Instructions to use oscarqjh/Qwen3-VL-4B-Instruct-Flowers102-Open-QA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use oscarqjh/Qwen3-VL-4B-Instruct-Flowers102-Open-QA with Transformers:

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

pipe = pipeline("image-text-to-text", model="oscarqjh/Qwen3-VL-4B-Instruct-Flowers102-Open-QA")
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 AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("oscarqjh/Qwen3-VL-4B-Instruct-Flowers102-Open-QA")
model = AutoModelForMultimodalLM.from_pretrained("oscarqjh/Qwen3-VL-4B-Instruct-Flowers102-Open-QA")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use oscarqjh/Qwen3-VL-4B-Instruct-Flowers102-Open-QA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "oscarqjh/Qwen3-VL-4B-Instruct-Flowers102-Open-QA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "oscarqjh/Qwen3-VL-4B-Instruct-Flowers102-Open-QA",
		"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/oscarqjh/Qwen3-VL-4B-Instruct-Flowers102-Open-QA

SGLang

How to use oscarqjh/Qwen3-VL-4B-Instruct-Flowers102-Open-QA 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 "oscarqjh/Qwen3-VL-4B-Instruct-Flowers102-Open-QA" \
    --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": "oscarqjh/Qwen3-VL-4B-Instruct-Flowers102-Open-QA",
		"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 "oscarqjh/Qwen3-VL-4B-Instruct-Flowers102-Open-QA" \
        --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": "oscarqjh/Qwen3-VL-4B-Instruct-Flowers102-Open-QA",
		"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 oscarqjh/Qwen3-VL-4B-Instruct-Flowers102-Open-QA with Docker Model Runner:
```
docker model run hf.co/oscarqjh/Qwen3-VL-4B-Instruct-Flowers102-Open-QA
```

Qwen3-VL-4B-Instruct-Flowers102-Open-QA

This model is a fine-tuned version of Qwen/Qwen3-VL-4B-Instruct on a custom dataset.

Model Details

Base Model: Qwen/Qwen3-VL-4B-Instruct
Fine-tuned from checkpoint: 2500
Model Type: Vision-Language Model
Training Framework: LMMS-Engine with FSDP2

Training Details

Training Data

Custom dataset based on Oxford Flowers102 found here.

Training Hyperparameters

Per Device Train Batch Size: 1
Learning Rate: 2e-05
Weight Decay: 0.0
Gradient Accumulation Steps: 8
Gradient Checkpointing: True
Warmup Ratio: 0.1
Max Steps: 5000
Number of Epochs: 1
LR Scheduler: cosine
Optimizer: adamw_torch_fused
Mixed Precision (BF16): True
FSDP2: True
Save Steps: 500
Logging Steps: 1

Training Configuration

Checkpoint Step: 2500
Training Framework: LMMS-Engine
Distributed Training: FSDP2 (Fully Sharded Data Parallel v2)

Usage

from transformers import AutoProcessor, AutoModel
import torch

# Load model and processor
processor = AutoProcessor.from_pretrained("oscarqjh/Qwen3-VL-4B-Instruct-Flowers102-Open-QA")
model = AutoModel.from_pretrained("oscarqjh/Qwen3-VL-4B-Instruct-Flowers102-Open-QA", torch_dtype=torch.bfloat16)

# Example usage
# Your inference code here

Training Framework

This model was trained using LMMS-Engine, a framework for training large multimodal models.

Limitations and Bias

This model inherits the limitations and potential biases from its base model Qwen/Qwen3-VL-4B-Instruct. Please refer to the base model's documentation for detailed information about limitations and ethical considerations.