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# Copyright 2025-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import Optional
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
from peft.optimizers import create_lorafa_optimizer
def train_model(
base_model_name_or_path: str,
dataset_name_or_path: str,
output_dir: str,
batch_size: int,
num_epochs: int,
lr: float,
cutoff_len: int,
quantize: bool,
eval_step: int,
save_step: int,
lora_rank: int,
lora_alpha: int,
lora_dropout: float,
lora_target_modules: Optional[str],
lorafa: bool,
):
os.environ["TOKENIZERS_PARALLELISM"] = "false"
is_bf16_supported = False
device_map = "cpu"
if torch.cuda.is_available():
is_bf16_supported = torch.cuda.is_bf16_supported()
device_map = "cuda"
elif torch.xpu.is_available():
is_bf16_supported = torch.xpu.is_bf16_supported()
device_map = "xpu"
compute_dtype = torch.bfloat16 if is_bf16_supported else torch.float16
# load tokenizer
tokenizer = AutoTokenizer.from_pretrained(base_model_name_or_path)
# load model
if quantize:
model = AutoModelForCausalLM.from_pretrained(
base_model_name_or_path,
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_use_double_quant=False,
bnb_4bit_quant_type="nf4",
),
torch_dtype=compute_dtype,
device_map=device_map,
)
# setup for quantized training
model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=True)
else:
model = AutoModelForCausalLM.from_pretrained(
base_model_name_or_path, torch_dtype=compute_dtype, device_map=device_map
)
# LoRA config for the PEFT model
if lora_target_modules is not None:
if lora_target_modules == "all-linear":
target_modules = "all-linear"
else:
target_modules = lora_target_modules.split(",")
else:
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
lora_config = LoraConfig(
r=lora_rank,
lora_alpha=lora_alpha,
target_modules=target_modules,
lora_dropout=lora_dropout,
bias="none",
)
# get the peft model with LoRA config
model = get_peft_model(model, lora_config)
tokenizer.pad_token = tokenizer.eos_token
# Load the dataset
dataset = load_dataset(dataset_name_or_path)
def tokenize_function(examples):
inputs = tokenizer(examples["query"], padding="max_length", truncation=True, max_length=cutoff_len)
outputs = tokenizer(examples["response"], padding="max_length", truncation=True, max_length=cutoff_len)
inputs["labels"] = outputs["input_ids"].copy()
return inputs
# Tokenize the dataset and prepare for training
tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=dataset["train"].column_names)
dataset = tokenized_datasets["train"].train_test_split(test_size=0.1, shuffle=True, seed=42)
train_dataset = dataset["train"]
eval_dataset = dataset["test"]
# 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,
gradient_accumulation_steps=1,
bf16=True if compute_dtype == torch.bfloat16 else False,
fp16=True if compute_dtype == torch.float16 else False,
learning_rate=lr,
)
# Here we initialize the LoRA-FA Optimizer
# After this, all adapter A will be fixed, only adapter B will be trainable
if lorafa:
optimizer = create_lorafa_optimizer(
model=model, r=lora_rank, lora_alpha=lora_alpha, lr=lr, weight_decay=training_args.weight_decay
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
data_collator=data_collator,
optimizers=(optimizer, None),
)
else:
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
data_collator=data_collator,
)
# Start model training
trainer.train()
# 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 Meta-Llama-3-8B-Instruct with LoRA-FA and PEFT")
parser.add_argument(
"--base_model_name_or_path",
type=str,
default="meta-llama/Meta-Llama-3-8B-Instruct",
help="Base model name or path",
)
parser.add_argument(
"--dataset_name_or_path", type=str, default="meta-math/MetaMathQA-40K", help="Dataset name or path"
)
parser.add_argument("--output_dir", type=str, 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=3, help="Number of training epochs")
parser.add_argument("--lr", type=float, default=7e-5, help="Learning rate")
parser.add_argument("--cutoff_len", type=int, default=1024, help="Cutoff length for tokenization")
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("--lora_rank", type=int, default=16, help="LoRA rank")
parser.add_argument("--lora_alpha", type=int, default=32, 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("--lorafa", action="store_true", help="Use LoRA-FA Optimizer")
args = parser.parse_args()
train_model(
base_model_name_or_path=args.base_model_name_or_path,
dataset_name_or_path=args.dataset_name_or_path,
output_dir=args.output_dir,
batch_size=args.batch_size,
num_epochs=args.num_epochs,
lr=args.lr,
cutoff_len=args.cutoff_len,
quantize=args.quantize,
eval_step=args.eval_step,
save_step=args.save_step,
lora_rank=args.lora_rank,
lora_alpha=args.lora_alpha,
lora_dropout=args.lora_dropout,
lora_target_modules=args.lora_target_modules,
lorafa=args.lorafa,
)
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