first commit

docs/source/en/model_doc/lighton_ocr.md

@@ -0,0 +1,74 @@
+<!--Copyright 2026 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the
+License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+specific language governing permissions and limitations under the License. -->
+*This model was contributed to Hugging Face Transformers on 2026-01-14.*
+
+# LightOnOcr
+
+
+**LightOnOcr** is a compact, end-to-end vision–language model for Optical Character Recognition (OCR) and document understanding. It achieves state-of-the-art accuracy in its weight class while being several times faster and cheaper than larger general-purpose VLMs.
+
+📝 **[Read the full blog post](https://huggingface.co/blog/lightonai/lightonocr/)** | 📓 **[Finetuning notebook](https://colab.research.google.com/drive/1WjbsFJZ4vOAAlKtcCauFLn_evo5UBRNa?usp=sharing)**
+
+**Model Overview**
+
+LightOnOcr combines a Vision Transformer encoder (Pixtral-based) with a lightweight text decoder (Qwen3-based) distilled from high-quality open VLMs. It is optimized for document parsing tasks, producing accurate, layout-aware text extraction from high-resolution pages.
+
+## Usage
+
+```python
+from transformers import LightOnOcrForConditionalGeneration, LightOnOcrProcessor
+
+
+model = LightOnOcrForConditionalGeneration.from_pretrained("lightonai/LightOnOCR-1B-1025", device_map="auto")
+processor = LightOnOcrProcessor.from_pretrained("lightonai/LightOnOCR-1B-1025")
+
+url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ocr/resolve/main/SROIE-receipt.jpeg"
+
+conversation = [{"role": "user", "content": [{"type": "image", "url": url}]}]
+
+inputs = processor.apply_chat_template(
+    conversation,
+    add_generation_prompt=True,
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt",
+).to(model.device)
+
+output_ids = model.generate(**inputs, max_new_tokens=1024)
+generated_ids = output_ids[0, inputs["input_ids"].shape[1] :]
+output_text = processor.decode(generated_ids, skip_special_tokens=True)
+print(output_text)
+```
+
+## LightOnOcrConfig
+
+[[autodoc]] LightOnOcrConfig
+
+## LightOnOcrProcessor
+
+[[autodoc]] LightOnOcrProcessor
+    - __call__
+
+## LightOnOcrModel
+
+[[autodoc]] LightOnOcrModel
+    - forward
+    - get_image_features
+
+## LightOnOcrForConditionalGeneration
+
+[[autodoc]] LightOnOcrForConditionalGeneration
+    - forward
+    - get_image_features