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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()
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