---
license: apache-2.0
language: en
base_model:
  - unsloth/Qwen3.5-0.8B
tags:
  - fetal-ultrasound
  - vision-language-model
  - medical-ai
  - gguf
  - llama.cpp
  - qwen3.5-vl
  - lora
  - knowledge-distillation
pipeline_tag: image-text-to-text
library_name: llama.cpp
---

# FADA-SKD 0.8B (GGUF)

**Fetal Anatomy Delineation and Analysis - Selective Knowledge Distillation**

On-device vision-language model for fetal ultrasound image analysis, optimized for mobile deployment via llama.cpp.

## Model Overview

| Property | Value |
|----------|-------|
| Base Model | Qwen3.5-VL 0.8B (via Unsloth) |
| Fine-tuning | LoRA (rank 16, alpha 32) via Unsloth SKD |
| Training Strategy | Offline Selective Knowledge Distillation   |
| Architecture | Qwen3.5 (text) + Qwen3-VL Merger (vision) |
| Context Length | 4096 tokens |
| Text Model | Q4_K_M quantized (~517 MB) |
| Vision Encoder | FP16 (~196 MB) |
| Format | GGUF v3 |
| Total Size | ~713 MB |

## Teacher Models (SKD)

| Teacher | Weight | Role |
|---------|--------|------|
| FetalCLIP | 0.40 | Fetal anatomy representation alignment |
| UltraSAM | 0.25 | Ultrasound segmentation knowledge |
| USF-MAE | 0.20 | Self-supervised ultrasound features |
| UltraFedFM | 0.15 | Federated foundation model knowledge |

**Training**: 21,144 steps, 3 epochs, fusion MSE loss, 19,000+ images across 32+ anatomical classes.

## Files

| File | Description | Size |
|------|-------------|------|
| `gguf/fada-skd-0.8b-Q4_K_M.gguf` | Text model (Q4_K_M quantized) | 517 MB |
| `gguf/fada-skd-0.8b-mmproj-f16.gguf` | Vision encoder (FP16 mmproj) | 196 MB |
| `tokenizer.json` | Tokenizer vocabulary | - |
| `tokenizer_config.json` | Tokenizer configuration | - |
| `chat_template.jinja` | Chat template for inference | - |

## Usage with llama.cpp

### Text + Vision Inference

```bash
# Using llama-mtmd-cli from llama.cpp
./llama-mtmd-cli \
  -m fada-skd-0.8b-Q4_K_M.gguf \
  --mmproj fada-skd-0.8b-mmproj-f16.gguf \
  -p "Analyze this fetal ultrasound image" \
  --image ultrasound.jpg
```

### Mobile Deployment

This model is designed for on-device mobile deployment using the FADA Android app with llama.cpp's multimodal (mtmd) library for native inference without cloud connectivity.

## Capabilities

FADA-SKD performs 5-phase fetal ultrasound analysis:

1. **Interpretation**: Structured clinical report (imaging plane, anatomical structures, gestational age, image quality, normality assessment)
2. **Classification**: Anatomical view identification (BPD plane, four-chamber, Doppler, etc.)
3. **Mapping**: Intelligent routing to relevant detection/segmentation classes
4. **Detection**: Bounding box localization of anatomical structures (normalized 0-1000 coordinates)
5. **Segmentation**: Polygon mask delineation of anatomical regions

### Supported Anatomy (32+ classes)

- **Brain**: BPD, CSP, lateral ventricle, brain parenchyma
- **Cardiac**: Heart chambers, thorax
- **First Trimester**: CRL, NT, nasal bone/skin/tip
- **Doppler**: Arteries, veins, liver, stomach
- **Pelvimetry**: Fetal head, pubic symphysis
- **Body/Pose**: Abdomen, limbs, head
- **Keypoints**: CRL endpoints, NT caliper points, scale bar

## Citation

If you use this model in your research, please cite:

```bibtex
@misc{alzubaidi2026fadaaccessiblefetalultrasound,
      title={FADA: Accessible fetal ultrasound interpretation and annotation with a selectively distilled unified vision-language model}, 
      author={ },
      year={2026},
      eprint={2606.11106},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2606.11106}, 
}
```

## License

Apache 2.0

## Disclaimer

This is a research prototype. Not for clinical diagnostic use. All outputs should be reviewed by qualified medical professionals.