Driving AI Transformations

# AI Model Training Pipeline
import tensorflow as tf
from transformers import GPT2LMHeadModel

class NeuralBitsAI:
    def __init__(self, model_type="gpt2"):
        self.model = GPT2LMHeadModel.from_pretrained(model_type)
        self.optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5)
        
    def train(self, dataset, epochs=3):
        for epoch in range(epochs):
            for batch in dataset:
                with tf.GradientTape() as tape:
                    outputs = self.model(batch["input_ids"])
                    loss = outputs.loss
                gradients = tape.gradient(loss, self.model.trainable_variables)
                self.optimizer.apply_gradients(
                    zip(gradients, self.model.trainable_variables)
                )
            print(f"Epoch {epoch+1}/{epochs} completed")
    
    def generate(self, prompt, max_length=100):
        input_ids = self.tokenizer.encode(prompt, return_tensors="tf")
        output = self.model.generate(
            input_ids,
            max_length=max_length,
            num_return_sequences=1,
            temperature=0.7
        )
        return self.tokenizer.decode(output[0], skip_special_tokens=True)

# Initialize and deploy model
ai_model = NeuralBitsAI("gpt2-large")
ai_model.train(training_dataset)