LayoutLLM is a new method for document analysis that combines large language models (LLMs) with document image understanding. The method uses a pre-trained VrDU model as an encoder to process document images and an LLM as a decoder to interpret and analyze the document's textual content and task. This approach allows the model to perform multiple tasks by fine-tuning to various VrDU tasks. The proposed model was evaluated on several benchmarks, including document image classification, information extraction, and document visual question-answering. The results showed that LayoutLLM outperformed professionally tuned models in several tasks and improved performance on NLP tasks. The model was fine-tuned using VrDU and NLP tasks, with the encoder processing document images and the decoder interpreting task instructions. The encoder uses a pre-trained LayoutLMv3 model to encode document images, while the decoder uses a pre-trained Llama model. The model was trained on a dataset of 52K instructions and their responses. The results showed that the model achieved high accuracy in document classification, information extraction, and visual question-answering tasks. The model's performance was also evaluated on NLP tasks, showing improvements in language comprehension. The study concludes that LayoutLLM is a flexible framework for multi-domain NLP and VrDU tasks, combining the strengths of VrDU models and LLMs.LayoutLLM is a new method for document analysis that combines large language models (LLMs) with document image understanding. The method uses a pre-trained VrDU model as an encoder to process document images and an LLM as a decoder to interpret and analyze the document's textual content and task. This approach allows the model to perform multiple tasks by fine-tuning to various VrDU tasks. The proposed model was evaluated on several benchmarks, including document image classification, information extraction, and document visual question-answering. The results showed that LayoutLLM outperformed professionally tuned models in several tasks and improved performance on NLP tasks. The model was fine-tuned using VrDU and NLP tasks, with the encoder processing document images and the decoder interpreting task instructions. The encoder uses a pre-trained LayoutLMv3 model to encode document images, while the decoder uses a pre-trained Llama model. The model was trained on a dataset of 52K instructions and their responses. The results showed that the model achieved high accuracy in document classification, information extraction, and visual question-answering tasks. The model's performance was also evaluated on NLP tasks, showing improvements in language comprehension. The study concludes that LayoutLLM is a flexible framework for multi-domain NLP and VrDU tasks, combining the strengths of VrDU models and LLMs.