---
license: apache-2.0
base_model: google/medgemma-1.5-4b-it
tags:
- medical
- ophthalmology
- vision-language-model
- fundus
- retinal-imaging
- unsloth
datasets:
- smrndmdude/eye-diseases-dataset
language:
- en
metrics:
- loss
pipeline_tag: image-text-to-text
library_name: peft
---

# Anidane (MedGemma 1.5 4B Fine-Tuned for Ophthalmic Analysis)

**Anidane** is a fine-tuned, multimodal vision-language model optimized for the analysis and diagnostic assessment of ophthalmic imagery, specifically fundus and retinal photography. Built on top of the `google/medgemma-1.5-4b-it` architecture and accelerated using **Unsloth**, Anidane bridges the gap in clinical accessibility by enabling parameter-efficient interpretation of localized eye pathologies and structural abnormalities.

## Model Details

- **Developed by:** WILLIAMS OPOKU & SAMUEL AGYEMANG
- **Model Type:** Multimodal Vision-Language Model (Image-Text to Text)
- **Base Model:** `google/medgemma-1.5-4b-it` (Gemma 3 / MedSigLIP Core)
- **Fine-Tuning Method:** QLoRA (4-bit quantized) via Unsloth
- **Primary Domain:** Ophthalmology, Retinal Image Diagnostic Reporting

## Intended Uses & Limitations

### Intended Use
- **Clinical Assistant Tool:** Providing detailed descriptive impressions of fundus abnormalities (e.g., microaneurysms, vascular alterations, optic disc changes).
- **Research & Education:** Analyzing pathological trends in public ophthalmic imagery datasets, with an emphasis on mitigating geographic screening biases in underrepresented cohorts.

### Limitations & Out of Scope
- **Not a Standalone Diagnostic Tool:** Anidane is designed strictly as an assistive reporting tool and must not be used for definitive medical diagnoses without certified practitioner oversight.
- **Data Demographics:** While optimized to interpret global clinical profiles, the model's accuracy is closely tied to the imaging quality and distribution found within the underlying train sets.

## Training Details

### Training Dataset
- **Dataset Source:** `smrndmdude/eye-diseases-dataset`
- **Modality:** High-resolution eye/fundus photography paired with localized clinical diagnostic captions.

### Hyperparameters & Optimization
The model was fine-tuned using the following configurations:
- **Quantization:** 4-bit NormalFloat4 (NF4)
- **LoRA Rank ($r$):** 16
- **LoRA Alpha ($\alpha$):** 16
- **Optimizer:** `adamw_8bit`
- **Learning Rate:** $2 \times 10^{-4}$ (Linear decay)
- **Gradient Accumulation Steps:** 4
- **Batch Size:** 2 per device
- **Total Steps:** 59

### Training Loss Curve Performance
During fine-tuning, the model displayed highly stable convergence characteristics, with training loss scaling down seamlessly as follows:
- **Initial Phase (Step 1):** 6.061622
- **Mid Phase (Step 30):** 0.171660
- **Final Convergence Phase (Step 59):** 0.080335

## How to Use the Model

You can load and query **Anidane** using the Hugging Face `transformers` library or via `unsloth` for accelerated inference.

```python
import torch

from PIL import Image

from transformers import AutoProcessor

from unsloth import FastVisionModel # Import FastVisionModel



model_id = "Willie999/Anidane"



processor = AutoProcessor.from_pretrained(model_id)

# Use FastVisionModel for loading the model, consistent with training

# FastVisionModel.from_pretrained returns a tuple (model, processor), so we unpack it.

model, _ = FastVisionModel.from_pretrained(

    model_name = model_id, # Use model_name for FastVisionModel

    torch_dtype=torch.bfloat16,

    device_map="auto"

)



# Load a target retinal image

image = Image.open("/content/red-eye-disease.jpg").convert("RGB")



messages = [

    {

        "role": "user",

        "content": [

            {"type": "image"},

            {"type": "text", "text": "Analyze this fundus/eye photograph. Provide a clear diagnostic assessment focusing on localized anomalies."}

        ]

    }

]



prompt = processor.apply_chat_template(messages, add_generation_prompt=True)

inputs = processor(text=prompt, images=image, return_tensors="pt").to(model.device)



with torch.no_grad():

    generated_ids = model.generate(

        input_ids=inputs["input_ids"],

        pixel_values=inputs["pixel_values"], # Explicitly pass pixel_values

        max_new_tokens=256,

        do_sample=False

    )



response = processor.batch_decode(

    generated_ids[:, inputs["input_ids"].shape[1]:],

    skip_special_tokens=True

)[0]



print("Anidane Analysis:", response)