train.py

"""
Train a US Architectural Floor Plan LLM using SFT with LoRA.

Base model: Qwen/Qwen2.5-3B-Instruct
Dataset: Nithins03/us-architectural-floorplan-sft
Method: SFT with LoRA (rank=128, all-linear) following "LoRA Without Regret" recipe
Output: Nithins03/us-architectural-floorplan-llm

Reference implementations:
- TRL SFT docs: https://huggingface.co/docs/trl/sft_trainer
- LoRA Without Regret: https://huggingface.co/docs/trl/lora_without_regret
- OptiScene (arxiv:2506.07570): LoRA r=16, alpha=32, lr=5e-6, 10 epochs
- DStruct2Design (arxiv:2407.15723): LLaMA3-8B + 8-bit + LoRA
"""

import os
import torch
from datasets import load_dataset
from peft import LoraConfig
from trl import SFTTrainer, SFTConfig
import trackio

# ============================================================================
# Configuration
# ============================================================================

MODEL_NAME = "Qwen/Qwen2.5-3B-Instruct"
DATASET_NAME = "Nithins03/us-architectural-floorplan-sft"
OUTPUT_DIR = "./floorplan-llm-output"
HUB_MODEL_ID = "Nithins03/us-architectural-floorplan-llm"

peft_config = LoraConfig(
    r=128,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
    target_modules="all-linear",
)

training_args = SFTConfig(
    output_dir=OUTPUT_DIR,
    num_train_epochs=5,
    learning_rate=1e-4,
    lr_scheduler_type="cosine",
    warmup_ratio=0.05,
    weight_decay=0.01,
    max_grad_norm=1.0,
    per_device_train_batch_size=2,
    gradient_accumulation_steps=4,
    max_length=4096,
    gradient_checkpointing=True,
    bf16=True,
    eval_strategy="steps",
    eval_steps=500,
    per_device_eval_batch_size=2,
    logging_strategy="steps",
    logging_steps=25,
    logging_first_step=True,
    disable_tqdm=True,
    report_to=["trackio"],
    save_strategy="steps",
    save_steps=500,
    save_total_limit=3,
    load_best_model_at_end=True,
    metric_for_best_model="eval_loss",
    push_to_hub=True,
    hub_model_id=HUB_MODEL_ID,
    hub_strategy="every_save",
    packing=False,
    assistant_only_loss=True,
    seed=42,
)

def main():
    print("=" * 60)
    print("US Architectural Floor Plan LLM Training")
    print("=" * 60)
    
    trackio.init(project="us-floorplan-llm", name="qwen2.5-3b-lora-sft")
    
    dataset = load_dataset(DATASET_NAME)
    print(f"Train: {len(dataset['train'])} | Test: {len(dataset['test'])}")
    
    trainer = SFTTrainer(
        model=MODEL_NAME,
        args=training_args,
        train_dataset=dataset["train"],
        eval_dataset=dataset["test"],
        peft_config=peft_config,
    )
    
    model = trainer.model
    trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)
    total = sum(p.numel() for p in model.parameters())
    print(f"Trainable: {trainable:,} / {total:,} ({100*trainable/total:.2f}%)")
    
    train_result = trainer.train()
    
    metrics = train_result.metrics
    print(f"Train loss: {metrics.get('train_loss', 'N/A')}")
    
    eval_metrics = trainer.evaluate()
    print(f"Eval loss: {eval_metrics.get('eval_loss', 'N/A')}")
    
    trainer.save_model()
    trainer.push_to_hub(commit_message="Final model after SFT training on US floor plans")
    print(f"Model pushed to: https://huggingface.co/{HUB_MODEL_ID}")

if __name__ == "__main__":
    main()