Instructions to use OctoMed/OctoMed-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OctoMed/OctoMed-7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="OctoMed/OctoMed-7B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("OctoMed/OctoMed-7B")
model = AutoModelForImageTextToText.from_pretrained("OctoMed/OctoMed-7B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OctoMed/OctoMed-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OctoMed/OctoMed-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OctoMed/OctoMed-7B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/OctoMed/OctoMed-7B

SGLang

How to use OctoMed/OctoMed-7B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "OctoMed/OctoMed-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OctoMed/OctoMed-7B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "OctoMed/OctoMed-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OctoMed/OctoMed-7B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use OctoMed/OctoMed-7B with Docker Model Runner:
```
docker model run hf.co/OctoMed/OctoMed-7B
```

OctoMed commited on Dec 6, 2025

Commit

f84fa4b

verified ·

1 Parent(s): 02c01e3

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -2

README.md CHANGED Viewed

@@ -284,7 +284,6 @@ Please reason step-by-step, and put your final answer within \\boxed{}.
 ### 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.
-* The model occasionally states that it cannot see the image even when the image is provided. Some of our text-only reasoning traces describe an image in words, and the expected reasoning trace indicates that the image is missing. Despite this, we observe high benchmark performance and the model can still reliably use visual information.
 We hope to address these concerns moving forward in future iterations!
@@ -293,7 +292,7 @@ We hope to address these concerns moving forward in future iterations!
 If you find our work helpful, feel free to give us a cite.
 ```
-@article{OctoMed,
   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},

 ### 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!
 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},