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VietAlphaLabs
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VietAlphaLabs
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VietAlphaLabs/SenOCR-Vi
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Supported Functionality
SenOCR-Vi INT8 is the efficiency-focused release of SenOCR-Vi, VietAlpha Lab's Vietnamese-specialized document OCR model. At less than half the size of the full-precision checkpoint, it is built for on-device OCR: smartphones, laptops and edge hardware, with no server round-trip.
from_pretrained call with no remote code.bash
pip install "transformers>=5.0.0" torchao torch pillowpython
from PIL import Imageimport torchfrom transformers import AutoProcessor, AutoModelForImageTextToText model_id = "VietAlphaLabs/SenOCR-Vi-INT8" model = AutoModelForImageTextToText.from_pretrained( model_id, dtype=torch.bfloat16, device_map="auto").eval()processor = AutoProcessor.from_pretrained(model_id) image = Image.open("document.png").convert("RGB")messages = [ { "role": "user", "content": [ {"type": "image", "image": image}, {"type": "text", "text": "OCR:"}, ], }] 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=512)print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))[!TIP] Load with
dtype=torch.bfloat16. Without it, the non-quantized modules load in FP32.
shell
hf download VietAlphaLabs/SenOCR-Vi-INT8 --local-dir SenOCR-Vi-INT8/The published SenOCR-Vi benchmarks (82.83 Vietnamese document composite, 86.7% Vietnamese text recognition) were measured on the full-precision release. Separate scores for the INT8 build are not yet available, so validate it on your own documents before production use.
See the SenOCR-Vi model card for full evaluation, training, data and limitations.
SenOCR-Vi INT8 is released under the Apache License 2.0, the same license as SenOCR-Vi and PaddleOCR-VL-1.6.
bibtex
@misc{vietalphalab2026senocrvi, title={SenOCR-Vi: A Vietnamese-Specialized Document OCR Model}, author={VietAlpha Lab}, year={2026}, publisher={Hugging Face}, url={https://huggingface.co/VietAlphaLabs/SenOCR-Vi},}pip install "transformers>=5.0.0" torchao torch pillowfrom PIL import Imageimport torchfrom transformers import AutoProcessor, AutoModelForImageTextToText model_id = "VietAlphaLabs/SenOCR-Vi-INT8" model = AutoModelForImageTextToText.from_pretrained( model_id, dtype=torch.bfloat16, device_map="auto").eval()processor = AutoProcessor.from_pretrained(model_id) image = Image.open("document.png").convert("RGB")messages = [ { "role": "user", "content": [ {"type": "image", "image": image}, {"type": "text", "text": "OCR:"}, ], }] 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=512)print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))@misc{vietalphalab2026senocrvi, title={SenOCR-Vi: A Vietnamese-Specialized Document OCR Model}, author={VietAlpha Lab}, year={2026}, publisher={Hugging Face}, url={https://huggingface.co/VietAlphaLabs/SenOCR-Vi},}