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---
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license: mit
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---
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<p align="center">
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<img src="./asset/XMAiNframe.png" width="560px" alt="logo">
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</p>
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<div align="center">
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# XMAiNframe: A Large Language Model for Mainframe Modernization
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</div>
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## Introduction
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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.
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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.
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## Model Versions
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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.
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<div align="center">
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| **Model** | **Download** |
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| :-----------------------------: | :----------------------------------------------------------: |
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| XMAiNframe-base-7b | [🤗 HuggingFace](https://https://huggingface.co/Fsoft-AIC/XMAiNframe-base-7b/) |
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| XMAiNframe-instruct-7b | [🤗 HuggingFace](https://huggingface.co/Fsoft-AIC/XMAiNframe-instruct-7b) |
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| XMAiNframe-base-10.5b | [🤗 HuggingFace](https://huggingface.co/Fsoft-AIC/XMAiNframe-base-10.5b) |
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| XMAiNframe-instruct-10.5b | [🤗 HuggingFace](https://huggingface.co/Fsoft-AIC/XMAiNframe-instruct-10.5b) |
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</div>
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## Quickstart
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Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("Fsoft-AIC/XMAiNframe-instruct-7b")
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model = AutoModelForCausalLM.from_pretrained("Fsoft-AIC/XMAiNframe-instruct-7b")
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messages=[
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{'role':'system','content':"You are a helpful assistant"},
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{'role': 'user', 'content': 'What is the future of Mainframe?'}
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]
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inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
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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)
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print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
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```
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## Additional Information
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### Other Resources:
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- Github: https://github.com/FSoft-AI4Code/XMainframe
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- Paper: https://arxiv.org/html/2406.11927v1
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### License
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[MIT License](LICENSE)
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### Citation Information
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More details can be found in our [paper](https://github.com/FSoft-AI4Code/).
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If you're using XMAiNframe, please cite using this BibTeX:
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```
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@article{,
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title={XMAiNframe: A Large Language Model for Mainframe Modernization},
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author={},
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journal={arXiv preprint },
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year={2024}
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}
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```
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# Contact us
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If you have any questions, comments or suggestions, please do not hesitate to contact us.
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- Website: [fpt-aicenter](https://www.fpt-aicenter.com/ai-residency/)
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- Email: [email protected]
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