128 lines
4.1 KiB
Python
Executable File
128 lines
4.1 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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Train LoRA adapter on Cyron summary dataset.
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Uses Hugging Face TRL for SFT training with QLoRA.
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"""
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import argparse
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import yaml
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from pathlib import Path
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def train(config_path):
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"""Train LoRA adapter using TRL."""
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with open(config_path) as f:
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config = yaml.safe_load(f)
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from transformers import (
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AutoModelForCausalLM,
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AutoTokenizer,
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BitsAndBytesConfig,
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TrainingArguments,
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)
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from peft import (
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prepare_model_for_kbit_training,
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LoraConfig,
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get_peft_model,
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)
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from trl import SFTTrainer, SFTConfig
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print(f"Loading model: {config['base_model']}")
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# Load model with quantization
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=config.get("load_in_4bit", True),
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bnb_4bit_compute_dtype=config.get("bnb_4bit_compute_dtype", "bfloat16"),
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bnb_4bit_quant_type=config.get("bnb_4bit_quant_type", "nf4"),
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use_nested_quant=config.get("use_nested_quant", False),
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)
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model = AutoModelForCausalLM.from_pretrained(
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config["base_model"],
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quantization_config=bnb_config,
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device_map="auto",
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)
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model = prepare_model_for_kbit_training(model)
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# Add LoRA
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lora_config = LoraConfig(
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r=config["lora_r"],
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lora_alpha=config["lora_alpha"],
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lora_dropout=config["lora_dropout"],
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target_modules=config["target_modules"],
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task_type=config.get("lora_task_type", "CAUSAL_LM"),
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)
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model = get_peft_model(model, lora_config)
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model.print_trainable_parameters()
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(config["base_model"])
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tokenizer.pad_token = tokenizer.eos_token
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tokenizer.padding_side = "right"
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# Load dataset
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from datasets import load_dataset
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dataset = load_dataset(
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"json",
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data_files={
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"train": config["dataset"][0]["path"].replace("../", ""),
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"test": config["dataset"][0]["path"].replace("../", "").replace("combined_20k.jsonl", "test.jsonl"),
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},
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)
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# Training arguments
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training_args = TrainingArguments(
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output_dir=config["train_params"]["output_dir"],
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num_train_epochs=config["train_params"]["num_train_epochs"],
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per_device_train_batch_size=config["train_params"]["per_device_train_batch_size"],
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gradient_accumulation_steps=config["train_params"]["gradient_accumulation_steps"],
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learning_rate=config["train_params"]["learning_rate"],
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lr_scheduler_type=config["train_params"]["lr_scheduler_type"],
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weight_decay=config["train_params"]["weight_decay"],
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warmup_ratio=config["train_params"]["warmup_ratio"],
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max_seq_length=config["train_params"]["max_seq_length"],
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logging_steps=config["train_params"]["logging_steps"],
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save_steps=config["train_params"]["save_steps"],
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save_total_limit=config["train_params"]["save_total_limit"],
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evaluation_strategy=config.get("eval_strategy", "steps"),
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eval_steps=config.get("eval_steps", 100),
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mixed_precision=config.get("mixed_precision", "bf16"),
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gradient_checkpointing=config.get("gradient_checkpointing", True),
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)
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# SFT Trainer
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trainer = SFTTrainer(
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model=model,
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tokenizer=tokenizer,
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train_dataset=dataset["train"],
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eval_dataset=dataset["test"],
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args=training_args,
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max_seq_length=config["train_params"]["max_seq_length"],
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)
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# Train
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print("Starting training...")
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trainer.train()
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# Save
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trainer.save_model(config["train_params"]["output_dir"])
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tokenizer.save_pretrained(config["train_params"]["output_dir"])
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print(f"Training complete! Model saved to {config['train_params']['output_dir']}")
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def main():
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parser = argparse.ArgumentParser(description="Train LoRA adapter")
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parser.add_argument("--config", type=str, default="configs/llama2-7b-lora.yaml",
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help="Training configuration file")
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args = parser.parse_args()
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train(args.config)
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if __name__ == "__main__":
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main()
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