To prepare for training large language models, you will need to follow a comprehensive process that involves data collection, model selection and configuration, model training, and evaluation and fine-tuning. Here's a detailed breakdown of the steps you need to take to prepare for training large language models:
1.Data Collection and Preprocessing:
Gather a large and diverse training dataset from sources such as books, websites, articles, and open datasets. Popular public sources to find datasets include Kaggle, Google Dataset Search, Hugging Face, Data.gov, and Wikipedia database.
Clean and preprocess the training data by converting it to lowercase, removing stop words, and tokenizing the text into sequences of tokens that make up the text.
2.Model Selection and Configuration:
Choose a suitable large language model architecture, such as the Transformer architecture, which is commonly used for sophisticated natural language processing (NLP) applications.
Specify key elements of the model, such as the number of layers in transformer blocks, number of attention heads, loss function, and hyperparameters based on the desired use case and the training data.
3.Model Training:
Train the model on the preprocessed text data using supervised learning. During training, the model is presented with a sequence of words and is trained to predict the next word in the sequence. The model adjusts its weights based on the difference between its prediction and the actual next word.
Utilize techniques such as model parallelism to decrease training time. Model parallelism involves spreading different parts of a large model across multiple GPUs, allowing the model to be trained in a distributed manner with AI chips. Common types of model parallelism include data parallelism, sequence parallelism, pipeline parallelism, and tensor parallelism.
4.Evaluation and Fine-Tuning:
Evaluate the trained model on a test dataset that has not been used as a training dataset to measure the model’s performance.
Based on the evaluation results, fine-tune the model by adjusting its hyperparameters, changing the architecture, or training on additional data to improve its performance.
By following these steps, you can effectively prepare for training large language models. Keep in mind that training large language models requires significant computational resources and expertise in machine learning and natural language processing.