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