MedQA-Qwen-2B-LoRA16

Model Details

• Base Model: Qwen2-VL-2B
• Fine-tuned on: PMC-VQA (Compounded images)
• Fine-tuning Method: LoRA (Rank=16, Alpha=16, Dropout=0)
• Layers Updated: Vision, Attention, Language, MLP Modules
• Optimiser: AdamW (8-bit)
• Batch Size: 5 per device (Gradient Accumulation = 2)
• Learning Rate: 2e-4
• Training Time: 572.73 minutes (~9.5 hours)
• Peak GPU Usage: 8.0GB (RTX 4080 Super)

Dataset

PMC-VQA-1 # Replace with the actual dataset name

• 226,948 samples split into train, validation and test sets
• Data Fields include: | Feature | Description | |---------------------|--------------------------------------------------------------| | Figure_path: | The filename of the corresponding image (e.g., "PMC_1.jpg"). | | Question: | The medical question related to the image. | | Answer: | The correct answer to the question. | | Choice A: | Option A for the multiple-choice question. | | Choice B: | Option B for the multiple-choice question. | | Choice C: | Option C for the multiple-choice question. | | Choice D: | Option D for the multiple-choice question. | | Answer_label: | The index label of the correct answer choice (A, B, C, D). | | image: | The actual image data, stored as a PIL Image object. | # Provide a detailed description of your dataset.
• Preprocessing:
  • Images resized: max_pixels = 256x256, min_pixels = 224x224
  • No additional augmentation
• Dataset created from PMC-VQA dataset https://huggingface.co/datasets/xmcmic/PMC-VQA.

Training Performance

Uploaded model

• Developed by: hamzamooraj99
• License: apache-2.0
• Finetuned from model : unsloth/Qwen2-VL-2B-Instruct-unsloth-bnb-4bit