Building and Quantizing a Small Language Model for Efficient Deployment

Introduction

Language models like GPT-2 have revolutionized natural language processing (NLP) with their impressive capabilities. However, these models can be resource-intensive, making them challenging to deploy in resource-constrained environments. In this blog, we’ll walk through the process of building a small language model using Hugging Face Transformers, and then apply binary quantization to make it more efficient for deployment.

Step-by-Step Guide

Step 1: Install Necessary Libraries

First, ensure you have the required libraries installed. You can use pip to install them:

pip install transformers datasets torch accelerate

Step 2: Training the Language Model

We’ll start by training a small GPT-2 model using the WikiText-2 dataset. This dataset is suitable for our needs as it provides a large corpus of text for training language models.

Here’s the script for training the model:

import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer, Trainer, TrainingArguments
from datasets import load_dataset

# Load pre-trained model and tokenizer…