Google-backed Cropin Feels Its Agricultural Chatbot 'Aksara' Outmatches GPT-4 Turbo In Relevance
Google-backed agricultural tech company Cropin on Tuesday introduced ‘Akṣara’, the first purpose-built open-source micro language model to address the problems faced by the underserved farming communities in the global south by removing barriers to knowledge and empowering anyone in the agriculture ecosystem to build frugal and scalable AI solutions for the sector.
In a statement, Cropin said the model to be more relevant than GPT-4 Turbo by almost 40 per cent as measured by the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) scoring algorithm. The model ensures that the responses are factually relevant and brief while minimising the compute and storage resource requirement, it added.
The company highlighted the aim is to empower agricultural stakeholders, developers, and researchers to tackle global challenges like food security, climate change, resource conservation - water and soil, and regenerative agriculture practices amongst others by providing access to contextual, factual and actionable information.
Cropin claimed the first version of Akṣara would cover nine crops viz, paddy, wheat, maize, sorghum, barley, cotton, sugarcane, soybean, and millet for 5 countries in the Indian subcontinent. These food crops collectively account for a substantial portion of the world's food requirements and are the staple food for the population in the global south.
Speaking on the development, Krishna Kumar, Founder & CEO, Cropin said, "In an era where Large Language Models are reshaping jobs, businesses, and customer interactions, the spotlight is now on industry-specific models trained on niche and comprehensive domain data as the 'next big thing."
He stressed Akṣara reinstates the company's commitment to leading the tech-driven agricultural movement in the years ahead, significantly impacting small-scale farmers' lives.
The model, hosted on Hugging Face, Akṣara is a frugal and scalable micro-LM built and fine-tuned on top of the Mistral-7B-v0.2 model. Recognising the environmental impact of running large language models (LLMs), Cropin has compressed ‘akṣara’ into 4-bit from 16-bit by using QLoRA or Quantization and Low-Rank Adapters (LoRA).
