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Kimang18
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Kimang18
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Fine-tune PaddlePaddle/PaddleOCR-VL on WildKhmerST.
For future benchmarking in Khmer OCR task, WildKhmerST is processed to contains pairs of 'text region image' and 'text'.
The processed dataset is uploaded to huggingface platform here.
There are 3 splits. train split is used to full fine-tune PaddlePaddle/PaddleOCR-VL.
test split is used to evaluate the fine-tuned model.
This paddleocr_vl model was trained 2x faster with Unsloth
Inference using Huggingface transformers on NVIDIA GPUs.
Requirements tested on python 3.12.13, T4 GPU:
text
transformers<5.0.0python
from PIL import Imageimport torchfrom transformers import AutoModelForCausalLM, AutoProcessor # ---- Settings ----model_path = "Kimang18/paddleOCR_vl_Khmer_finetuned"image_path = "khmer_text_region.png"# ------------------ DEVICE = "cuda" if torch.cuda.is_available() else "cpu" image = Image.open(image_path).convert("RGB") model = AutoModelForCausalLM.from_pretrained( model_path, trust_remote_code=True, dtype=torch.bfloat16).to(DEVICE).eval()processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True) messages = [ {"role": "user", "content": [ {"type": "image", "image": image}, {"type": "text", "text": "OCR:"}, ] }]inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt").to(DEVICE) outputs = model.generate(**inputs, max_new_tokens=1024)outputs = processor.batch_decode(outputs, skip_special_tokens=True)[0]print(outputs)NOTE: if you use transformers>=5.0.0, you will encounter KeyError from rope_init_fn=ROPE_INIT_FUNCTIONS[rope_type].
This is because PaddleOCR-VL uses rope_type="default" which is not supported anymore for transformers>=5.0.0.
from PIL import Imageimport torchfrom transformers import AutoModelForCausalLM, AutoProcessor # ---- Settings ----model_path = "Kimang18/paddleOCR_vl_Khmer_finetuned"image_path = "khmer_text_region.png"# ------------------ DEVICE = "cuda" if torch.cuda.is_available() else "cpu" image = Image.open(image_path).convert("RGB") model = AutoModelForCausalLM.from_pretrained( model_path, trust_remote_code=True, dtype=torch.bfloat16).to(DEVICE).eval()processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True) messages = [ {"role": "user", "content": [ {"type": "image", "image": image}, {"type": "text", "text": "OCR:"}, ] }]inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt").to(DEVICE) outputs = model.generate(**inputs, max_new_tokens=1024)outputs = processor.batch_decode(outputs, skip_special_tokens=True)[0]print(outputs)