with Larger Models:

Retrieval Augmented Generation

  1. Retrieval Augmented Generation:

  2. Web Scraping Wikipedia:

  3. Knowledge Cutoff and Model Limitations:

  4. Sentence Transformer for Similarity:

  5. Vector Database and Pre-built Systems:

  6. Retrieval Augmented Generation in Action:

  7. Challenges and Considerations:

Fine Tuning Models

  1. Introduction to Fine-Tuning:

  2. Use of NoSQL Dataset for Fine-Tuning:

  3. Hugging Face Datasets Library:

  4. Axolotl for Fine-Tuning:

  5. Accelerated Fine-Tuning Process:

  6. Creation of Custom SQL Query:

  7. Contextual Information in Fine-Tuning:

  8. Remarkable Results:

Running Models on Macs

  1. Fine-Tuning SQL Queries:

  2. Running Models on Macs:

  3. Demonstration on Mac:

  4. Accessibility of Models on Various Platforms:

  5. Implementation on Mac:

Llama.cpp & Its Cross Platform Abilities

  1. Running Models on Macs:

  2. Llama.cpp and Cross-Platform Abilities:

  3. gguf Format in Llama.cpp:

  4. Options for Model Usage:

  5. Fast AI Discord Channel:

  6. Exciting but Early Days:

  7. Conclusion: