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MyanmarGPT-Big - bnb 8bits

license: creativeml-openrail-m language:

Everyone can fine-tune this model. Designed primarily for Burmese text completion, this model serves as a foundational framework for fine-tuning various natural language processing tasks specific to the Burmese language context.

As Myanmar embarks on its journey towards AI democratization, a strategic and collaborative approach is crucial. Addressing challenges and seizing opportunities in sectors such as agriculture, healthcare, and education can position Myanmar as a regional leader in harnessing the benefits of AI for the betterment of its people and the advancement of its economy. With careful planning and investment, Myanmar has the potential to create a vibrant AI ecosystem that empowers individuals, businesses, and the nation as a whole.

There is already MyanmarGPT which 125 M parameters. But people in Myanmar has been asking me for More precise model with more weights, Here is the MyanmarGPT-Big Model now.

You can use the MyanmarGPT model 125 M which is lightweight and free to use.

MyanmarGPT-Big can be used for the following use cases.

Text generation, Chatbots and Virtual Assistants, Content Summarization, Translations, Question-Answering System, and Sentiment Analysis.

Originally crafted for text completion in Burmese, this model functions as a fundamental asset for various Natural Language Processing (NLP) tasks. Although its primary role is presently centered on aiding in text generation and completion, it harbors considerable potential for broader applications. Researchers and developers have the option to refine this model using specialized datasets, thereby expanding its utility to other NLP domains, including summarization and instruction-based tasks. Nevertheless, it is crucial to acknowledge that when dealing with high-stakes decisions or comprehending domain-specific terminology, additional specialized training for the model is advised to ensure optimal accuracy and reliability.

Users need to recognize the inherent limitations and biases present in language models. Responsible usage is crucial, particularly in sensitive contexts, as this model is not designed to generate misleading or harmful content.

While the MyanmarGPT-Big excels in handling general Burmese text, its effectiveness might be limited when dealing with daily-life spoken burmese words. Users are encouraged to perform comprehensive testing tailored to their specific use cases.

Special thanks to mGPT Project by ai-forever. Without mGPT, MyanmarGPT-Big would have taken a long time to move on from building from scratch.

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