1 files changed, 122 insertions, 0 deletions
diff --git a/finetune.py b/finetune.py
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+++ b/finetune.py
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+import torch
+from datasets import Dataset, Image
+from transformers import (
+    AutoProcessor,
+    AutoModelForCausalLM,
+    TrainingArguments,
+    BitsAndBytesConfig,
+)
+from trl import SFTTrainer
+from peft import LoraConfig
+from glob import glob
+import json
+import os
+
+
+model_name = "google/gemma-3-4b-it"
+
+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(model_name, use_fast=True)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    quantization_config=bnb_config,
+    device_map="auto",
+)
+
+peft_config = LoraConfig(
+    lora_alpha=32,
+    lora_dropout=0.05,
+    r=16,
+    task_type="CAUSAL_LM",
+    bias="none",
+    target_modules="all-linear",
+    modules_to_save=["lm_head", "embed_tokens"],
+)
+
+def make_dataset(base="./baseline"):
+    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())
+            print(f"{data=}")
+        image_path = f"../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())
+
+dataset = make_dataset()
+
+def add_chat_text(example):
+    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
+
+dataset = dataset.map(add_chat_text)
+
+def collate(batch):
+    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",
+    )
+    out["labels"] = out["input_ids"].clone()
+    out["labels"][out["attention_mask"] == 0] = -100
+
+    return out
+
+ds_split = dataset.train_test_split(test_size=0.2, seed=42)
+
+args = TrainingArguments(
+    output_dir="gemma3-mm-sft-lora",
+    per_device_train_batch_size=1,
+    gradient_accumulation_steps=24,
+    num_train_epochs=1,
+    learning_rate=1e-5,
+    fp16=False,
+    bf16=True,
+    gradient_checkpointing=True,
+    gradient_checkpointing_kwargs={"use_reentrant": False},
+    optim="paged_adamw_8bit",
+    remove_unused_columns=False,
+    logging_steps=10,
+    save_steps=20,
+)
+
+trainer = SFTTrainer(
+    model=model,
+    train_dataset=ds_split["train"],
+    eval_dataset=ds_split["test"],
+    data_collator=collate,
+    args=args,
+	peft_config=peft_config,
+)
+
+trainer.train()
+trainer.save_model()