Foundation Models.

Foundation models are a type of pre-trained generative AI model that offers immense versatility by being adaptable for various specific tasks.g Data

What are Foundation Models?

Foundation models are a type of pre-trained generative AI model that offers immense versatility by being adaptable for various specific tasks. They undergo extensive training on vast and diverse datasets, enabling them to grasp general patterns and relationships within the data. This initial pre-training phase equips the models with a strong foundational understanding across different domains, laying the groundwork for further fine-tuning.

Characteristics of Foundation Models

Foundation models are designed with transfer learning in mind, meaning they can effectively apply the knowledge acquired during pre-training to new, related tasks. They can be fine-tuned and specialized towards specific tasks, covering a spectrum from general instruction following, and chatbot interactions, to specific tasks such as sentiment analysis. Foundation models are distinguished by their size, which is measured by the number of parameters, the number of training tokens, and the number of FLOPs needed for training.

Foundation vs Task-Specific Models

Task-specific models are for specialized use cases and their sole reason for existing is to perform these use cases. They can only do the THING they have been taught to do and nothing different (or more).

How do we fine-tune Foundation Models?

Foundation models can be fine-tuned for specific tasks, allowing them to achieve high performance on those tasks. This fine-tuning process involves training the model on a specific dataset and adjusting the model's parameters to optimize its performance on that task. 

The code sample below shows fine-tuning a pre-trained BERT model on a specific task using the Hugging Face Transformers library.

import torch
from transformers import BertTokenizer, BertModel

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

# Fine-tune the model on a specific task
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)

# Train the model
for epoch in range(5):
    model.train()
    total_loss = 0
    for batch in train_data:
        input_ids = batch['input_ids'].to(device)
        attention_mask = batch['attention_mask'].to(device)
        labels = batch['labels'].to(device)

        optimizer.zero_grad()

        outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
        loss = criterion(outputs, labels)

        loss.backward()
        optimizer.step()

        total_loss += loss.item()
    print(f'Epoch {epoch+1}, Loss: {total_loss / len(train_data)}')

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