Instructions to use AdaptLLM/biomed-InternVL3-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AdaptLLM/biomed-InternVL3-1B with Transformers:

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

pipe = pipeline("image-text-to-text", model="AdaptLLM/biomed-InternVL3-1B", trust_remote_code=True)
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("AdaptLLM/biomed-InternVL3-1B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AdaptLLM/biomed-InternVL3-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AdaptLLM/biomed-InternVL3-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AdaptLLM/biomed-InternVL3-1B",
		"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/AdaptLLM/biomed-InternVL3-1B

SGLang

How to use AdaptLLM/biomed-InternVL3-1B 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 "AdaptLLM/biomed-InternVL3-1B" \
    --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": "AdaptLLM/biomed-InternVL3-1B",
		"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 "AdaptLLM/biomed-InternVL3-1B" \
        --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": "AdaptLLM/biomed-InternVL3-1B",
		"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 AdaptLLM/biomed-InternVL3-1B with Docker Model Runner:
```
docker model run hf.co/AdaptLLM/biomed-InternVL3-1B
```

biomed-InternVL3-1B / README.md

AdaptLLM

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---
license: other
license_name: qwen
license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
pipeline_tag: image-text-to-text
library_name: transformers
base_model:
- OpenGVLab/InternVL3-1B
language:
- multilingual
tags:
- internvl
- multimodal
- biology
- medical
datasets:
- AdaptLLM/biomed-visual-instructions
---
# Adapting Multimodal Large Language Models to Domains via Post-Training (EMNLP 2025)

This repos contains the **biomedicine MLLM developed from InternVL3-1B** in our paper: [On Domain-Adaptive Post-Training for Multimodal Large Language Models](https://huggingface.co/papers/2411.19930). The correspoding training dataset is in [biomed-visual-instructions](https://huggingface.co/datasets/AdaptLLM/biomed-visual-instructions).

The main project page is: [Adapt-MLLM-to-Domains](https://huggingface.co/AdaptLLM/Adapt-MLLM-to-Domains)

## 1. To Chat with AdaMLLM  
Our model architecture aligns with the base model: InternVL3-1B, so you can refer to the official [OpenGVLab/InternVL3-1B](https://huggingface.co/OpenGVLab/InternVL3-1B) for the usage instructions.

**Note:** For AdaMLLM, always place the image at the beginning of the input instruction in the messages.  

## 2. Domain-Specific Benchmarks  

We provide [biomed-VQA-benchmark](https://huggingface.co/datasets/AdaptLLM/biomed-VQA-benchmark) for evaluating MLLMs on domain-specific tasks.

## 3. To Reproduce this Domain-Adapted MLLM  

Using our training data, [biomed-visual-instructions](https://huggingface.co/datasets/AdaptLLM/biomed-visual-instructions), you can easily reproduce our models based on the [LlamaFactory](https://github.com/hiyouga/LLaMA-Factory) repository.

For reference, we train from [OpenGVLab/InternVL3-1B-hf](https://huggingface.co/OpenGVLab/InternVL3-1B-hf) (note that we train from the `-hf` version) for 1 epoch with a learning rate of 1e-5 and a global batch size of 128.
After training, follow [this instruction](https://github.com/hiyouga/LLaMA-Factory/issues/8086#issuecomment-2898640569) to convert the `hf` version back to the official [OpenGVLab/InternVL3-1B](https://huggingface.co/OpenGVLab/InternVL3-1B) version.


## Citation
If you find our work helpful, please cite us.

[Adapt MLLM to Domains](https://huggingface.co/papers/2411.19930) (EMNLP 2025 Findings)
```bibtex
@article{adamllm,
  title={On Domain-Adaptive Post-Training for Multimodal Large Language Models},
  author={Cheng, Daixuan and Huang, Shaohan and Zhu, Ziyu and Zhang, Xintong and Zhao, Wayne Xin and Luan, Zhongzhi and Dai, Bo and Zhang, Zhenliang},
  journal={arXiv preprint arXiv:2411.19930},
  year={2024}
}
```

[Adapt LLM to Domains](https://huggingface.co/papers/2309.09530) (ICLR 2024)
```bibtex
@inproceedings{
cheng2024adapting,
title={Adapting Large Language Models via Reading Comprehension},
author={Daixuan Cheng and Shaohan Huang and Furu Wei},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=y886UXPEZ0}
}
```

	---
	license: other
	license_name: qwen
	license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
	pipeline_tag: image-text-to-text
	library_name: transformers
	base_model:
	- OpenGVLab/InternVL3-1B
	language:
	- multilingual
	tags:
	- internvl
	- multimodal
	- biology
	- medical
	datasets:
	- AdaptLLM/biomed-visual-instructions
	---

	# Adapting Multimodal Large Language Models to Domains via Post-Training (EMNLP 2025)

	This repos contains the biomedicine MLLM developed from InternVL3-1B in our paper: [On Domain-Adaptive Post-Training for Multimodal Large Language Models](https://huggingface.co/papers/2411.19930). The correspoding training dataset is in [biomed-visual-instructions](https://huggingface.co/datasets/AdaptLLM/biomed-visual-instructions).

	The main project page is: [Adapt-MLLM-to-Domains](https://huggingface.co/AdaptLLM/Adapt-MLLM-to-Domains)

	## 1. To Chat with AdaMLLM
	Our model architecture aligns with the base model: InternVL3-1B, so you can refer to the official [OpenGVLab/InternVL3-1B](https://huggingface.co/OpenGVLab/InternVL3-1B) for the usage instructions.

	Note: For AdaMLLM, always place the image at the beginning of the input instruction in the messages.

	## 2. Domain-Specific Benchmarks

	We provide [biomed-VQA-benchmark](https://huggingface.co/datasets/AdaptLLM/biomed-VQA-benchmark) for evaluating MLLMs on domain-specific tasks.

	## 3. To Reproduce this Domain-Adapted MLLM

	Using our training data, [biomed-visual-instructions](https://huggingface.co/datasets/AdaptLLM/biomed-visual-instructions), you can easily reproduce our models based on the [LlamaFactory](https://github.com/hiyouga/LLaMA-Factory) repository.

	For reference, we train from [OpenGVLab/InternVL3-1B-hf](https://huggingface.co/OpenGVLab/InternVL3-1B-hf) (note that we train from the `-hf` version) for 1 epoch with a learning rate of 1e-5 and a global batch size of 128.
	After training, follow [this instruction](https://github.com/hiyouga/LLaMA-Factory/issues/8086#issuecomment-2898640569) to convert the `hf` version back to the official [OpenGVLab/InternVL3-1B](https://huggingface.co/OpenGVLab/InternVL3-1B) version.


	## Citation
	If you find our work helpful, please cite us.

	[Adapt MLLM to Domains](https://huggingface.co/papers/2411.19930) (EMNLP 2025 Findings)
	```bibtex
	@article{adamllm,
	title={On Domain-Adaptive Post-Training for Multimodal Large Language Models},
	author={Cheng, Daixuan and Huang, Shaohan and Zhu, Ziyu and Zhang, Xintong and Zhao, Wayne Xin and Luan, Zhongzhi and Dai, Bo and Zhang, Zhenliang},
	journal={arXiv preprint arXiv:2411.19930},
	year={2024}
	}
	```

	[Adapt LLM to Domains](https://huggingface.co/papers/2309.09530) (ICLR 2024)
	```bibtex
	@inproceedings{
	cheng2024adapting,
	title={Adapting Large Language Models via Reading Comprehension},
	author={Daixuan Cheng and Shaohan Huang and Furu Wei},
	booktitle={The Twelfth International Conference on Learning Representations},
	year={2024},
	url={https://openreview.net/forum?id=y886UXPEZ0}
	}
	```