Slide 1:

Introduction to Foundation Models

Foundation models, also known as large language models or pre-trained models, are powerful AI systems trained on vast amounts of data to learn patterns and relationships in natural language. These models serve as a foundation for various downstream tasks in natural language processing (NLP), computer vision, and other domains. They can be fine-tuned on specific datasets for tasks like text generation, summarization, translation, and more.

Code:

Don’t worry, this is easier than it looks! Here’s how we can tackle this:

from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load pre-trained GPT-2 model and tokenizer
model = GPT2LMHeadModel.from_pretrained('gpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')

# Tokenize input text
input_text = "The quick brown fox"
input_ids = tokenizer.encode(input_text, return_tensors='pt')

# Generate text using the pre-trained model
output = model.generate(input_ids, max_length=50, do_sample=True, top_k=50, top_p=0.95, num_return_sequences=1)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)

print(generated_text)

Slide 2:

Fine-tuning Foundation Models

One of the key advantages of foundation models is their ability to be fine-tuned on specific datasets for various downstream tasks. Fine-tuning involves training the pre-trained model on a smaller, task-specific dataset, allowing it to adapt its knowledge to the target domain and task. This process can significantly improve the model’s performance on the desired task while leveraging the general knowledge learned during pre-training.

Code:

Let me walk you through this step by step! Here’s how we can tackle this:

from transformers import GPT2LMHeadModel, GPT2Tokenizer, Trainer, TrainingArguments

# Load pre-trained GPT-2 model and tokenizer
model = GPT2LMHeadModel.from_pretrained('gpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')

# Prepare task-specific dataset
# ... (code to load and preprocess dataset)

# Define training arguments
training_args = TrainingArguments(
    output_dir='./results',
    num_train_epochs=3,
    per_device_train_batch_size=8,
    # ... (add other training configurations)
)

# Define trainer and fine-tune the model
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    # ... (add other trainer configurations)
)

trainer.train()

Slide 3:

Foundation Models for Natural Language Generation

Foundation models excel at natural language generation tasks, such as text completion, creative writing, and open-ended dialogue generation. These models can generate coherent and fluent text by learning patterns and relationships from large text corpora during pre-training. Fine-tuning on specific domains or tasks can further improve the quality and relevance of the generated text.

Code:

Don’t worry, this is easier than it looks! Here’s how we can tackle this:

from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load pre-trained GPT-2 model and tokenizer
model = GPT2LMHeadModel.from_pretrained('gpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')

# Tokenize input text
input_text = "Once upon a time, there was a"
input_ids = tokenizer.encode(input_text, return_tensors='pt')

# Generate text using the pre-trained model
output = model.generate(input_ids, max_length=200, do_sample=True, top_k=50, top_p=0.95, num_return_sequences=1)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)

print(generated_text)

Slide 4:

Foundation Models for Natural Language Understanding

Foundation models can also be leveraged for natural language understanding tasks, such as text classification, named entity recognition, and sentiment analysis. By fine-tuning these models on labeled datasets for specific tasks, they can learn to understand and classify text based on the provided labels or annotations.

Code:

Let’s make this super clear! Here’s how we can tackle this:

from transformers import BertForSequenceClassification, BertTokenizer

# Load pre-trained BERT model and tokenizer
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# Tokenize input text
text = "This movie was absolutely amazing! I loved every minute of it."
inputs = tokenizer(text, return_tensors='pt')

# Classify the input text
outputs = model(**inputs)
logits = outputs.logits
predicted_class = logits.argmax().item()

# Print the predicted sentiment (0: negative, 1: positive)
print("Predicted sentiment:", "Positive" if predicted_class == 1 else "Negative")

Slide 5:

Foundation Models for Computer Vision

While originally designed for natural language processing tasks, foundation models have also shown remarkable capabilities in computer vision tasks, such as image classification, object detection, and image captioning. By pre-training on large image-text datasets, these models can learn to understand and generate text descriptions of visual content.

Code:

Here’s where it gets exciting! Here’s how we can tackle this:

from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
import torch
from PIL import Image

# Load pre-trained vision encoder-decoder model, feature extractor, and tokenizer
model = VisionEncoderDecoderModel.from_pretrained('google/vit-gpt2-image-captioning')
feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-gpt2-image-captioning')
tokenizer = AutoTokenizer.from_pretrained('google/vit-gpt2-image-captioning')

# Load and preprocess the image
image = Image.open("example_image.jpg")
pixel_values = feature_extractor(images=image, return_tensors='pt').pixel_values

# Generate a caption for the image
output_ids = model.generate(pixel_values, max_length=50, num_beams=4, early_stopping=True)
caption = tokenizer.decode(output_ids[0], skip_special_tokens=True)

print(caption)

Slide 6:

Foundation Models for Multimodal Tasks

Foundation models can also be applied to multimodal tasks that involve multiple modalities, such as image-text or video-text tasks. These models can learn to understand and generate content across different modalities, enabling applications like visual question answering, image captioning, and video summarization.

Code:

Let me walk you through this step by step! Here’s how we can tackle this:

from transformers import ViltForImagesAndTextClassification, ViTFeatureExtractor, BertTokenizer
import torch
from PIL import Image

# Load pre-trained multimodal model, feature extractor, and tokenizer
model = ViltForImagesAndTextClassification.from_pretrained('google/vilt-b32-finetuned-vqa')
feature_extractor = ViTFeatureExtractor.from_pretrained('google/vilt-b32-finetuned-vqa')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# Load and preprocess the image and question
image = Image.open("example_image.jpg")
question = "What is the color of the car?"
pixel_values = feature_extractor(images=image, return_tensors='pt').pixel_values
text_inputs = tokenizer(question, return_tensors='pt', padding=True, truncation=True)

# Get the model output for the image and question
outputs = model(pixel_values=pixel_values, **text_inputs)
logits = outputs.logits

# Get the predicted answer
predicted_class = logits.argmax(-1).item()
answer_labels = model.config.id2label
predicted_answer = answer_labels[predicted_class]

print(f"Question: {question}")
print(f"Predicted Answer: {predicted_answer}")

Slide 7:

Responsible AI and Foundation Models

While foundation models offer immense potential, their responsible development and deployment is crucial. Concerns around bias, privacy, and misuse must be addressed through techniques like debiasing, ethical AI principles, and reliable security measures. Researchers and practitioners must prioritize ethical considerations and mitigate potential risks associated with these powerful models.

Code:

This next part is really neat! Here’s how we can tackle this:

from transformers import DataProcessor
import pandas as pd

class BiasMitigationProcessor(DataProcessor):
    def get_train_examples(self, data_dir):
        data = pd.read_csv(os.path.join(data_dir, 'train.csv'))
        return self._create_examples(data, 'train')

    def get_dev_examples(self, data_dir):
        data = pd.read_csv(os.path.join(data_dir, 'dev.csv'))
        return self._create_examples(data, 'dev')

    def get_test_examples(self, data_dir):
        data = pd.read_csv(os.path.join(data_dir, 'test.csv'))
        return self._create_examples(data, 'test')

    def _create_examples(self, data, set_type):
        examples = []
        for _, row in data.iterrows():
            text = row['text']
            label = row['label']
            examples.append(InputExample(guid=f'{set_type}-{idx}', text_a=text, label=label))
        return examples

# Use the BiasMitigationProcessor to load and preprocess data
# Fine-tune the model on the debiased data
# Implement additional bias mitigation techniques during training and inference

Slide 8:

Foundation Models for Few-shot Learning

One of the remarkable capabilities of foundation models is their ability to perform few-shot learning, where they can learn to solve new tasks with only a few examples or prompts. This is achieved through the models’ ability to generalize from the vast knowledge acquired during pre-training, enabling them to quickly adapt to new tasks with minimal additional data.

Code:

Let’s break this down together! Here’s how we can tackle this:

from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load pre-trained GPT-2 model and tokenizer
model = GPT2LMHeadModel.from_pretrained('gpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')

# Define a few-shot learning prompt
prompt = "Here are some examples of simple addition problems and their solutions:\n\n" \
         "2 + 3 = 5\n4 + 7 = 11\n1 + 9 = 10\n\n" \
         "Here is a new addition problem: 8 + 5 ="

# Tokenize the prompt
input_ids = tokenizer.encode(prompt, return_tensors='pt')

# Generate the solution using the pre-trained model
output = model.generate(input_ids, max_length=50, do_sample=True, top_k=50, top_p=0.95, num_return_sequences=1)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)

print(generated_text)

Slide 9:

Foundation Models for Transfer Learning

Transfer learning is another powerful application of foundation models, where the knowledge acquired during pre-training can be transferred and fine-tuned on a wide range of downstream tasks. This way allows for efficient model adaptation and improved performance, even with limited task-specific data.

Code:

This next part is really neat! Here’s how we can tackle this:

from transformers import BertForSequenceClassification, BertTokenizer

# Load pre-trained BERT model and tokenizer
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# Prepare task-specific dataset
# ... (code to load and preprocess dataset)

# Fine-tune the pre-trained model on the task-specific dataset
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset
)

trainer.train()

# Evaluate the fine-tuned model on the test set
test_results = trainer.evaluate(test_dataset)
print(f"Test accuracy: {test_results['eval_accuracy']}")

Slide 10:

Foundation Models for Domain Adaptation

Foundation models can be further adapted to specific domains by fine-tuning on domain-specific data. This process, known as domain adaptation, allows the models to acquire domain-specific knowledge and terminology, improving their performance on tasks within that domain.

Code:

Here’s where it gets exciting! Here’s how we can tackle this:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

# Load pre-trained model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')

# Prepare domain-specific dataset
# ... (code to load and preprocess dataset)

# Fine-tune the pre-trained model on the domain-specific dataset
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset
)

trainer.train()

# Evaluate the domain-adapted model on a domain-specific test set
test_results = trainer.evaluate(test_dataset)
print(f"Domain-specific test accuracy: {test_results['eval_accuracy']}")

Slide 11:

Foundation Models for Continual Learning

Continual learning is the ability of a model to continuously learn and adapt to new tasks or domains without catastrophically forgetting previously acquired knowledge. Foundation models can leverage techniques like rehearsal, regularization, and architecture modifications to enable continual learning, allowing them to expand their capabilities over time.

Code:

Let’s make this super clear! Here’s how we can tackle this:

from transformers import GPT2LMHeadModel, GPT2Tokenizer
import numpy as np

# Load pre-trained GPT-2 model and tokenizer
model = GPT2LMHeadModel.from_pretrained('gpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')

# Continual learning loop
for task_data in task_datasets:
    # Prepare task-specific dataset
    # ... (code to load and preprocess task_data)

    # Fine-tune the model on the new task data
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset
    )

    trainer.train()

    # Evaluate the model on the new task
    test_results = trainer.evaluate(test_dataset)
    print(f"Task {task_id} test accuracy: {test_results['eval_accuracy']}")

    # Optional: Apply rehearsal or regularization techniques to mitigate forgetting

Slide 12:

Foundation Models for Zero-shot and Few-shot Learning

Zero-shot and few-shot learning are two closely related concepts in the context of foundation models. Zero-shot learning refers to the ability of a model to perform a new task without any task-specific training data, relying solely on its pre-trained knowledge. Few-shot learning involves fine-tuning the model on a small number of examples for the new task.

Code:

This next part is really neat! Here’s how we can tackle this:

from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load pre-trained GPT-2 model and tokenizer
model = GPT2LMHeadModel.from_pretrained('gpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')

# Zero-shot learning example
prompt = "Translate the following English sentence to French: 'The cat is sitting on the mat.'"
input_ids = tokenizer.encode(prompt, return_tensors='pt')
output = model.generate(input_ids, max_length=50, do_sample=True, top_k=50, top_p=0.95, num_return_sequences=1)
french_translation = tokenizer.decode(output[0], skip_special_tokens=True)
print(french_translation)

# Few-shot learning example
few_shot_examples = [
    "English: The dog is barking.\nFrench: Le chien aboie.",
    "English: I am hungry.\nFrench: J'ai faim.",
    "English: She is reading a book.\nFrench: Elle lit un livre."
]

prompt = "\n".join(few_shot_examples) + "\n\nEnglish: The weather is nice today.\nFrench: "
input_ids = tokenizer.encode(prompt, return_tensors='pt')
output = model.generate(input_ids, max_length=50, do_sample=True, top_k=50, top_p=0.95, num_return_sequences=1)
french_translation = tokenizer.decode(output[0], skip_special_tokens=True)
print(french_translation)

Slide 13:

Foundation Models for Prompt Engineering

Prompt engineering is a crucial aspect of leveraging foundation models effectively. Well-crafted prompts can significantly improve the model’s performance on specific tasks by providing the right context and guidance. Researchers and practitioners are actively exploring prompt engineering techniques, such as prompt decomposition, prompt tuning, and prompt ensembling.

Code:

Don’t worry, this is easier than it looks! Here’s how we can tackle this:

from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load pre-trained GPT-2 model and tokenizer
model = GPT2LMHeadModel.from_pretrained('gpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')

# Prompt engineering example
prompt = "You are a helpful AI assistant. Here is some background information on the task: \n\n" \
         "<background information>\n\n" \
         "Based on the above information, please <task description>."

input_ids = tokenizer.encode(prompt, return_tensors='pt')
output = model.generate(input_ids, max_length=500, do_sample=True, top_k=50, top_p=0.95, num_return_sequences=1)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)

print(generated_text)

Slide 14:

Additional Resources

For further exploration of foundation models, here are some valuable resources from arXiv.org:

1. “Language Models are Few-Shot Learners” by Radford et al. (https://arxiv.org/abs/2005.14165)
2. “Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer” by Raffel et al. (https://arxiv.org/abs/1910.10683)
3. “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding” by Devlin et al. (https://arxiv.org/abs/1810.04805)
4. “GPT-3: Language Models are Few-Shot Learners” by Brown et al. (https://arxiv.org/abs/2005.14165)
5. “Multimodal Neurons in Artificial Neural Networks” by Akila et al. (https://arxiv.org/abs/2005.14165)

These papers cover various aspects of foundation models, including pre-training, transfer learning, few-shot learning, and multimodal learning.

🎊 Awesome Work!

You’ve just learned some really powerful techniques! Don’t worry if everything doesn’t click immediately - that’s totally normal. The best way to master these concepts is to practice with your own data.

What’s next? Try implementing these examples with your own datasets. Start small, experiment, and most importantly, have fun with it! Remember, every data science expert started exactly where you are right now.

Keep coding, keep learning, and keep being awesome! 🚀