# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM
tokenizer = AutoTokenizer.from_pretrained("acceler-technology/dev-lhdn-parse-donut-model")
model = AutoModelForMultimodalLM.from_pretrained("acceler-technology/dev-lhdn-parse-donut-model", device_map="auto")LHDN Document Parser · Donut · Development
A Donut-based document-image parser for the acceler-technology team's LHDN document-processing project. This repository holds the development checkpoint for the team's model training and iteration work.
中文简介： 团队用于 LHDN 文档图片解析的 Donut 开发模型，探索从页面图片生成文档内容的文本表示。
Purpose and model details
Donut combines a visual encoder with a text decoder. It reads a document image and generates an output sequence without requiring a separate OCR model in the Donut architecture. PDF pages must first be rendered as images.
| Item | Value |
|---|---|
| Role | Development checkpoint |
| Architecture | VisionEncoderDecoderModel |
| Encoder / decoder | Donut Swin / mBART |
| Recorded base | naver-clova-ix/donut-base |
| Encoder image-size setting | [1800, 1350] |
| Saved Transformers version | 4.47.0 |
Source: config.json. Development and production snapshots have different image-size settings. Use this repository's processor alongside its weights.
Load the model
Install torch, transformers, sentencepiece, and pillow first.
from transformers import DonutProcessor, VisionEncoderDecoderModel
model_id = "acceler-technology/dev-lhdn-parse-donut-model"
processor = DonutProcessor.from_pretrained(model_id)
model = VisionEncoderDecoderModel.from_pretrained(model_id)
model.eval()
This is a loading example. The task prompt, image preparation, and output decoding must match the training setup. It is not a validated extraction demo.
Project and limitations
Related project data is held in lhdn-dataset, which remains private. The model history records training iterations. The exact training subset, full recipe, and quantitative evaluation results are not documented here.
Poor scans and unfamiliar layouts can affect generated output. Check extracted values against the source document before use. The model does not determine tax or legal outcomes.
Team and links
Developed within acceler-technology. Henry (HenryTeo27) led this project area, executed the data work, and participated in model training and technical discussions. kwyong handled model uploads.
Project showcase · Production Donut snapshot
A license has not been specified for this checkpoint. Check the checkpoint's and upstream model's terms before reuse.
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Model tree for acceler-technology/dev-lhdn-parse-donut-model
Base model
naver-clova-ix/donut-base
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("image-to-text", model="acceler-technology/dev-lhdn-parse-donut-model")