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# /// script
# dependencies = [
# "trl>=0.12.0",
# "peft>=0.7.0",
# "trackio",
# "transformers>=4.45.0",
# "torch",
# "torchvision",
# "datasets",
# "pillow",
# "qwen-vl-utils"
# ]
# ///
from datasets import load_dataset
from peft import LoraConfig
from trl import SFTTrainer, SFTConfig
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
import trackio
import torch
# Load 1% of the train split
print("Loading dataset...")
dataset = load_dataset("trl-lib/llava-instruct-mix", split="train[:1%]")
print(f"Dataset size: {len(dataset)} examples")
# Create a small eval split (10% of the 1%)
dataset_split = dataset.train_test_split(test_size=0.1, seed=42)
train_dataset = dataset_split["train"]
eval_dataset = dataset_split["test"]
print(f"Train: {len(train_dataset)}, Eval: {len(eval_dataset)}")
# Configure trainer with VL-specific settings
trainer = SFTTrainer(
model="Qwen/Qwen2.5-VL-3B-Instruct",
train_dataset=train_dataset,
eval_dataset=eval_dataset,
peft_config=LoraConfig(
r=16,
lora_alpha=32,
target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM"
),
args=SFTConfig(
output_dir="qwen3-vl-3b-llava-instruct",
push_to_hub=True,
hub_model_id="merve/qwen2.5-vl-3b-llava-instruct",
num_train_epochs=3,
per_device_train_batch_size=1,
gradient_accumulation_steps=8,
gradient_checkpointing=True,
learning_rate=2e-4,
warmup_steps=100,
logging_steps=10,
eval_strategy="steps",
eval_steps=50,
save_strategy="steps",
save_steps=100,
save_total_limit=2,
bf16=True,
report_to="trackio",
project="qwen3-vl-finetuning",
run_name="qwen3-vl-3b-llava-1pct",
max_length=None, # Important for VL models - don't truncate image tokens
hub_strategy="every_save",
remove_unused_columns=False, # Keep all columns for VL processing
)
)
print("Starting training...")
trainer.train()
print("Pushing final model to Hub...")
trainer.push_to_hub()
print("Training complete!")
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