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import os
import torch
from datasets import load_dataset
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
DataCollatorForLanguageModeling,
Trainer,
TrainingArguments,
)
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
def train_model(
base_model: str,
data_path: str,
output_dir: str,
batch_size: int,
num_epochs: int,
learning_rate: float,
cutoff_len: int,
val_set_size: int,
use_dora: bool,
quantize: bool,
eval_step: int,
save_step: int,
device: str,
lora_r: int,
lora_alpha: int,
lora_dropout: float,
lora_target_modules: str,
hub_model_id: str,
push_to_hub: bool,
):
os.environ["TOKENIZERS_PARALLELISM"] = "false"
hf_token = os.getenv("HF_TOKEN")
# Setup device
if device == "auto":
device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
else:
device = torch.device(device)
print(f"Using device: {device}")
# load tokenizer
tokenizer = AutoTokenizer.from_pretrained(base_model, token=hf_token)
# QDoRA (quantized dora): IF YOU WANNA QUANTIZE THE MODEL
if quantize:
if (torch.cuda.is_available() and torch.cuda.is_bf16_supported()) or torch.xpu.is_available():
bnb_4bit_compute_dtype = torch.bfloat16
else:
bnb_4bit_compute_dtype = torch.float16
model = AutoModelForCausalLM.from_pretrained(
base_model,
token=hf_token,
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=bnb_4bit_compute_dtype,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
),
)
# setup for quantized training
model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=True)
else:
model = AutoModelForCausalLM.from_pretrained(base_model, token=hf_token)
# LoRa config for the PEFT model
lora_config = LoraConfig(
use_dora=use_dora, # to use Dora OR compare to Lora just set the --use_dora
r=lora_r, # Rank of matrix
lora_alpha=lora_alpha,
target_modules=(
lora_target_modules.split(",")
if lora_target_modules
else ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
),
lora_dropout=lora_dropout,
bias="none",
)
# get the peft model with LoRa config
model = get_peft_model(model, lora_config)
model.to(device) # MODEL TO GPU/CUDA
tokenizer.pad_token = tokenizer.eos_token
# Load the dataset
dataset = load_dataset(data_path)
def tokenize_function(examples):
inputs = tokenizer(examples["text"], padding="max_length", truncation=True, max_length=cutoff_len)
inputs["labels"] = inputs["input_ids"].copy() # setting labels for a language modeling task
return inputs
# Tokenize the dataset and prepare for training
tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=dataset["train"].column_names)
# Data collator to dynamically pad the batched examples
data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)
# Define training arguments
training_args = TrainingArguments(
output_dir=output_dir,
num_train_epochs=num_epochs,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
warmup_steps=100,
weight_decay=0.01,
logging_dir="./logs",
logging_steps=eval_step,
save_steps=save_step,
save_total_limit=2,
push_to_hub=push_to_hub,
hub_model_id=hub_model_id,
gradient_accumulation_steps=16,
fp16=True,
learning_rate=learning_rate,
hub_token=hf_token,
)
# Clear device cache to free memory
if torch.cuda.is_available():
torch.cuda.empty_cache()
elif torch.xpu.is_available():
torch.xpu.empty_cache()
# Initialize the Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_datasets["train"],
eval_dataset=tokenized_datasets["test"],
data_collator=data_collator,
)
# Start model training
trainer.train()
# Save and push the trained model and tokenizer
if push_to_hub:
# Push the main model to the hub
trainer.push_to_hub(commit_message="Fine-tuned model")
# Save the model and tokenizer locally
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Fine-tune LLaMA with DoRA and PEFT")
parser.add_argument("--base_model", type=str, default="huggyllama/llama-7b", help="Base model path or name")
parser.add_argument(
"--data_path", type=str, default="timdettmers/openassistant-guanaco", help="Dataset path or name"
)
parser.add_argument(
"--output_dir", type=str, default="path/to/output", help="Output directory for the fine-tuned model"
)
parser.add_argument("--batch_size", type=int, default=1, help="Batch size")
parser.add_argument("--num_epochs", type=int, default=1, help="Number of training epochs")
parser.add_argument("--learning_rate", type=float, default=3e-4, help="Learning rate")
parser.add_argument("--cutoff_len", type=int, default=512, help="Cutoff length for tokenization")
parser.add_argument("--val_set_size", type=int, default=500, help="Validation set size")
parser.add_argument("--use_dora", action="store_true", help="Apply Dora")
parser.add_argument("--quantize", action="store_true", help="Use quantization")
parser.add_argument("--eval_step", type=int, default=10, help="Evaluation step interval")
parser.add_argument("--save_step", type=int, default=100, help="Save step interval")
parser.add_argument("--device", type=str, default="auto", help="Device to use for training")
parser.add_argument("--lora_r", type=int, default=8, help="LoRA rank")
parser.add_argument("--lora_alpha", type=int, default=16, help="LoRA alpha")
parser.add_argument("--lora_dropout", type=float, default=0.05, help="LoRA dropout rate")
parser.add_argument(
"--lora_target_modules", type=str, default=None, help="Comma-separated list of target modules for LoRA"
)
parser.add_argument(
"--hub_model_id",
type=str,
default="path/to/repo",
help="Repository name to push the model on the Hugging Face Hub",
)
parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to Hugging Face Hub")
args = parser.parse_args()
train_model(
base_model=args.base_model,
data_path=args.data_path,
output_dir=args.output_dir,
batch_size=args.batch_size,
num_epochs=args.num_epochs,
learning_rate=args.learning_rate,
cutoff_len=args.cutoff_len,
val_set_size=args.val_set_size,
use_dora=args.use_dora,
quantize=args.quantize,
eval_step=args.eval_step,
save_step=args.save_step,
device=args.device,
lora_r=args.lora_r,
lora_alpha=args.lora_alpha,
lora_dropout=args.lora_dropout,
lora_target_modules=args.lora_target_modules,
hub_model_id=args.hub_model_id,
push_to_hub=args.push_to_hub,
)
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