README.md

---
license: apache-2.0
language:
- en
base_model:
- MBZUAI/MedMO-8B
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- text-generation-inference
- medical
---

![1](https://huggingface.co/proxy/cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/CXtMrz4ZwjD2vMX4wv9MH.png)

# **MedMO-8B-FP8**

> **MedMO-8B-FP8** is an FP8-compressed variant built on top of **MBZUAI/MedMO-8B**. This edition applies **BF16 · FP8 (F8_E4M3)** precision formats to significantly reduce memory footprint and improve inference throughput while preserving the advanced medical multimodal reasoning and grounding capabilities of the original 8B architecture. The base **MedMO-8B** model from MBZUAI is an 8B-parameter open-source multimodal large language model built on the Qwen3-VL architecture. It is specialized for comprehensive medical image understanding and grounding across radiology including X-ray, CT, MRI, and ultrasound, as well as pathology, ophthalmology, dermatology, and nuclear medicine.

Trained on 26M+ diverse samples from 45 datasets through a three-stage pipeline, general medical supervised fine-tuning with 18.5M image-text pairs, high-resolution grounding with 3M samples at 1280×1280, and instruction tuning with 4.3M pairs, the model demonstrates strong performance in VQA, medical QA, report generation, disease localization, clinical reasoning, and multi-task medical understanding. This FP8 edition maintains high diagnostic reasoning fidelity while enabling more efficient deployment on compatible GPU hardware.

> [!important]
> FP8 8-bit floating point weight and activation quantization using hardware acceleration on GPUs – [FP8 W8A8](https://docs.vllm.ai/en/stable/features/quantization/fp8/). Quantization W8A8 FP8-dynamic recipe – [examples](https://github.com/vllm-project/llm-compressor/tree/main/examples/quantization_w8a8_fp8).

## Key Highlights

* **BF16 · FP8 (F8_E4M3) Compression**: Transformer Engine based FP8 quantization reduces VRAM usage and improves inference efficiency while maintaining medical reasoning accuracy.
* **Medical Multimodal Specialization**: Optimized for radiology, pathology, ophthalmology, dermatology, and nuclear medicine tasks.
* **High-Resolution Grounding**: Supports precise spatial localization at up to 1280×1280 resolution.
* **Disease & Anatomical Localization**: Bounding box grounding with strong IoU improvements over baseline and prior SOTA systems.
* **Clinical Report Generation**: Achieves strong CIDEr scores including 270.4 on Med-Trinity, significantly outperforming prior systems.
* **Robust Cross-Modality Generalization**: Strong performance across diverse imaging modalities and medical benchmarks.
* **Transformers & vLLM Compatible**: Deployable via standard Hugging Face Transformers and vLLM inference stacks.

## Performance Highlights

* **VQA**: +13.7% over baseline
* **Text-based Medical QA**: +6.9% improvement
* **Report Generation**: CIDEr 270.4 on Med-Trinity, 4× prior SOTA
* **Localization**: IoU +40.4% over baseline, +37% over Fleming-VL
* **Bacteria Segmentation**: IoU 54.6%
* **MedSG Multi-task Benchmark**: 75.8% multi-view accuracy
* **Grounding Benchmarks**: SOTA on NIH Chest X-ray and DeepLesion datasets

## Quick Start with Transformers

```python
from transformers import Qwen3VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch

# Load the FP8 MedMO model
model = Qwen3VLForConditionalGeneration.from_pretrained(
    "MBZUAI/MedMO-8B-FP8",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "MBZUAI/MedMO-8B-FP8"
)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "medical_scan.png",
            },
            {
                "type": "text",
                "text": "Identify abnormalities and provide a diagnostic summary."
            },
        ],
    }
]

text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)

image_inputs, video_inputs = process_vision_info(messages)

inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
).to("cuda")

generated_ids = model.generate(**inputs, max_new_tokens=512)

generated_ids_trimmed = [
    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]

output_text = processor.batch_decode(
    generated_ids_trimmed,
    skip_special_tokens=True,
    clean_up_tokenization_spaces=False
)

print(output_text)
```

## Intended Use

* **Clinical Decision Support Research**
* **Medical Image Grounding & Localization**
* **Radiology & Pathology Report Generation**
* **Medical VQA Systems**
* **Healthcare AI Research & Benchmarking**

## Limitations & Risks

> **Important**: This model is intended for research and clinical decision support development only.

* **Not a Substitute for Medical Professionals**: Outputs must be reviewed by qualified clinicians.
* **Execution Risk**: If integrated into automated clinical workflows, safeguards and validation layers are mandatory.
* **Data Distribution Sensitivity**: Performance may vary across rare diseases or underrepresented modalities.
* **Hardware Requirements**: FP8 requires compatible GPU hardware for optimal acceleration.