Instructions to use adopd/LocateAnything-3B-text-box-ADOPD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use adopd/LocateAnything-3B-text-box-ADOPD with Transformers:

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

pipe = pipeline("image-text-to-text", model="adopd/LocateAnything-3B-text-box-ADOPD", 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("adopd/LocateAnything-3B-text-box-ADOPD", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use adopd/LocateAnything-3B-text-box-ADOPD with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "adopd/LocateAnything-3B-text-box-ADOPD"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "adopd/LocateAnything-3B-text-box-ADOPD",
		"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/adopd/LocateAnything-3B-text-box-ADOPD

SGLang

How to use adopd/LocateAnything-3B-text-box-ADOPD 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 "adopd/LocateAnything-3B-text-box-ADOPD" \
    --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": "adopd/LocateAnything-3B-text-box-ADOPD",
		"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 "adopd/LocateAnything-3B-text-box-ADOPD" \
        --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": "adopd/LocateAnything-3B-text-box-ADOPD",
		"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 adopd/LocateAnything-3B-text-box-ADOPD with Docker Model Runner:
```
docker model run hf.co/adopd/LocateAnything-3B-text-box-ADOPD
```

LocateAnything-3B Text-Box ADOPD

Thinking with Anchors Project | ADOPD 2026 Paper: Thinking with Anchors: Grounded and Efficient Document Reasoning | ADOPD 2024 Paper | Dataset | Code

Model Overview

Model developer: Thinking with Anchors project contributors
Base model: nvidia/LocateAnything-3B
Model type: vision-language localization model
Task: grouped document text-region detection (Doc2Box)
Fine-tuning dataset: adopd/adopd2026
Input: one document image and a text-localization prompt
Output: generated LocateAnything box tokens

Description

LocateAnything-3B Text-Box ADOPD is a fine-tuned version of nvidia/LocateAnything-3B for locating grouped text regions in document images. The model is trained with one target box per structured OCR group in ADOPD and generates all detected regions in a single structured response.

The companion ADOPD code maps generated coordinates back to pixel-space boxes and preserves the raw model response for downstream evaluation.

Training Data

This checkpoint is fine-tuned on the ADOPD Doc2Box task. In the ADOPD2026 Parquet release, supervision is stored in:

ocr.grouped_blocks[].bbox_xyxy

Each training target represents one grouped OCR text region. The model does not use OCR transcription as an output target.

Input And Output

The recommended prompt is:

Detect all the text in box format.

The generated response uses LocateAnything normalized coordinate tokens. Use the companion parser to obtain pixel-space boxes_xyxy; do not interpret the raw tokens directly as pixels.

Quick Start

1. Install The Companion Code

git clone https://github.com/SichenZhu/ADOPD2026.git
cd ADOPD2026/release_code

python -m pip install -e model_zoo/common
python -m pip install -e model_zoo/locateanything_3b

Inference requires a CUDA-capable NVIDIA GPU. Eagle source and its compatibility overlay are required for fine-tuning, not for loading this self-contained Transformers checkpoint.

2. Download The Checkpoint

hf download adopd/LocateAnything-3B-text-box-ADOPD \
  --local-dir checkpoints/locany-text-box

3. Run Inference

adopd-locany-infer \
  --checkpoint checkpoints/locany-text-box \
  --image document.jpg \
  --prompt "Detect all the text in box format." \
  --output prediction.json

prediction.json contains the raw generated response and parsed pixel-space boxes.

Fine-Tuning And Evaluation

Download adopd/adopd2026, then prepare the Doc2Box split with:

adopd-locany-prepare \
  --dataset-root DATASET_ROOT \
  --output-root PREPARED_ROOT \
  --split train \
  --task text-box

Training and evaluation commands are documented in locateanything_3b.

Limitations

This model detects grouped text regions but does not transcribe text. Generated localization can be sensitive to document domain, image resolution, prompt wording, and decoding parameters. Evaluate and calibrate the model on the target domain before deployment.

License

Use is governed by the NVIDIA License included in this repository. The license permits academic and non-profit research use and does not permit commercial use except as stated in the license.

Citation

Please cite the ADOPD 2026 and ADOPD 2024 papers.

@misc{zhu2026thinkingwithanchors,
  title={Thinking with Anchors: Grounded and Efficient Document Reasoning},
  author={Sichen Zhu and Yuchen Zhu and Wenzhuo Xu and Jason Kuen and Wanrong Zhu and Jing Shi and Xuan Shen and Quanyi Wang and Yiwei Wang and Yujun Cai and Bing Shuai and Qin Zhang and Yongxin Chen and Shilong Liu and Molei Tao and Jiuxiang Gu},
  year={2026}
}

@inproceedings{gu2024adopd,
  title={{ADOPD}: A Large-Scale Document Page Decomposition Dataset},
  author={Jiuxiang Gu and Xiangxi Shi and Jason Kuen and Lu Qi and Ruiyi Zhang and Anqi Liu and Ani Nenkova and Tong Sun},
  booktitle={The Twelfth International Conference on Learning Representations},
  year={2024},
  url={https://openreview.net/forum?id=x1ptaXpOYa}
}