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shubhdotai
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Model Details
Model Provider
shubhdotai
Model Tree
lightonai/LightOnOCR-3-0.8B
Input Modalities
Output Modalities
Supported Functionality
python
import torchfrom PIL import Imagefrom transformers import AutoProcessor, Qwen3_5ForConditionalGenerationĀ model_id = "shubhdotai/kora-v0.1"device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"processor = AutoProcessor.from_pretrained(model_id)model = Qwen3_5ForConditionalGeneration.from_pretrained( model_id, dtype=torch.float32 if device == "cpu" else torch.bfloat16).to(device).eval()Ā img = Image.open("page.png").convert("RGB")img.thumbnail((1540, 1540)) # longest edge at most 1,540 pxprompt = processor.apply_chat_template([{"role": "user", "content": [{"type": "image"}]}], add_generation_prompt=True, tokenize=False, enable_thinking=False)inputs = processor(text=[prompt], images=[img], return_tensors="pt").to(device)end = processor.tokenizer.convert_tokens_to_ids("<|im_end|>")out = model.generate(**inputs, max_new_tokens=6144, do_sample=False, eos_token_id=end)print(processor.decode(out[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))Requires transformers 5.5.4 or newer.
repetition_penalty=1.05.Apache-2.0. Based on lightonai/LightOnOCR-3-0.8B (Apache-2.0).
import torchfrom PIL import Imagefrom transformers import AutoProcessor, Qwen3_5ForConditionalGenerationĀ model_id = "shubhdotai/kora-v0.1"device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"processor = AutoProcessor.from_pretrained(model_id)model = Qwen3_5ForConditionalGeneration.from_pretrained( model_id, dtype=torch.float32 if device == "cpu" else torch.bfloat16).to(device).eval()Ā img = Image.open("page.png").convert("RGB")img.thumbnail((1540, 1540)) # longest edge at most 1,540 pxprompt = processor.apply_chat_template([{"role": "user", "content": [{"type": "image"}]}], add_generation_prompt=True, tokenize=False, enable_thinking=False)inputs = processor(text=[prompt], images=[img], return_tensors="pt").to(device)end = processor.tokenizer.convert_tokens_to_ids("<|im_end|>")out = model.generate(**inputs, max_new_tokens=6144, do_sample=False, eos_token_id=end)print(processor.decode(out[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))