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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.