Qwen3-VL-4B-Instruct-Flowers102-Open-QA – AI Model by oscarqjh | AlphaNeural AI

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

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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

python

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.

Citation

If you use this model, please cite the original base model and the training framework used.