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# XMAiNframe: A Large Language Model for Mainframe Modernization
## Introduction We are introducing **XMAiNframe**, a state-of-the-art large language model (LLM) specifically designed with knowledge of mainframe legacy systems and COBOL codebases. XMAiNframe is built on top of DeepSeek-Coder 7B and is available with 7B and 10.5B parameters. Additionally, we present [MainframeBench](https://huggingface.co/datasets/Fsoft-AIC/MainframeBench), a comprehensive benchmark for assessing mainframe knowledge, including multiple-choice questions, question answering, and COBOL code summarization. Our empirical evaluations demonstrate that XMAiNframe consistently outperforms existing state-of-the-art LLMs across these tasks. Specifically, XMAiNframe achieves 30% higher accuracy than DeepSeek-Coder on multiple-choice questions, doubles the BLEU score of Mixtral-Instruct 8x7B on question answering, and scores six times higher than GPT-3.5 on COBOL summarization. Our work highlights the potential of XMAiNframe to drive significant advancements in managing and modernizing legacy systems, thereby enhancing productivity and saving time for software developers. ## Model Versions We release XMAiNframe with 7B and 10.5B parameters, including base and instruct models, to the public. XMAiNframe 10.5B is expanded from DeepSeek-Coder 7B by the depth up-scaling method without introducing additional modules or dynamic expert selection methods.
| **Model** | **Download** | | :-----------------------------: | :----------------------------------------------------------: | | XMAiNframe-base-7b | [🤗 HuggingFace](https://https://huggingface.co/Fsoft-AIC/XMAiNframe-base-7b/) | | XMAiNframe-instruct-7b | [🤗 HuggingFace](https://huggingface.co/Fsoft-AIC/XMAiNframe-instruct-7b) | | XMAiNframe-base-10.5b | [🤗 HuggingFace](https://huggingface.co/Fsoft-AIC/XMAiNframe-base-10.5b) | | XMAiNframe-instruct-10.5b | [🤗 HuggingFace](https://huggingface.co/Fsoft-AIC/XMAiNframe-instruct-10.5b) |
## Quickstart Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Fsoft-AIC/XMAiNframe-instruct-7b") model = AutoModelForCausalLM.from_pretrained("Fsoft-AIC/XMAiNframe-instruct-7b") messages=[ {'role':'system','content':"You are a helpful assistant"}, {'role': 'user', 'content': 'What is the future of Mainframe?'} ] inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id) print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)) ``` ## Additional Information ### Other Resources: - Github: https://github.com/FSoft-AI4Code/XMainframe - Paper: https://arxiv.org/html/2406.11927v1 ### License [MIT License](LICENSE) ### Citation Information More details can be found in our [paper](https://github.com/FSoft-AI4Code/). If you're using XMAiNframe, please cite using this BibTeX: ``` @article{, title={XMAiNframe: A Large Language Model for Mainframe Modernization}, author={}, journal={arXiv preprint }, year={2024} } ``` # Contact us If you have any questions, comments or suggestions, please do not hesitate to contact us. - Website: [fpt-aicenter](https://www.fpt-aicenter.com/ai-residency/) - Email: support.ailab@fpt.com