MedMO-8B-Next API & Inference Endpoint

🏆 Benchmark Performance

VQA & Text QA Results

MedMO-8B-Next sets a new state-of-the-art across the board, achieving the highest average scores on both medical VQA and Text QA benchmarks — surpassing strong baselines including Lingshu-7B and Fleming-VL-8B.

OMIVQA = OmniMedVQA · MedXQA = MedXpertQA · Medbullets reported as op4/op5

Medical VQA Benchmarks

Table with columns: Model, MMMU-Med, VQA-RAD (closed/all), SLAKE (closed/all), PathVQA, PMC-VQA, OmniMedVQA, MedXpertQA, Avg.
Model	MMMU-Med	VQA-RAD (closed/all)	SLAKE (closed/all)	PathVQA	PMC-VQA	OmniMedVQA	MedXpertQA	Avg.
Lingshu-7B	54.0	77.2 / 43.0	82.4 / 33.2	41.9	54.2	82.9	26.9	55.1
Fleming-VL-8B	63.3	78.4 / 56.4	86.9 / 80.0	56.5	64.3	88.2	21.6	66.1
MediX-R1-8B	63.3	75.2/51.6	70.3/54.4	41.0	55.3	73.8	24.9	57.1
MedMO-4B	54.6	50.9 / 35.0	41.0 / 30.0	42.4	50.6	79.7	24.8	45.4
MedMO-8B	64.6	72.3 / 64.7	70.6 / 70.0	56.3	59.4	84.8	26.2	63.2
MedMO-4B-Next	58.7	79.7 / 59.6	78.0 / 74.0	73.3	75.7	90.6	27.0	68.5
MedMO-8B-Next	69.3	86.4 / 68.0	83.0 / 81.6	56.3	74.1	93.3	42.9	72.7

Medical Text QA Benchmarks

Table with columns: Model, MMLU-Med, PubMedQA, MedMCQA, MedQA, Medbullets (op4/op5), MedXpertQA, SGPQA, Avg.
Model	MMLU-Med	PubMedQA	MedMCQA	MedQA	Medbullets (op4/op5)	MedXpertQA	SGPQA	Avg.
Lingshu-7B	69.6	75.8	56.3	63.5	62.0 / 53.8	16.4	27.5	53.1
Fleming-VL-8B	71.8

Bold = best result, underline = second-best result.

Benchmarked on AMD MI210 GPU.

Supported Imaging Modalities

Table with columns: Domain, Modalities
Domain	Modalities
Radiology	X-ray, CT, MRI, Ultrasound
Pathology	Whole-slide imaging, Microscopy
Ophthalmology	Fundus photography, OCT
Dermatology	Clinical skin images
Nuclear Medicine	PET, SPECT

🚀 Quick Start

Installation

bash
pip install transformers torch qwen-vl-utils

Basic Usage

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

# Load model
model = Qwen3VLForConditionalGeneration.from_pretrained(
    "MBZUAI/MedMO-8B-Next",
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
    device_map="auto",
)

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

# Prepare input
messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "path/to/medical/image.png",
            },
            {"type": "text", "text": "What abnormalities are present in this chest X-ray?"},
        ],
    }
]

# Process and generate
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(model.device)

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[0])

Example: Disease Localization with Bounding Boxes

python
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "chest_xray.png"},
            {"type": "text", "text": "Detect and localize all abnormalities in this image."},
        ],
    }
]
# Example output:
# "Fractures <box>[[156, 516, 231, 607], [240, 529, 296, 581]]</box>"

Example: Radiology Report Generation

python
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "ct_scan.png"},
            {"type": "text", "text": "Generate a detailed radiology report for this CT scan."},
        ],
    }
]
# MedMO-8B-Next generates comprehensive clinical reports with findings and impressions

📦 Model Family

Table with columns: Model, Parameters, Best For
Model	Parameters	Best For
MedMO-8B-Next	8B	SOTA highest accuracy, all tasks — recommended
MedMO-4B-Next	4B	2nd SOTA, high accuracy in resource-constrained environments
MedMO-8B	8B	Previous generation
MedMO-4B

📄 Citation

If you use MedMO in your research, please cite our paper:

bibtex
@article{deria2026medmo,
  title={MedMO: Grounding and Understanding Multimodal Large Language Model for Medical Images},
  author={Deria, Ankan and Kumar, Komal and Dukre, Adinath Madhavrao and Segal, Eran and Khan, Salman and Razzak, Imran},
  journal={arXiv preprint arXiv:2602.06965},
  year={2026}
}

📜 License

This project is licensed under the Apache License 2.0 — see the LICENSE file for details.

Model

MMMU-Med

VQA-RAD (closed/all)

SLAKE (closed/all)

PathVQA

PMC-VQA

OmniMedVQA

MedXpertQA

Avg.

Lingshu-7B

54.0

77.2 / 43.0

82.4 / 33.2

41.9

54.2

82.9

26.9

55.1

Fleming-VL-8B

63.3

78.4 / 56.4

86.9 / 80.0

56.5

64.3

88.2

21.6

66.1

MediX-R1-8B

63.3

75.2/51.6

70.3/54.4

41.0

55.3

73.8

24.9

57.1

MedMO-4B

54.6

50.9 / 35.0

41.0 / 30.0

42.4

50.6

79.7

24.8

45.4

MedMO-8B

64.6

72.3 / 64.7

70.6 / 70.0

56.3

59.4

84.8

26.2

63.2

MedMO-4B-Next

58.7

79.7 / 59.6

78.0 / 74.0

73.3

75.7

90.6

27.0

68.5

MedMO-8B-Next

69.3

86.4 / 68.0

83.0 / 81.6

56.3

74.1

93.3

42.9

72.7

Model

MMLU-Med

PubMedQA

MedMCQA

MedQA

Medbullets (op4/op5)

MedXpertQA

SGPQA

Avg.

Lingshu-7B

69.6

75.8

56.3

63.5

62.0 / 53.8

16.4

27.5

53.1

Fleming-VL-8B

71.8

Domain

Modalities

Radiology

X-ray, CT, MRI, Ultrasound

Pathology

Whole-slide imaging, Microscopy

Ophthalmology

Fundus photography, OCT

Dermatology

Clinical skin images

Nuclear Medicine

PET, SPECT

python

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

# Load model
model = Qwen3VLForConditionalGeneration.from_pretrained(
    "MBZUAI/MedMO-8B-Next",
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
    device_map="auto",
)

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

# Prepare input
messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "path/to/medical/image.png",
            },
            {"type": "text", "text": "What abnormalities are present in this chest X-ray?"},
        ],
    }
]

# Process and generate
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(model.device)

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[0])

python

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "chest_xray.png"},
            {"type": "text", "text": "Detect and localize all abnormalities in this image."},
        ],
    }
]
# Example output:
# "Fractures <box>[[156, 516, 231, 607], [240, 529, 296, 581]]</box>"

python

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "ct_scan.png"},
            {"type": "text", "text": "Generate a detailed radiology report for this CT scan."},
        ],
    }
]
# MedMO-8B-Next generates comprehensive clinical reports with findings and impressions

Model

Parameters

Best For

MedMO-8B-Next

SOTA highest accuracy, all tasks — recommended

MedMO-4B-Next

2nd SOTA, high accuracy in resource-constrained environments

MedMO-8B

Previous generation

MedMO-4B

bibtex

@article{deria2026medmo,
  title={MedMO: Grounding and Understanding Multimodal Large Language Model for Medical Images},
  author={Deria, Ankan and Kumar, Komal and Dukre, Adinath Madhavrao and Segal, Eran and Khan, Salman and Razzak, Imran},
  journal={arXiv preprint arXiv:2602.06965},
  year={2026}
}

MedMO-8B-Next

README

🏆 Benchmark Performance

VQA & Text QA Results

Medical VQA Benchmarks

Medical Text QA Benchmarks

Supported Imaging Modalities

🚀 Quick Start

Installation

Basic Usage

Example: Disease Localization with Bounding Boxes

Example: Radiology Report Generation

📦 Model Family

📄 Citation

📜 License

Explore FriendliAI today

README

🏆 Benchmark Performance

VQA & Text QA Results

Medical VQA Benchmarks

Medical Text QA Benchmarks

Supported Imaging Modalities

🚀 Quick Start

Installation

Basic Usage

Example: Disease Localization with Bounding Boxes

Example: Radiology Report Generation

📦 Model Family

📄 Citation

📜 License