Instructions to use OpenGVLab/Mini-InternVL2-4B-DA-DriveLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenGVLab/Mini-InternVL2-4B-DA-DriveLM with Transformers:

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

pipe = pipeline("image-text-to-text", model="OpenGVLab/Mini-InternVL2-4B-DA-DriveLM", 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)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("OpenGVLab/Mini-InternVL2-4B-DA-DriveLM", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OpenGVLab/Mini-InternVL2-4B-DA-DriveLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenGVLab/Mini-InternVL2-4B-DA-DriveLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenGVLab/Mini-InternVL2-4B-DA-DriveLM",
		"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/OpenGVLab/Mini-InternVL2-4B-DA-DriveLM

SGLang

How to use OpenGVLab/Mini-InternVL2-4B-DA-DriveLM 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 "OpenGVLab/Mini-InternVL2-4B-DA-DriveLM" \
    --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": "OpenGVLab/Mini-InternVL2-4B-DA-DriveLM",
		"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 "OpenGVLab/Mini-InternVL2-4B-DA-DriveLM" \
        --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": "OpenGVLab/Mini-InternVL2-4B-DA-DriveLM",
		"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 OpenGVLab/Mini-InternVL2-4B-DA-DriveLM with Docker Model Runner:
```
docker model run hf.co/OpenGVLab/Mini-InternVL2-4B-DA-DriveLM
```

Mini-InternVL2-4B-DA-DriveLM / README.md

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	---
	license: mit
	pipeline_tag: image-text-to-text
	library_name: transformers
	base_model:
	- OpenGVLab/InternVL2-4B
	base_model_relation: merge
	language:
	- multilingual
	tags:
	- internvl
	- custom_code
	---

	# Mini-InternVL2-DA-RS

	[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL) [\[🆕 Blog\]](https://internvl.github.io/blog/) [\[📜 Mini-InternVL\]](https://arxiv.org/abs/2410.16261) [\[📜 InternVL 1.0\]](https://arxiv.org/abs/2312.14238) [\[📜 InternVL 1.5\]](https://arxiv.org/abs/2404.16821) [\[📜 InternVL 2.5\]](https://huggingface.co/papers/2412.05271)

	[\[🗨️ InternVL Chat Demo\]](https://internvl.opengvlab.com/) [\[🤗 HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL) [\[🚀 Quick Start\]](#quick-start) [\[📖 中文解读\]](https://zhuanlan.zhihu.com/p/706547971) [\[📖 Documents\]](https://internvl.readthedocs.io/en/latest/internvl2.0/domain_adaptation.html#data-preparation)


	![image/png](https://cdn-uploads.huggingface.co/production/uploads/64564b0e4a7ffb7d5a47f412/Qp9tEtBAjbq39bJZ7od4A.png)

	## Introduction

	We release the adaptation models for the specific domains: autonomous driving, medical images, and remote sensing.

	These models are built upon Mini-InternVL and fine-tuned using a unified adaptation framework, achieving good performance on tasks in specific domains.

	![image/png](https://cdn-uploads.huggingface.co/production/uploads/64564b0e4a7ffb7d5a47f412/rlz4XL8DFWXplvp0Yx4lg.png)

	<table>
	<tr>
	<th>Model Name</th>
	<th>HF Link</th>
	<th>Note</th>
	</tr>
	<tr>
	<td>Mini-InternVL2-DA-Drivelm</td>
	<td><a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-1B-DA-Drivelm">🤗1B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-2B-DA-Drivelm">🤗2B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-4B-DA-Drivelm">🤗4B</a></td>
	<td> Adaptation for <a href="https://github.com/OpenDriveLab/DriveLM/tree/main/challenge"> CVPR 2024 Autonomous Driving Challenge </a></td>
	</tr>
	<tr>
	<td>Mini-InternVL2-DA-BDD</td>
	<td><a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-1B-DA-BDD">🤗1B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-2B-DA-BDD">🤗2B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-4B-DA-BDD">🤗4B</a></td>
	<td> Fine-tuning with data constructed by <a href="https://tonyxuqaq.github.io/projects/DriveGPT4/"> DriveGPT4 </a></td>
	</tr>
	<tr>
	<td>Mini-InternVL2-DA-RS</td>
	<td><a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-1B-DA-RS">🤗1B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-2B-DA-RS">🤗2B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-4B-DA-RS">🤗4B</a></td>
	<td> Adaptation for remote sensing domain </td>
	</tr>
	<tr>
	<td>Mini-InternVL2-DA-Medical</td>
	<td><a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-1B-DA-Medical">🤗1B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-2B-DA-Medical">🤗2B</a> / <a href="https://huggingface.co/OpenGVLab/Mini-InternVL2-4B-DA-Medical">🤗4B</a></td>
	<td> Fine-tuning using our <a href="https://huggingface.co/datasets/OpenGVLab/InternVL-Domain-Adaptation-Data/blob/main/train_meta/internvl_1_2_finetune_medical.json">medical data</a>.</td>
	</tr>
	</table>

	The script for evaluation is in the [document](https://internvl.readthedocs.io/en/latest/internvl2.0/domain_adaptation.html#id3).

	## Training datasets

	- General domain dataset:

	ShareGPT4V, AllSeeingV2, LLaVA-Instruct-ZH, DVQA, ChartQA, AI2D, DocVQA, GeoQA+, SynthDoG-EN

	- Autonomous driving dataset:

	[DriveLM](https://github.com/OpenDriveLab/DriveLM).

	## Quick Start

	We provide an example code to run `Mini-InternVL2-4B` using `transformers`.

	> Please use transformers>=4.37.2 to ensure the model works normally.


	```python
	import numpy as np
	import torch
	import torchvision.transforms as T
	from decord import VideoReader, cpu
	from PIL import Image
	from torchvision.transforms.functional import InterpolationMode
	from transformers import AutoModel, AutoTokenizer

	IMAGENET_MEAN = (0.485, 0.456, 0.406)
	IMAGENET_STD = (0.229, 0.224, 0.225)

	def build_transform(input_size):
	MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
	transform = T.Compose([
	T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
	T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
	T.ToTensor(),
	T.Normalize(mean=MEAN, std=STD)
	])
	return transform

	def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
	best_ratio_diff = float('inf')
	best_ratio = (1, 1)
	area = width * height
	for ratio in target_ratios:
	target_aspect_ratio = ratio[0] / ratio[1]
	ratio_diff = abs(aspect_ratio - target_aspect_ratio)
	if ratio_diff < best_ratio_diff:
	best_ratio_diff = ratio_diff
	best_ratio = ratio
	elif ratio_diff == best_ratio_diff:
	if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
	best_ratio = ratio
	return best_ratio

	def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
	orig_width, orig_height = image.size
	aspect_ratio = orig_width / orig_height

	# calculate the existing image aspect ratio
	target_ratios = set(
	(i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
	i * j <= max_num and i * j >= min_num)
	target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])

	# find the closest aspect ratio to the target
	target_aspect_ratio = find_closest_aspect_ratio(
	aspect_ratio, target_ratios, orig_width, orig_height, image_size)

	# calculate the target width and height
	target_width = image_size * target_aspect_ratio[0]
	target_height = image_size * target_aspect_ratio[1]
	blocks = target_aspect_ratio[0] * target_aspect_ratio[1]

	# resize the image
	resized_img = image.resize((target_width, target_height))
	processed_images = []
	for i in range(blocks):
	box = (
	(i % (target_width // image_size)) * image_size,
	(i // (target_width // image_size)) * image_size,
	((i % (target_width // image_size)) + 1) * image_size,
	((i // (target_width // image_size)) + 1) * image_size
	)
	# split the image
	split_img = resized_img.crop(box)
	processed_images.append(split_img)
	assert len(processed_images) == blocks
	if use_thumbnail and len(processed_images) != 1:
	thumbnail_img = image.resize((image_size, image_size))
	processed_images.append(thumbnail_img)
	return processed_images

	def load_image(image_file, input_size=448, max_num=12):
	image = Image.open(image_file).convert('RGB')
	transform = build_transform(input_size=input_size)
	images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
	pixel_values = [transform(image) for image in images]
	pixel_values = torch.stack(pixel_values)
	return pixel_values

	# If you want to load a model using multiple GPUs, please refer to the `Multiple GPUs` section.
	path = 'OpenGVLab/Mini-InternVL2-4B-DA-DriveLM'
	model = AutoModel.from_pretrained(
	path,
	torch_dtype=torch.bfloat16,
	low_cpu_mem_usage=True,
	use_flash_attn=True,
	trust_remote_code=True).eval().cuda()
	tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)

	# set the max number of tiles in `max_num`
	pixel_values = load_image('path/to/image.jpg', max_num=12).to(torch.bfloat16).cuda()
	generation_config = dict(max_new_tokens=1024, do_sample=True)

	# pure-text conversation (纯文本对话)
	question = 'Hello, who are you?'
	response, history = model.chat(tokenizer, None, question, generation_config, history=None, return_history=True)
	print(f'User: {question}\nAssistant: {response}')

	question = 'Can you tell me a story?'
	response, history = model.chat(tokenizer, None, question, generation_config, history=history, return_history=True)
	print(f'User: {question}\nAssistant: {response}')

	# single-image single-round conversation (单图单轮对话)
	question = '<image>\nPlease describe the image shortly.'
	response = model.chat(tokenizer, pixel_values, question, generation_config)
	print(f'User: {question}\nAssistant: {response}')

	# single-image multi-round conversation (单图多轮对话)
	question = '<image>\nPlease describe the image in detail.'
	response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
	print(f'User: {question}\nAssistant: {response}')

	question = 'Please write a poem according to the image.'
	response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True)
	print(f'User: {question}\nAssistant: {response}')

	# multi-image multi-round conversation, combined images (多图多轮对话，拼接图像)
	pixel_values1 = load_image('path/to/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
	pixel_values2 = load_image('path/to/image2.jpg', max_num=12).to(torch.bfloat16).cuda()
	pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)

	question = '<image>\nDescribe the two images in detail.'
	response, history = model.chat(tokenizer, pixel_values, question, generation_config,
	history=None, return_history=True)
	print(f'User: {question}\nAssistant: {response}')

	question = 'What are the similarities and differences between these two images.'
	response, history = model.chat(tokenizer, pixel_values, question, generation_config,
	history=history, return_history=True)
	print(f'User: {question}\nAssistant: {response}')

	# multi-image multi-round conversation, separate images (多图多轮对话，独立图像)
	pixel_values1 = load_image('path/to/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
	pixel_values2 = load_image('path/to/image2.jpg', max_num=12).to(torch.bfloat16).cuda()
	pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
	num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)]

	question = 'Image-1: <image>\nImage-2: <image>\nDescribe the two images in detail.'
	response, history = model.chat(tokenizer, pixel_values, question, generation_config,
	num_patches_list=num_patches_list,
	history=None, return_history=True)
	print(f'User: {question}\nAssistant: {response}')

	question = 'What are the similarities and differences between these two images.'
	response, history = model.chat(tokenizer, pixel_values, question, generation_config,
	num_patches_list=num_patches_list,
	history=history, return_history=True)
	print(f'User: {question}\nAssistant: {response}')

	# batch inference, single image per sample (单图批处理)
	pixel_values1 = load_image('path/to/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
	pixel_values2 = load_image('path/to/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
	num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)]
	pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)

	questions = ['<image>\nDescribe the image in detail.'] * len(num_patches_list)
	responses = model.batch_chat(tokenizer, pixel_values,
	num_patches_list=num_patches_list,
	questions=questions,
	generation_config=generation_config)
	for question, response in zip(questions, responses):
	print(f'User: {question}\nAssistant: {response}')

	```
	## Citation

	If you find this project useful in your research, please consider citing:

	```BibTeX
	@article{gao2024mini,
	title={Mini-internvl: A flexible-transfer pocket multimodal model with 5\% parameters and 90\% performance},
	author={Gao, Zhangwei and Chen, Zhe and Cui, Erfei and Ren, Yiming and Wang, Weiyun and Zhu, Jinguo and Tian, Hao and Ye, Shenglong and He, Junjun and Zhu, Xizhou and others},
	journal={arXiv preprint arXiv:2410.16261},
	year={2024}
	}
	@article{chen2024expanding,
	title={Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling},
	author={Chen, Zhe and Wang, Weiyun and Cao, Yue and Liu, Yangzhou and Gao, Zhangwei and Cui, Erfei and Zhu, Jinguo and Ye, Shenglong and Tian, Hao and Liu, Zhaoyang and others},
	journal={arXiv preprint arXiv:2412.05271},
	year={2024}
	}
	@article{chen2024far,
	title={How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites},
	author={Chen, Zhe and Wang, Weiyun and Tian, Hao and Ye, Shenglong and Gao, Zhangwei and Cui, Erfei and Tong, Wenwen and Hu, Kongzhi and Luo, Jiapeng and Ma, Zheng and others},
	journal={arXiv preprint arXiv:2404.16821},
	year={2024}
	}
	@inproceedings{chen2024internvl,
	title={Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks},
	author={Chen, Zhe and Wu, Jiannan and Wang, Wenhai and Su, Weijie and Chen, Guo and Xing, Sen and Zhong, Muyan and Zhang, Qinglong and Zhu, Xizhou and Lu, Lewei and others},
	booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
	pages={24185--24198},
	year={2024}
	}
	```