Create veda_train.py

veda_train.py

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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)