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.

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📁 Download Code: https://github.com/AIAnytime/Training...
📚 Resources:
Hugging Face Model: https://huggingface.co/distilgpt2
Dataset Source: https://huggingface.co/datasets/Quyen...

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