Run an LLM on M1 Mac 8BG

Dec 13, 2023 ·

Language models are powerful tools used to create accurate and natural-sounding text. Long Short Term Memory (LSTM) networks are a type of recurrent neural network used for natural language processing and understanding. In this article, Christian Seitz outlines how to train an LSTM-based language model using the Hugging Face Transformers library. The article covers how to set up the necessary environment, preprocessing of data, training and hyperparameter tuning, analyzing and evaluating results, and saving and loading models.

Preprocessing is the process of preparing data for modeling. It involves cleaning, tokenizing, and formatting the data so that it can be understood by the model. This article explains how to use the TorchText library to do the preprocessing. Once the data is prepared, the next step is to define the model and its parameters. The model chosen in this article is a GPT-2 transformer, which is an autoregressive Transformer-based language model. The article outlines the parameters and the structure of the model.

The next step is to train the model. Training involves defining an objective function, setting the hyperparameters, and running the optimization loop. A learning rate scheduler is also used to adjust the learning rate as the training progresses. The article provides code snippets and explanations on how to set up the training process and tune the hyperparameters.

Once the model is trained, the next step is to analyze and evaluate the results. The author explains how to use the HuggingFace Evaluate function to get metrics such as perplexity and accuracy. The article also explains how to save and load the model for future use.

In conclusion, this article provides a detailed guide on how to train a language model using the Hugging Face Transformers library. It covers the necessary steps from setting up the environment to analyzing and evaluating the results.