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