---
license: apache-2.0
language:
- en
pipeline_tag: image-text-to-text
tags:
- multimodal
library_name: transformers
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
---
# 
OctoMed-7B
## Introduction
OctoMed-7B is a high-performance multimodal medical reasoning model created through large-scale data curation and supervised fine-tuning (SFT). To support reliable clinical reasoning, we developed a scalable data pipeline that distills structured reasoning traces from DeepSeek-R1 and GPT-4o and produced the largest multimodal medical reasoning dataset to date with more than 8 million traces and 6.8 billion response tokens.
Using Qwen2.5-VL-7B-Instruct as the base model, OctoMed-7B is trained on this curated corpus and achieves strong, robust performance on a wide range of out-of-distribution medical benchmarks.
OctoMed-7B produces internal reasoning traces in \...\ tokens before writing out its final answer. In general, the model has a tendency to think longer for harder or ill-defined questions, while sticking to shorter reasoning traces for easier queries.
## Evaluation
### Medical Benchmark Performances
**Notes:**
- Green = OSS smaller models (<10B), Cyan = large proprietary models.
- † = 10-sample majority vote ensemble result.
### Legacy Medical Benchmark Performance
| Dataset | Setting | Performance |
|----------|---------|--------------|
| VQA-RAD | Open (Token F1) | 64.23 |
| VQA-RAD | Closed (Accuracy) | 85.66 |
| SLAKE | Open (Token F1) | 84.96 |
| SLAKE | Closed (Accuracy) | 89.66 |
We also train on the train splits of the VQA-RAD and SLAKE datasets and report the performances here. For these results, we apply a **direct** prompt by including the phrase **Answer in a short word or phrase.** at the end of each sample. GPT2 is used as the tokenizer to compute Token F1 for open-ended questions following prior work.
## Requirements
We recommend installing the transformers version used in our experiments and other dependencies with this command:
```
pip install transformers==4.57.1 accelerate==1.12.0 torchvision==0.24.1 qwen-vl-utils==0.0.14
```
## Quickstart
Below, we provide a some examples to show how to use OctoMed-7B with 🤗 Transformers or vLLM.
Inference with HF Transformers 🤗
Here we show a code snippet to show you how chat with OctoMed-7B using `transformers` and `qwen_vl_utils`:
```python
import torch
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
# default: Load the model on the available device(s)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"OctoMed/OctoMed-7B", dtype=torch.bfloat16, device_map="auto"
)
# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
# model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
# "OctoMed/OctoMed-7B",
# dtype=torch.bfloat16,
# attn_implementation="flash_attention_2",
# device_map="auto",
# )
# The default range for the number of visual tokens per image in the model is 4-16384.
# You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost.
min_pixels = 262144
max_pixels = 262144
processor = AutoProcessor.from_pretrained("OctoMed/OctoMed-7B", min_pixels=min_pixels, max_pixels=max_pixels)
# Text-Only Query
# messages = [
# {
# "role": "user",
# "content": [
# {"type": "text", "text": "I've had a persistent dry cough for two weeks but no fever. Could this be allergies, and when should I see a doctor?"},
# ],
# }
# ]
# General Query
# messages = [
# {
# "role": "user",
# "content": [
# {
# "type": "image",
# "image": "https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51b2/10835941/13323b55fbb5/13256_2024_4349_Fig1_HTML.jpg",
# },
# {"type": "text", "text": "Describe this image."},
# ],
# }
# ]
# Multiple Choice Query
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51b2/10835941/13323b55fbb5/13256_2024_4349_Fig1_HTML.jpg",
},
{"type": "text", "text": "What orientation was the MRI in image B taken in?\nA. Axial\nB. Coronal\nC. Sagittal\nD. Oblique\n\nPlease reason step-by-step, and put your final answer within \\boxed{}."},
],
}
]
# Preparation for inference
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",
)
inputs = inputs.to(device="cuda")
# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=8192)
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)
```
Inference with vLLM
Here we show an example of how to use OctoMed with vLLM (tested with vLLM==0.11.2 and transformers==4.57.1):
```python
from vllm import LLM, SamplingParams
from transformers import AutoProcessor
min_pixels = 262144
max_pixels = 262144
processor = AutoProcessor.from_pretrained("OctoMed/OctoMed-7B", min_pixels=min_pixels, max_pixels=max_pixels)
llm = LLM(
model="OctoMed/OctoMed-7B",
trust_remote_code=True,
dtype="bfloat16",
max_model_len=8192,
tensor_parallel_size=4,
gpu_memory_utilization=0.8,
limit_mm_per_prompt={"image": 1}
)
# Set up sampling parameters
sampling_params = SamplingParams(
temperature=0.6,
top_p=0.95,
max_tokens=8192,
)
image_data = []
# Text-Only Query
messages = [
{
"role": "user",
"content": [
{"type": "text", "text": "Explain the difference between type 1 and type 2 diabetes."},
],
}
]
# General Query
# image_data = ['https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51b2/10835941/13323b55fbb5/13256_2024_4349_Fig1_HTML.jpg']
# messages = [
# {
# "role": "user",
# "content": [
# {
# "type": "image",
# "image": image_data[0],
# },
# {"type": "text", "text": "Describe this image."},
# ],
# }
# ]
# Multiple Choice Query
# image_data = ['https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51b2/10835941/13323b55fbb5/13256_2024_4349_Fig1_HTML.jpg']
# messages = [
# {
# "role": "user",
# "content": [
# {
# "type": "image",
# "image": image_data[0],
# },
# {"type": "text", "text": "What orientation was the MRI in image B taken in?\nA. Axial\nB. Coronal\nC. Sagittal\nD. Oblique\n\nPlease reason step-by-step, and put your final answer within \\boxed{}."},
# ],
# }
# ]
prompt = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True)
if image_data:
mm_prompt = {
"prompt": prompt,
"multi_modal_data": {"image": image_data}
}
else:
mm_prompt = {"prompt": prompt}
# Generate response
outputs = llm.generate([mm_prompt], sampling_params)
# Print the generated response
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt}")
print(f"Generated text: {generated_text}")
print("-" * 50)
```
### Suggested Hyperparameters
We suggest using the same settings used in evaluation to reproduce results:
Format multiple choice questions with the following template:
```
{optional image(s)}
{question}
{options, 1 on each line}
Please reason step-by-step, and put your final answer within \\boxed{}.
```
Example Prompt:
```
{image(s)}
What orientation was the MRI in image B taken in?
A: Axial
B: Coronal
C: Sagittal
D: Oblique
Please reason step-by-step, and put your final answer within \\boxed{}.
```
- Use the default system prompt ("You are a helpful assistant.")
- Extract the answer by looking at the content within the last \\boxed{}.
- Temperature of 0.6
- Top-p of 0.95
- min_pixels = 262144
- max_pixels = 262144
### Known Issues
* Model is sensitive to system prompt. We recommend using the default one.
* The model is finetuned for multiple-choice VQA. The model may follow instructions for other tasks but is not extensively tested or post-trained to do so.
We hope to address these concerns moving forward in future iterations!
## Citation
If you find our work helpful, feel free to give us a cite.
```
@article{ossowski2025octomed,
title={OctoMed: Data Recipes for State-of-the-Art Multimodal Medical Reasoning},
author={Ossowski, Timothy and Zhang, Sheng and Liu, Qianchu and Qin, Guanghui and Tan, Reuben and Naumann, Tristan and Hu, Junjie and Poon, Hoifung},
journal={arXiv preprint arXiv:2511.23269},
year={2025}
}
```