kamal3501/gemma-4-agriculture-qa
The kamal3501/gemma-4-agriculture-qa is a 7.9 billion parameter instruction-tuned causal language model developed by Kamal Jaiswal. Built on Google's Gemma 4 E4B Instruct, it is specifically fine-tuned using QLoRA for question answering in agriculture, livestock, veterinary, and dairy farming domains. This model excels at providing specialized information for crop management, animal health, and various farming practices.
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Overview
This model, developed by Kamal Jaiswal, is an instruction-tuned version of google/gemma-4-E4B-it (7.9 billion parameters, 32768 context length) specifically designed for agricultural question answering. It was fine-tuned using QLoRA with LoRA Adapters and 4-bit quantization over one epoch, achieving a mean token accuracy of 79.73% and a perplexity of 2.49 on its validation set. The fine-tuning process focused on imparting domain-specific knowledge while maintaining stable performance.
Key Capabilities
- Domain-Specific Q&A: Provides answers related to crop management, livestock health, animal vaccination, soil health, irrigation, dairy farming, goat & sheep farming, and poultry management.
- Instruction-Tuned: Optimized for following instructions to deliver relevant agricultural information.
- Efficient Fine-Tuning: Utilizes QLoRA for efficient adaptation to specialized agricultural datasets.
Intended Uses
This model is suitable for applications requiring specialized agricultural knowledge, including:
- AI-powered agricultural chatbots and assistants
- Livestock and veterinary advisory systems
- Educational tools for farming practices
- Dairy farming support systems
Future Development
Future plans include Hindi and Hinglish fine-tuning, multilingual support, image-based crop disease diagnosis, livestock image analysis, and integration with RAG systems using government agricultural and veterinary knowledge bases.