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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, PeftModel, 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,
invocation_string: str,
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")
device = torch.device(device)
print(f"Using device: {device}")
tokenizer = AutoTokenizer.from_pretrained(base_model, token=hf_token)
tokenizer.pad_token = tokenizer.unk_token
invocation_tokens = tokenizer.encode(invocation_string, add_special_tokens=False)
if quantize:
model = AutoModelForCausalLM.from_pretrained(
base_model,
token=hf_token,
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=(
torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float16
),
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
),
)
model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=True)
else:
model = AutoModelForCausalLM.from_pretrained(base_model, token=hf_token)
lora_config = LoraConfig(
task_type="CAUSAL_LM",
alora_invocation_tokens=invocation_tokens,
r=lora_r,
lora_alpha=lora_alpha,
target_modules=(lora_target_modules.split(",") if lora_target_modules else ["q_proj", "k_proj", "v_proj"]),
lora_dropout=lora_dropout,
bias="none",
)
model = get_peft_model(model, lora_config)
model.to(device)
tokenizer.pad_token = tokenizer.eos_token
dataset = load_dataset(data_path)
def tokenize_function(examples):
formatted_texts = [
tokenizer.apply_chat_template(
[
{"role": "user", "content": user_msg},
{"role": "assistant", "content": assistant_msg},
],
tokenize=False, # get plain text first
add_generation_prompt=False,
)
for user_msg, assistant_msg in zip(examples["input"], examples["output"])
]
# 2) Tokenize those texts
model_inputs = tokenizer(
formatted_texts,
padding="max_length",
truncation=True,
max_length=cutoff_len,
)
labels = []
for ids in model_inputs["input_ids"]:
labels.append([(token_id if token_id != tokenizer.pad_token_id else -100) for token_id in ids])
model_inputs["labels"] = labels
return model_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)
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,
)
torch.cuda.empty_cache()
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_datasets["train"],
eval_dataset=tokenized_datasets["test"],
data_collator=data_collator,
)
trainer.train()
if push_to_hub:
trainer.push_to_hub(commit_message="Fine-tuned model")
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
def model_inference(model_path: str, adapter_path: str, prompt: str = None, data_path: str = None):
"""
Simple inference with the tuned aLoRA adapter. Optionally (reuse_cache = True) demonstrates
that the aLoRA adapter can (but does not need to) use KV cache created by the base model,
perhaps during a prior generation turn.
Purely for demonstration purposes. See the [paper](https://huggingface.co/papers/2504.12397)
for realistic multiturn cache reuse examples.
"""
if prompt is None:
# Use first row of test data
dataset = load_dataset(data_path)
prompt = dataset["test"][0]["input"]
tokenizer = AutoTokenizer.from_pretrained(model_path)
base_model = AutoModelForCausalLM.from_pretrained(model_path)
alora_model = PeftModel.from_pretrained(base_model, adapter_path)
chat = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(base_model.device)
# Generate answer with adapter
output_dict = alora_model.generate(**inputs, return_dict_in_generate=True, max_new_tokens=20)
alora_outputs = output_dict.sequences
# Print results
print(f"Prompt: {text}")
response = tokenizer.decode(alora_outputs[0][inputs["input_ids"].shape[1] :], skip_special_tokens=True)
print(f"Trained adapter response: {response}")
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Fine-tune Mistral with Activated LoRA")
parser.add_argument(
"--base_model", type=str, default="mistralai/Mistral-7B-Instruct-v0.3", help="Base model path or name"
)
parser.add_argument(
"--data_path",
type=str,
default="Lots-of-LoRAs/task1660_super_glue_question_generation",
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=2, 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=1e-4, help="Learning rate")
parser.add_argument("--cutoff_len", type=int, default=2048, help="Cutoff length for tokenization")
parser.add_argument("--val_set_size", type=int, default=500, help="Validation set size")
parser.add_argument(
"--invocation_string",
type=str,
default="[/INST]",
help="String that activates the aLoRA adapter. Model dependent.",
)
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="cuda:0", help="Device to use for training")
parser.add_argument("--lora_r", type=int, default=32, 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(
"--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,
invocation_string=args.invocation_string,
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,
)
print("Model trained. Running test inference.")
model_inference(model_path=args.base_model, adapter_path=args.output_dir, data_path=args.data_path)
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