train_lora.py

from transformers import (
    AutoTokenizer, 
    AutoModelForCausalLM, 
    TrainingArguments, 
    Trainer,
    DataCollatorForLanguageModeling
)
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
from datasets import load_dataset
import torch
import json

MODEL_NAME = "Qwen/Qwen2.5-0.5B-Instruct"
MAX_LENGTH = 512

# Load tokenizer and model
print("Loading model and tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
tokenizer.pad_token = tokenizer.eos_token

model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Improved LoRA configuration
lora_config = LoraConfig(
    r=16,  # Increased from 8 for better capacity
    lora_alpha=32,  # Increased from 16
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],  # More modules
    lora_dropout=0.1,  # Increased for better regularization
    bias="none",
    task_type="CAUSAL_LM"
)

model = get_peft_model(model, lora_config)
model.print_trainable_parameters()

# Load and split dataset
print("Loading dataset...")
dataset = load_dataset("json", data_files="train.jsonl")

# Split into train/validation (80/20)
split_dataset = dataset["train"].train_test_split(test_size=0.2, seed=42)
train_dataset = split_dataset["train"]
eval_dataset = split_dataset["test"]

print(f"Training samples: {len(train_dataset)}")
print(f"Validation samples: {len(eval_dataset)}")

def tokenize_function(examples):
    """Tokenize the examples with proper formatting"""
    texts = []
    for messages in examples["messages"]:
        # Apply chat template
        text = tokenizer.apply_chat_template(
            messages,
            tokenize=False,
            add_generation_prompt=False
        )
        texts.append(text)
    
    # Tokenize with padding and truncation
    tokenized = tokenizer(
        texts,
        truncation=True,
        max_length=MAX_LENGTH,
        padding="max_length",
        return_tensors=None
    )
    
    # Labels are the same as input_ids for causal LM
    tokenized["labels"] = tokenized["input_ids"].copy()
    
    return tokenized

# Tokenize datasets
print("Tokenizing datasets...")
tokenized_train = train_dataset.map(
    tokenize_function,
    batched=True,
    remove_columns=train_dataset.column_names
)

tokenized_eval = eval_dataset.map(
    tokenize_function,
    batched=True,
    remove_columns=eval_dataset.column_names
)

# Improved training arguments
training_args = TrainingArguments(
    output_dir="./brad-ai-lora",
    
    # Training hyperparameters
    num_train_epochs=5,  # Increased from 3
    per_device_train_batch_size=2,  # Increased from 1
    per_device_eval_batch_size=2,
    gradient_accumulation_steps=4,  # Effective batch size = 8
    
    # Learning rate and scheduling
    learning_rate=3e-4,  # Slightly increased
    lr_scheduler_type="cosine",  # Better than default
    warmup_ratio=0.1,  # Warmup for 10% of training
    
    # Optimization
    optim="adamw_torch",
    weight_decay=0.01,
    max_grad_norm=1.0,
    
    # Logging and evaluation
    logging_steps=10,
    eval_strategy="steps",
    eval_steps=50,
    save_strategy="steps",
    save_steps=50,
    save_total_limit=3,  # Keep only best 3 checkpoints
    
    # Performance
    fp16=True,  # Mixed precision training
    dataloader_num_workers=2,
    
    # Monitoring
    load_best_model_at_end=True,
    metric_for_best_model="eval_loss",
    greater_is_better=False,
    
    # Misc
    report_to="none",  # Change to "tensorboard" if you want logging
    seed=42
)

# Create trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_train,
    eval_dataset=tokenized_eval,
    tokenizer=tokenizer
)

# Train the model
print("Starting training...")
trainer.train()

# Save the final model
print("Saving model...")
trainer.save_model("./brad-ai-lora-final")
tokenizer.save_pretrained("./brad-ai-lora-final")

# Evaluate final model
print("Final evaluation:")
eval_results = trainer.evaluate()
print(eval_results)

print("Training complete!")