ikellllllll/vqa-base-model

VQA Base Model

Fine-tuned VQA model using Qwen2.5-VL-3B-Instruct with LoRA.

Performance:

• Validation Accuracy: 88.69% (345/389)
• High-res (512px) Accuracy: 89.72% (349/389)
• Baseline model for the project

Part of 3-Model Ensemble:

• Combined with Improved Epoch 1 and Improved Epoch 2
• Ensemble Validation: 90.75%
• Ensemble Test (Kaggle): 91.82%

Model Details

• Base Model: Qwen/Qwen2.5-VL-3B-Instruct
• Fine-tuning Method: LoRA (Low-Rank Adaptation)
• Quantization: 4-bit (NF4)
• Hardware: NVIDIA A100 40GB
• Training: Fine-tuned on VQA dataset (604 samples)

LoRA Configuration

{
    "r": 16,
    "lora_alpha": 32,
    "lora_dropout": 0.05,
    "target_modules": [
        "q_proj", "k_proj", "v_proj", "o_proj",
        "gate_proj", "up_proj", "down_proj"
    ]
}

Usage

from transformers import AutoModelForVision2Seq, AutoProcessor, BitsAndBytesConfig
from peft import PeftModel
import torch

# Load model with 4-bit quantization
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

base_model = AutoModelForVision2Seq.from_pretrained(
    "Qwen/Qwen2.5-VL-3B-Instruct",
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True
)

model = PeftModel.from_pretrained(base_model, "ikellllllll/vqa-base-model")
processor = AutoProcessor.from_pretrained(
    "Qwen/Qwen2.5-VL-3B-Instruct",
    min_pixels=512*512,
    max_pixels=512*512,
    trust_remote_code=True
)

# IMPORTANT: Set left-padding for decoder-only models
processor.tokenizer.padding_side = 'left'

Inference Settings

• Image Resolution: 512×512px (higher resolution recommended)
• Batch Size: 32 (for A100 40GB)
• Padding: Left-padding (critical for decoder-only models!)

Dataset

• Training: 604 VQA samples
• Validation: 389 VQA samples
• Test: 3,887 VQA samples

Performance Notes

• 384px resolution: 88.69% validation accuracy
• 512px resolution: 89.72% validation accuracy (+1.03%)
• Higher resolution significantly improves performance

Links

• GitHub Repository: SSAFY_AI_competition
• Related Models:
  • vqa-improved-epoch1 (90.49%)
  • vqa-improved-epoch2 (90.23%)

Citation

@misc{vqa-base-model,
  author = {Team 203},
  title = {VQA Base Model},
  year = {2025},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/ikellllllll/vqa-base-model}}
}

License

Apache 2.0