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- Edit this `README.md` markdown file to author your organization card 🔥
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ## About
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+ The Odia Generative AI (in short, OdiaGenAI) is an initiative to research Generative AI and Large Language Models (LLMs) for the low-resource Odia language.
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+
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+ ## Objective
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+ The OdiaGenAI aims to
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+ 1. Build pre-trained Odia LLM,
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+ 2. Fine-tuned Odia LLM, and
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+ 3. Instruct LLM (Odia).
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+ The data, code, and models will be available to the public for research and non-commercial purposes.
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+ ## Why OdiaGenAI
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+ * **First**: Though many LLMs support multilingual, including Odia language, the performance for various tasks (e.g., content generation, question-answering) is limited due to the amount of ingested data for Odia.
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+ * **Second**: There is subscription or fees associated with the high-performing LLMs.
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+ * **Third**: The usage (privacy) and bias of data input to these LLMs are in question.
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+ ## What are the focus research areas of OdiaGenAI
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+ We have divided the primary focus areas into three parts.
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+
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+ **1. Literature Survey:** Investigate the latest developments in Generative AI and LLMs and analyze current methods to support the Odia language for different tasks.
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+ **2. Development:** Developing pre-trained and fine-tuned Odia LLM, which includes dataset preparation, model training, evaluation, prompt engineering, and API development.
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+ **3. Deployment:** Deploy the Odia LLM models for public access for research and non-commercial purposes.
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+ ## Who can use OdiaGenAI LLMs
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+ The models (pre-trained/fine-tuned) will be available through Hugging Face for research and non-commercial purposes. Feel free to contact us for a domain-specific application or particular use cases.
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+
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+ ## What are the use cases of OdiaGenAI LLMs
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+ There are several use cases of OdiaGenAI LLMs. Three primary domains relating to Odisha which we are focusing to use the developed LLM are:
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+ * Education
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+ * Healthcare
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+ * Governance
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+ * Tourism
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+ * Agriculture
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+ * Industrial Application
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+
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+ ## Contributors
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+ * [Shantipriya Parida](https://www.linkedin.com/in/shantipriya-parida-9781a9127/)
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+ * [Sambit Sekhar](https://www.linkedin.com/in/sambit-sekhar-ai/)
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+ * [Subhadarshi Panda](https://www.linkedin.com/in/subhadarshi-panda-1ba5091a/)
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+ * [Soumendra Kumar Sahoo](https://www.linkedin.com/in/soumendrak/)
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+ * [Swateek Jena](https://www.linkedin.com/in/swateek/)
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+ * [Abhijeet Parida](https://www.linkedin.com/in/a-parida/)
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+ * [Arghyadeep Sen](https://www.linkedin.com/in/arghyadeep-sen-kiit/)
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+ * [Dr. Satya Ranjan Dash](https://ksca.kiit.ac.in/profiles/satya-ranjan-dash/)