Fine-Tuning LLMs with Conversation Data

Study Notes

Fine-Tuning LLMs with Conversation Data

Creating a custom AI model by fine-tuning a pre-trained large language model (LLM) using your conversation history is possible.

Data Collection

Objective: Gathering and preparing your conversation data.
Task: Extracting chat history from various platforms (e.g., ChatGPT, Rewind, Google Assistant).
Tools/Resources: APIs or manual export tools (ChatGPT export, Rewind logs).
Normalization: Converting conversations into a structured format (JSON or CSV).
Tools/Resources: Python (e.g., pandas).
Anonymization: Removing sensitive personal data.
Tools/Resources: Regex methods, libraries like presidio.

Data Preprocessing

Objective: Preparing the data for fine-tuning.
Step: Tokenization—breaking text into model-compatible tokens.
Tools/Resources: Hugging Face tokenizers.
Step: Adding metadata—annotating conversations with timestamps, topics, intents.
Tools/Resources: A schema (e.g., timestamp, topic, intent, message).
Step: Deduplication—removing redundant conversations.
Tools/Resources: Python set(), fuzzywuzzy for similarity.
Step: Formatting for model training—converting data to the required format (e.g., {"prompt": ..., "completion": ...}).
Tools/Resources: OpenAI Fine-Tuning format, Hugging Face Dataset format.

Model Selection

Objective: Choosing the model architecture and framework.
Framework: OpenAI GPT-3/4—simple API integration, readily available for fine-tuning.
Framework: Hugging Face Transformers—allows for extensive control over the model and training process.
Framework: Google’s T5 or BERT—efficient for dedicated tasks like summarization or question answering.

Fine-Tuning Process

Objective: Training the model with the collected data.
Step: Preparing the training dataset—splitting data into training, validation, and test sets.
Tools/Resources: Python's scikit-learn (sklearn).
Step: Setting up the fine-tuning pipeline—defining hyperparameters.
Tools/Resources: Hugging Face Trainer, OpenAI fine-tuning API.
Step: Training the model—starting the training process and monitoring.
Tools/Resources: GPU/TPU, cloud platforms (AWS, GCP), local NVIDIA GPUs.
Step: Evaluating—testing on unseen conversations to assess accuracy, relevance, and coherence.
Tools/Resources: Metrics like BLEU, ROUGE, or manual analysis.

Deployment

Objective: Making the fine-tuned model accessible.
Step: Hosting the model—deploying on cloud or local servers.
Tools/Resources: Hugging Face Spaces, AWS SageMaker, OpenAI Deployment API.
Step: Creating an interface—building a user-friendly interaction tool (e.g., CLI or web-based).
Tools/Resources: Streamlit, Flask, FastAPI.
Step: Monitoring and iterating—continuously evaluating performance and retraining with updated data.
Tools/Resources: Weights & Biases, or custom logging.

Key Considerations

Data Privacy: Anonymizing sensitive data.
Compute Resources: Cloud GPUs for large-scale training.
Scalability: Planning model updates to accommodate more data.
Alignment: Assessing model alignment with desired outputs.

Description

This quiz focuses on the process of fine-tuning large language models (LLMs) using personal conversation data. It covers data collection, preprocessing, and normalization techniques necessary for creating a custom AI model. Test your knowledge on the tools and methods used in this advanced machine learning task.