Train a Small Language Model for Disease Symptoms | Step-by-Step Tutorial

Dive into the world of Language Model as I guide you through the process of training a small language model using GPT-2! In this tutorial, we'll explore how to leverage the powerful distilgpt2 transformer to understand diseases and symptoms better. 📋 Tutorial Highlights: Dataset Loading: Learn how to load a relevant dataset on diseases and symptoms from Hugging Face datasets. Tokenization and Model Setup: Understand the crucial steps of tokenization using GPT-2's tokenizer and initializing the language model. Training Loop: Walk through the training loop, exploring each epoch, monitoring training and validation losses, and ensuring your model is learning effectively. Hyperparameter Tuning: Fine-tune your model by adjusting batch sizes, learning rates, and more. Text Generation: Witness the power of your trained model by generating meaningful text based on input strings. 🤖 Why Train a domain specific Language Model like MedLLM? Training a language model allows you to teach your model about the relationships between diseases and symptoms, enabling it to generate informative and context-aware responses. 🔔 Don't forget to like, comment, and subscribe for more exciting tutorials on Gen AI and machine learning! Your support keeps the channel thriving. Join this channel to get access to perks: https://www.youtube.com/channel/UC-zVytOQB62OwMhKRi0TDvg/join 📁 Download Code: https://github.com/AIAnytime/Training-Small-Language-Model 📚 Resources: Hugging Face Model: https://huggingface.co/distilgpt2 Dataset Source: https://huggingface.co/datasets/QuyenAnhDE/Diseases_Symptoms #generativeai #llm #ai