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#!/usr/bin/env python3
import argparse
import json
import os
import torch
from datasets import Dataset, Image # Use PIL?
from functools import partial
from glob import glob
from peft import LoraConfig
from transformers import (
AutoProcessor,
AutoModelForCausalLM,
AutoModelForImageTextToText,
TrainingArguments,
BitsAndBytesConfig,
)
from trl import SFTTrainer
def make_dataset(base="./baseline"): # TODO: Make actual hf dataset
prompt = "You are a professional English-German translator and also a renowned photography critic.\n\nWrite a detailed caption for this image in a single sentence. Translate the caption into German. The output needs to be JSON, the keys being 'English' and 'German' for the respective captions. Only output the JSON, nothing else." + "<start_of_image>"
user_prompts = []
images = []
assistant_replies = []
for filename in glob(f"{base}/*.jsonl"):
with open(filename, "r") as f:
data = json.loads(f.read())
image_path = f"../d/Images/{os.path.basename(filename).removesuffix(".jsonl")}.jpg"
user_prompts.append(prompt)
assistant_replies.append(json.dumps({
"English": data["English"],
"German": data["Translation"],
}, ensure_ascii=False, indent=0))
images.append(image_path)
return Dataset.from_dict({"image": images, "user": user_prompts, "assistant": assistant_replies}).cast_column("image", Image())
def add_chat_text(example, processor):
messages = [
{"role": "user", "content": example["user"]},
{"role": "assistant", "content": example["assistant"]},
]
example["text"] = processor.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=False,
)
return example
def collate(batch, processor): # FIXME: Support batch_size > 1
images = [i["image"] for i in batch]
texts = [i["text"] for i in batch]
out = processor(
text=texts,
images=images,
padding=True,
truncation=True,
max_length=512,
return_tensors="pt",
)
image_token_id = [
processor.tokenizer.convert_tokens_to_ids(
processor.tokenizer.special_tokens_map["boi_token"]
)
]
out["labels"] = out["input_ids"].clone()
out["labels"][labels == processor.tokenizer.pad_token_id] = -100
out["labels"][labels == image_token_id] = -100
out["labels"][labels == 262144] = -100
return out
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model", default="google/gemma-3-4b-it")
parser.add_argument("--batch-size", default=1)
parser.add_argument("--gradient-accumulation", default=4)
parser.add_argument("--learning-rate", default=1e-4)
parser.add_argument("--epochs", default=1)
parser.add_argument("--warmup-ratio", default=0.03)
parser.add_argument("--scheduler-type", default="constant")
parser.add_argument("--logging-steps", default=10)
parser.add_argument("--lora-alpha", default=32)
parser.add_argument("--lora-dropout", default=0.05)
parser.add_argument("--lora-r", default=16)
args = parser.parse_args()
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
)
processor = AutoProcessor.from_pretrained(args.model, use_fast=True)
#model = AutoModelForCausalLM.from_pretrained(
model = AutoModelForImageTextToText.from_pretrained(
args.model,
quantization_config=bnb_config,
device_map="auto",
low_cpu_mem_usage=True,
)
peft_config = LoraConfig(
lora_alpha=args.lora_alpha,
lora_dropout=args.lora_dropout,
r=args.lora_r,
task_type="CAUSAL_LM",
bias="none",
target_modules="all-linear",
modules_to_save=["lm_head", "embed_tokens"],
)
dev_ds = make_dataset("./baseline/files_dev").map(partial(add_chat_text, processor=processor))
train_ds = make_dataset("./baseline/files_train").map(partial(add_chat_text, processor=processor))
args = TrainingArguments(
output_dir="gemma3-mm-sft-lora",
per_device_train_batch_size=args.batch_size,
gradient_accumulation_steps=args.gradient_accumulation,
num_train_epochs=args.epochs,
learning_rate=args.learning_rate,
warmup_ratio=args.warmup_ratio,
lr_scheduler_type=args.scheduler_type,
fp16=False,
bf16=True,
gradient_checkpointing=True,
gradient_checkpointing_kwargs={"use_reentrant": False},
optim="adamw_torch_8bit", # Alternative from BnB: paged_adamw_8bit
remove_unused_columns=False,
logging_steps=args.logging_steps,
save_strategy="epoch",
)
trainer = SFTTrainer(
model=model,
train_dataset=train_ds,
eval_dataset=dev_ds,
data_collator=partial(collate, processor=processor),
args=args,
peft_config=peft_config,
)
trainer.train()
trainer.save_model()
if __name__ == "__main__":
main()
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