š There are two methods to achieve specific use cases with GPT: fine tuning and creating a knowledge base.
āØ Fine tuning is effective for creating a large knowledge model with specific behaviors, such as imitating someone like Trump.
š Creating an embedding or vector database is more suitable for accurate data retrieval, such as legal or financial information.
š Fine-tuning large language models for specific use cases is beneficial.
āļø Choosing the appropriate model for fine-tuning is essential, with Falcon being a recommended option.
š§© Preparing high-quality data sets is crucial for the success of the fine-tuned model.
š You can find and download relevant datasets for training large language models.
š Fine-tuning with your own private datasets is recommended.
š GPT can generate training data to be used for fine-tuning.
š The video demonstrates how to use GPT to perform specific tasks.
āļø The method involves fine-tuning the model and importing data from a CSV file.
š§ Several libraries and tools are required, such as Hugging Face and Google Colab.
š Using a specific type of method called Low ranks adapters, it is possible to fine-tune a large language model for conversation tasks, making it more efficient and fast.
š The base model of GPT, without fine-tuning, does not generate good results for a specific task, as it struggles to understand the context.
š” Generating good results for fine-tuning doesn't require a large dataset; even 100 or 200 rows can suffice.
š” Properly load and map data sets into a specified format.
āļø Create training arguments and start the training process.
š¾ Save the trained model locally or upload it to a repository.
šāāļøšØļø Generate a result using the trained model for a specific prompt.
š Fine-tuning a large language model can improve its results with more data.
š» Training a 7B model is recommended due to lower computer power requirements.
š” Possible use cases for fine-tuned models include customer support, legal documents, medical diagnosis, and financial advisories.