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import tensorflow as tf
from transformers import TFAutoModelForCausalLM, AutoTokenizer
import datasets
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.losses import SparseCategoricalCrossentropy
import numpy as np
class VedaTrainer:
"""
Advanced training pipeline for VEDA LLM
"""
def __init__(self, base_model="gpt2"):
self.base_model = base_model
self.tokenizer = AutoTokenizer.from_pretrained(base_model)
self.model = None
# Configure tokenizer for VEDA
if self.tokenizer.pad_token is None:
self.tokenizer.pad_token = self.tokenizer.eos_token
# Add VEDA special tokens
special_tokens = {
"pad_token": "[VEDA_PAD]",
"bos_token": "[VEDA_START]",
"eos_token": "[VEDA_END]",
"unk_token": "[VEDA_UNK]"
}
self.tokenizer.add_special_tokens(special_tokens)
def prepare_veda_dataset(self, dataset_name="wikitext", dataset_config="wikitext-2-raw-v1"):
"""Prepare dataset for VEDA training"""
print("📚 Loading dataset for VEDA training...")
dataset = datasets.load_dataset(dataset_name, dataset_config)
def tokenize_function(examples):
# Add VEDA tokens
texts = [f"[VEDA_START] {text} [VEDA_END]" for text in examples["text"]]
return self.tokenizer(
texts,
truncation=True,
padding=True,
max_length=256,
return_tensors="tf"
)
tokenized_dataset = dataset.map(tokenize_function, batched=True)
return tokenized_dataset
def create_veda_model(self):
"""Create VEDA model with custom architecture"""
print("🏗️ Building VEDA model...")
# Load base model
self.model = TFAutoModelForCausalLM.from_pretrained(self.base_model)
self.model.resize_token_embeddings(len(self.tokenizer))
# Compile with VEDA optimizer settings
optimizer = Adam(
learning_rate=3e-5,
beta_1=0.9,
beta_2=0.95,
epsilon=1e-9
)
loss = SparseCategoricalCrossentropy(from_logits=True)
self.model.compile(
optimizer=optimizer,
loss=loss,
metrics=['accuracy']
)
return self.model
def train_veda(self, dataset, epochs=3, batch_size=4):
"""Train VEDA model"""
model = self.create_veda_model()
print("🎯 Starting VEDA training...")
# Prepare training data
train_data = dataset["train"].to_tf_dataset(
columns=["input_ids", "attention_mask", "labels"],
shuffle=True,
batch_size=batch_size
)
# Training
history = model.fit(
train_data,
epochs=epochs,
validation_split=0.1
)
print("✅ VEDA training completed!")
return model, history
def save_veda_model(self, model, path="./veda_model"):
"""Save trained VEDA model"""
print(f"💾 Saving VEDA model to {path}...")
model.save_pretrained(path)
self.tokenizer.save_pretrained(path)
print("✅ VEDA model saved!")
# Usage
if __name__ == "__main__":
trainer = VedaTrainer()
dataset = trainer.prepare_veda_dataset()
model, history = trainer.train_veda(dataset)
trainer.save_veda_model(model) |