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import torch
from datasets import load_dataset
from peft import LoraConfig, get_peft_model
from transformers import AutoTokenizer, AutoModelForCausalLM
from trl import DPOTrainer
if __name__ == "__main__":
model_name = "..."
dataset = load_dataset(...)
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(
model_name,
low_cpu_mem_usage=True,
torch_dtype=torch.bfloat16,
load_in_4bit=True,
use_flash_attention_2=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_quant_type="nf4",
)
model.resize_token_embeddings(len(tokenizer))
model.config.pad_token_id = tokenizer.pad_token_id
model.config.use_cache = False
ref_model = AutoModelForCausalLM.from_pretrained(
model_name,
low_cpu_mem_usage=True,
torch_dtype=torch.bfloat16,
load_in_4bit=True,
use_flash_attention_2=True,
bnb_4bit_compute_dtype=torch.bfloat16,
).eval()
peft_config = LoraConfig(
lora_alpha=128,
lora_dropout=0.05,
r=64,
bias="none",
task_type="CAUSAL_LM",
target_modules=[
"q_proj",
"k_proj",
"v_proj",
],
)
model = get_peft_model(model, peft_config)
training_args = DPOConfig(
num_train_epochs=3,
learning_rate=5e-07,
per_device_train_batch_size=1,
do_eval=True,
per_device_eval_batch_size=1,
adam_epsilon=1e-08,
lr_scheduler_type="linear",
warmup_ratio=0.1,
seed=42,
logging_steps=100,
save_steps=500,
save_strategy="steps",
output_dir="./output-dir",
gradient_checkpointing=True,
bf16=True,
remove_unused_columns=False,
)
dpo_trainer = DPOTrainer(
model,
ref_model,
args=training_args,
beta=training_args.beta,
train_dataset=dataset["train"],
eval_dataset=dataset["test"],
tokenizer=tokenizer,
max_length=training_args.max_length,
max_prompt_length=training_args.max_prompt_length,
peft_config=peft_config,
)
dpo_trainer.train()
dpo_trainer.save_model() |