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base_model: Qwen/Qwen2.5-VL-3B-Instruct
library_name: peft
pipeline_tag: image-text-to-text
tags:
- vision
- vqa
- qwen2.5-vl
- lora
- transformers
license: apache-2.0
---
# 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
```python
{
"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
```python
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](https://github.com/ikellllllll/SSAFY_AI_competition)
- **Related Models:**
- [vqa-improved-epoch1](https://huggingface.co/ikellllllll/vqa-improved-epoch1) (90.49%)
- [vqa-improved-epoch2](https://huggingface.co/ikellllllll/vqa-improved-epoch2) (90.23%)
## Citation
```bibtex
@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
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