Fine-Tuning LLMs Reduces Hallucinations - Here's How

AI & LLM Engineering Comprehensive course: https://bit.ly/llm-engineer Get 70% OFF by using code: LLM-70-OFF Join the AI Guild Community: 🚀 https://bit.ly/ai-guild-join 🚀 Timestamps: 00:04 - Fine-tuning enhances large language models for specific applications. 02:24 - Fine-tuning adapts pre-trained models for specific tasks efficiently. 07:43 - Optimizing parameter updates in fine-tuning large language models. 10:20 - Fine-tuning LLMs preserves prior knowledge while adapting to specific tasks. 15:25 - Overview of the fine-tuning process for adapting models to tasks. 17:42 - Fine-tuning LLMs enhances model performance and reduces costs. 21:50 - Understanding the cost and process of fine-tuning LLMs with OpenAI. 23:42 - Understanding how LLMs process text through tokenization. 28:03 - Configuring OpenAI SDK for model fine-tuning with JSONL format. 30:19 - Validating file formats and managing token calculations for fine-tuning. 34:30 - Initiating fine-tuning for a machine learning model using specific file IDs. 36:36 - Fine-tuning LLMs involves creating a job and retrieving its status. 40:44 - Loading and evaluating a fine-tuned model using binary result files. 42:56 - Fine-tuning models reduces hallucinations and improves response accuracy. 47:31 - Basics of fine-tuning OpenAI models for effective responses. 49:27 - Building a custom support chatbot for tea subscription services. 54:00 - Introduction to parameter efficient fine-tuning (PFT) using LoRA 56:12 - LoRA enables efficient fine-tuning with minimal parameters and memory usage. 1:00:38 - Understanding rank selection in fine-tuning LLMs affects performance and generalizability. 1:02:50 - Understanding parameter tuning in fine-tuning LLMs. 1:07:04 - Setting up the environment for fine-tuning LLMs using datasets and transformers. 1:09:06 - Setting up model and parameters for fine-tuning. 1:12:48 - Preparing two datasets for model training and tokenization. 1:14:40 - Configuring and training sentiment analysis models with specific datasets and parameters. 1:18:34 - Setting up label maps and testing sentiment and topic models. 1:20:32 - Training a sentiment analysis model using BERT. 1:24:43 - Training and evaluating sentiment and topic analysis models with checkpoints. 1:26:56 - Fine-tuning models efficiently using minimal parameters and diverse datasets. 1:30:58 - Creating a sentiment analysis API using model classes and responses. 1:32:59 - Loading and using a fine-tuned model for sentiment prediction. 1:36:54 - Setting up and running a FastAPI server for sentiment analysis. 1:38:58 - Creating a test API file to handle predictions from the API. 1:43:02 - Improving sentiment analysis requires more training data and fine-tuning techniques. 1:45:07 - Fine-tuning models enhances performance and encourages further exploration. 1:49:15 - Exploring AI model sentiment analysis using fine-tuning techniques. 1:51:24 - Understanding model training setups across different platforms. 1:55:30 - Training a new model with IMDb data set in Python and PyTorch. 1:57:48 - Understanding and fine-tuning large language models is essential for accurate sentiment analysis.