Instructions to use ikellllllll/vqa-base-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ikellllllll/vqa-base-model with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct")
model = PeftModel.from_pretrained(base_model, "ikellllllll/vqa-base-model")

Transformers

How to use ikellllllll/vqa-base-model with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="ikellllllll/vqa-base-model")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ikellllllll/vqa-base-model", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ikellllllll/vqa-base-model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ikellllllll/vqa-base-model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ikellllllll/vqa-base-model",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/ikellllllll/vqa-base-model

SGLang

How to use ikellllllll/vqa-base-model with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ikellllllll/vqa-base-model" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ikellllllll/vqa-base-model",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ikellllllll/vqa-base-model" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ikellllllll/vqa-base-model",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use ikellllllll/vqa-base-model with Docker Model Runner:
```
docker model run hf.co/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

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

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