301 lines
9.3 KiB
Markdown
301 lines
9.3 KiB
Markdown
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---
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license: apache-2.0
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language:
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- en
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pipeline_tag: image-text-to-text
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tags:
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- multimodal
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library_name: transformers
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base_model:
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- Qwen/Qwen2.5-VL-7B-Instruct
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---
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# <img src="assets/OctoMed.svg" alt="OctoMed Logo" width="100" style="vertical-align:bottom; margin-right:0px;" /> OctoMed-7B
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## Introduction
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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.
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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.
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OctoMed-7B produces internal reasoning traces in \<think>...\</think> 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.
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## Evaluation
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### Medical Benchmark Performances
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<p align="center">
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<img src="assets/performances.svg" alt="Medical Benchmark Performances" width="100%" />
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</p>
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**Notes:**
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- Green = OSS smaller models (<10B), Cyan = large proprietary models.
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- † = 10-sample majority vote ensemble result.
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### Legacy Medical Benchmark Performance
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| Dataset | Setting | Performance |
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|----------|---------|--------------|
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| VQA-RAD | Open (Token F1) | 64.23 |
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| VQA-RAD | Closed (Accuracy) | 85.66 |
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| SLAKE | Open (Token F1) | 84.96 |
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| SLAKE | Closed (Accuracy) | 89.66 |
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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.
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## Requirements
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We recommend installing the transformers version used in our experiments and other dependencies with this command:
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```
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pip install transformers==4.57.1 accelerate==1.12.0 torchvision==0.24.1 qwen-vl-utils==0.0.14
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```
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## Quickstart
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Below, we provide a some examples to show how to use OctoMed-7B with 🤗 Transformers or vLLM.
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<details>
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<summary>Inference with HF Transformers 🤗</summary>
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Here we show a code snippet to show you how chat with OctoMed-7B using `transformers` and `qwen_vl_utils`:
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```python
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import torch
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from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
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from qwen_vl_utils import process_vision_info
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# default: Load the model on the available device(s)
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model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
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"OctoMed/OctoMed-7B", dtype=torch.bfloat16, device_map="auto"
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)
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# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
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# model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
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# "OctoMed/OctoMed-7B",
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# dtype=torch.bfloat16,
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# attn_implementation="flash_attention_2",
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# device_map="auto",
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# )
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# The default range for the number of visual tokens per image in the model is 4-16384.
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# 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.
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min_pixels = 262144
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max_pixels = 262144
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processor = AutoProcessor.from_pretrained("OctoMed/OctoMed-7B", min_pixels=min_pixels, max_pixels=max_pixels)
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# Text-Only Query
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# messages = [
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# {
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# "role": "user",
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# "content": [
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# {"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?"},
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# ],
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# }
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# ]
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# General Query
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# messages = [
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# {
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# "role": "user",
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# "content": [
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# {
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# "type": "image",
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# "image": "https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51b2/10835941/13323b55fbb5/13256_2024_4349_Fig1_HTML.jpg",
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# },
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# {"type": "text", "text": "Describe this image."},
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# ],
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# }
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# ]
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# Multiple Choice Query
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "image",
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"image": "https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51b2/10835941/13323b55fbb5/13256_2024_4349_Fig1_HTML.jpg",
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},
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{"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{}."},
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],
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}
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]
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# Preparation for inference
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text = processor.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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image_inputs, video_inputs = process_vision_info(messages)
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inputs = processor(
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text=[text],
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images=image_inputs,
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videos=video_inputs,
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padding=True,
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return_tensors="pt",
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)
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inputs = inputs.to(device="cuda")
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# Inference: Generation of the output
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generated_ids = model.generate(**inputs, max_new_tokens=8192)
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generated_ids_trimmed = [
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out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
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]
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output_text = processor.batch_decode(
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
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)
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print(output_text)
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```
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</details>
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<details>
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<summary>Inference with vLLM</summary>
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Here we show an example of how to use OctoMed with vLLM (tested with vLLM==0.11.2 and transformers==4.57.1):
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```python
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from vllm import LLM, SamplingParams
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from transformers import AutoProcessor
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min_pixels = 262144
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max_pixels = 262144
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processor = AutoProcessor.from_pretrained("OctoMed/OctoMed-7B", min_pixels=min_pixels, max_pixels=max_pixels)
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llm = LLM(
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model="OctoMed/OctoMed-7B",
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trust_remote_code=True,
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dtype="bfloat16",
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max_model_len=8192,
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tensor_parallel_size=4,
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gpu_memory_utilization=0.8,
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limit_mm_per_prompt={"image": 1}
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)
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# Set up sampling parameters
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sampling_params = SamplingParams(
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temperature=0.6,
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top_p=0.95,
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max_tokens=8192,
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)
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image_data = []
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# Text-Only Query
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "Explain the difference between type 1 and type 2 diabetes."},
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],
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}
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]
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# General Query
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# image_data = ['https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51b2/10835941/13323b55fbb5/13256_2024_4349_Fig1_HTML.jpg']
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# messages = [
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# {
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# "role": "user",
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# "content": [
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# {
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# "type": "image",
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# "image": image_data[0],
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# },
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# {"type": "text", "text": "Describe this image."},
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# ],
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# }
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# ]
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# Multiple Choice Query
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# image_data = ['https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51b2/10835941/13323b55fbb5/13256_2024_4349_Fig1_HTML.jpg']
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# messages = [
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# {
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# "role": "user",
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# "content": [
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# {
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# "type": "image",
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# "image": image_data[0],
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# },
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# {"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{}."},
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# ],
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# }
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# ]
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prompt = processor.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True)
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if image_data:
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mm_prompt = {
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"prompt": prompt,
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"multi_modal_data": {"image": image_data}
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}
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else:
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mm_prompt = {"prompt": prompt}
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# Generate response
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outputs = llm.generate([mm_prompt], sampling_params)
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# Print the generated response
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for output in outputs:
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(f"Prompt: {prompt}")
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print(f"Generated text: {generated_text}")
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print("-" * 50)
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```
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</details>
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### Suggested Hyperparameters
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We suggest using the same settings used in evaluation to reproduce results:
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Format multiple choice questions with the following template:
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```
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{optional image(s)}
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{question}
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{options, 1 on each line}
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Please reason step-by-step, and put your final answer within \\boxed{}.
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```
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Example Prompt:
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```
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{image(s)}
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What orientation was the MRI in image B taken in?
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A: Axial
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B: Coronal
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C: Sagittal
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D: Oblique
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Please reason step-by-step, and put your final answer within \\boxed{}.
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```
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- Use the default system prompt ("You are a helpful assistant.")
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- Extract the answer by looking at the content within the last \\boxed{}.
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- Temperature of 0.6
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- Top-p of 0.95
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- min_pixels = 262144
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- max_pixels = 262144
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### Known Issues
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* Model is sensitive to system prompt. We recommend using the default one.
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* 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.
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We hope to address these concerns moving forward in future iterations!
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## Citation
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If you find our work helpful, feel free to give us a cite.
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```
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@article{ossowski2025octomed,
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title={OctoMed: Data Recipes for State-of-the-Art Multimodal Medical Reasoning},
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author={Ossowski, Timothy and Zhang, Sheng and Liu, Qianchu and Qin, Guanghui and Tan, Reuben and Naumann, Tristan and Hu, Junjie and Poon, Hoifung},
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journal={arXiv preprint arXiv:2511.23269},
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year={2025}
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}
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``` |