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Building a Small-Scale Language Model: The Python Setup and Beyond

Resources to help your team think about how to structure the delivery and presentation of your Project:

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Comparing AI Language Models coda.io⁠

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Building a Small-Scale Language Model: The Python Setup and Beyond coda.io⁠

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For the course project: You are building a small scale, MVP “toy” version or a proof of concept version of the Generative AI Language Model.

Some of the steps in your project:

Stepping up your PYTHON ENVIRONMENT

Find some Training Data.

Using some PYTHON Tooling such as PYTORCH to train your Model.

Interaction: Good enough to use a Text prompt interface. Refer to your slides for a simple example.

Learning Outcomes:

How to set up a Python environment for building a small-scale language model, much like GPT-3 or GPT-4.

While these large models are products of massive computational resources and extensive training data, your project is to create smaller models that illustrate the fundamental principles and processes.

Python Environment Setup

First things first. Python, as a versatile, accessible, and well-supported language, is the ideal platform for our machine learning endeavors. I will assume that you already have a basic familiarity with Python; if not, I encourage you to familiarize yourself with it as a first step.

Let's start with Python setup. We will need Anaconda Python 3.8 or newer. You can download it from the official Python website. After successful installation, we recommend creating a virtual environment.

This is a self-contained Python environment which helps to keep dependencies required by different projects separate.

To create a virtual environment, navigate to your project directory and execute the following commands:

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python3 -m venv env

source env/bin/activate

Installing Essential Libraries

Now, let's talk about the libraries you need.

First and foremost, we need TensorFlow or PyTorch.

These are open-source libraries for high-performance numerical computation, ideal for machine learning tasks.

Training your ML Ops Model is very computational intense. People are buying USB GPUs for more number crunching power.

TensorFlow

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Lecture 7: Transformer Models: The Backbone of Modern NLP coda.io⁠

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In this lecture, we'll use TensorFlow, but much of what I'm saying would apply equally to PyTorch with some syntax adjustments. Install TensorFlow by running:

pythonCopy code

pip install tensorflow

Next, install Transformers, a state-of-the-art Natural Language Processing (NLP) library by Hugging Face. It provides thousands of pretrained models and allows for seamless model training and serving. Install it with:

pythonCopy code

pip install transformers

Data Collection and Processing

With our environment set, let's move onto data. Good data is essential for training a robust language model.

You may use a custom corpus or an existing dataset like the Wikipedia dump, CommonCrawl, or the BookCorpus.

Once you have the data, you need to clean and process it.

Cleaning might involve removing unnecessary spaces, punctuation, or links, while processing involves tokenizing your text. Tokens are chunks of text, typically words or parts of words.

Tokenization is crucial for preparing data for a language model.

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from transformers import BertTokenizer

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

tokenized_text = tokenizer.tokenize(your_text)

Building the Language Model

Now, we come to the heart of our discussion, building the language model.

Using the Transformers library, you can leverage a pre-existing model architecture. The GPT-2 model, for instance, can be used as a base to build upon.

GPT-2 is a transformers-based model that was pre-trained on a diverse range of Internet text.

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from transformers import GPT2LMHeadModel

model = GPT2LMHeadModel.from_pretrained('gpt2')

Training the Model

After preparing the model and data, it's time for training.

Training involves feeding your tokenized data to the model and adjusting the model’s parameters to minimize errors.

Training a language model is computationally intensive and time-consuming, even for smaller models.

from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(

output_dir='./results',

num_train_epochs=3,

per_device_train_batch_size=16,

per_device_eval_batch_size=64,

warmup_steps=500,

weight_decay=0.01,

logging_dir='./logs',

)

trainer = Trainer(

model=model,

args=training_args,

train_dataset=train_dataset,

eval_dataset=test_dataset

)

trainer.train()

Evaluating and Using the Model: We use Training Metrics at this stage.

After the model is trained, we evaluate its performance using various metrics, such as perplexity.

Finally, the model is ready to generate text.

pythonCopy code

generated_text = model.generate(start_prompt, max_length=100, temperature=0.7)

In summary, the process involves: For your Project Presentation: I will ask you to discuss these questions:

Setting up a Python environment

Preparing and processing data

selecting a model architecture

training the mode

evaluating and using the model: By what Metrics

Building a language model like GPT-3 or GPT-4 is a complex task that involves considerable computational resources.

In your Project, you are making a Toy Version Minimal Viable Product to help you understanding the fundamental steps and principles can help you get started on your own machine learning projects, even on a smaller scale.

I hope this lecture has provided a helpful introduction to the setup and processes involved in building a language model with Python.

The journey of learning and exploration in the field of Natural Language Processing and machine language engineering is a long one, and this is just the beginning.

Thank you for your time. I look forward to seeing what you will build with these tools and principles.

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