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kamal3501/gemma-4-agriculture-qa

sourceHugging Facegemmaupdated 2mo agoView on Hugging Face
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Gemma 4 Agriculture Assistant

A domain-specific instruction-tuned version of Google Gemma 4 E4B Instruct, fine-tuned using QLoRA for agriculture, livestock, veterinary, and dairy farming question answering.

Model Details

Model Description

This model is built on google/gemma-4-E4B-it and fine-tuned for agricultural question answering using supervised instruction tuning.

It is designed to assist with:

  • โ€”๐ŸŒพ Crop management
  • โ€”๐Ÿ„ Livestock health
  • โ€”๐Ÿ’‰ Animal vaccination
  • โ€”๐ŸŒฑ Soil health
  • โ€”๐Ÿ’ง Irrigation
  • โ€”๐Ÿƒ Dairy farming
  • โ€”๐Ÿ Goat & sheep farming
  • โ€”๐Ÿฆ† Poultry management
  • โ€”๐ŸŒฟ Pest and disease management

Developed by

Kamal Jaiswal

Model Type

Instruction-tuned Causal Language Model

Base Model

google/gemma-4-E4B-it

Fine-tuned From

google/gemma-4-E4B-it

Languages

  • โ€”English

License

Gemma License

Training Details

Fine-tuning Method

  • โ€”QLoRA
  • โ€”LoRA Adapters
  • โ€”4-bit Quantization
  • โ€”Supervised Fine-Tuning (SFT)

Training Metrics

MetricValue
Base ModelGemma 4 E4B IT
Epochs1
Runtime937.6 seconds
Training Steps48
Validation Loss0.9112
Mean Token Accuracy79.73%
Train Samples/sec0.818
Train Steps/sec0.051

Evaluation

The model was evaluated on a held-out validation dataset after one epoch of supervised fine-tuning.

Evaluation Metrics

MetricValue
Validation Loss0.9112
Perplexity2.49
Mean Token Accuracy79.73%
Entropy0.8914
Validation Tokens769,406

Results

The model successfully learned domain-specific agricultural knowledge while maintaining stable validation performance.

Key observations:

  • โ€”Validation Loss: 0.9112
  • โ€”Perplexity: 2.49, indicating the model predicts validation tokens with relatively high confidence.
  • โ€”Mean Token Accuracy: 79.73%, showing strong token-level prediction performance.
  • โ€”The model completed one epoch of QLoRA fine-tuning without signs of instability.

Training Summary

MetricValue
Base Modelgoogle/gemma-4-E4B-it
Fine-tuning MethodQLoRA
Epochs1
Training Steps48
Runtime937.6 seconds (~15.6 min)
Train Loss1.6427
Validation Loss0.9112
Perplexity2.49
Mean Token Accuracy79.73%

Notes

These metrics represent an initial proof-of-concept fine-tuning run. Future versions of the model will be evaluated on domain-specific agriculture and livestock question-answering benchmarks, multilingual datasets (English and Hindi), and expert human evaluations.

Intended Uses

This model is suitable for:

  • โ€”Agriculture assistants
  • โ€”Livestock advisory
  • โ€”Veterinary Q&A
  • โ€”Dairy farming support
  • โ€”Educational applications
  • โ€”AI-powered agricultural chatbots

Future Roadmap

  • โ€”โœ… Agriculture instruction tuning
  • โ€”๐Ÿ”„ Hindi fine-tuning
  • โ€”๐Ÿ”„ Hinglish support
  • โ€”๐Ÿ”„ Multilingual agriculture assistant
  • โ€”๐Ÿ”„ Image-based crop disease diagnosis
  • โ€”๐Ÿ”„ Livestock image analysis
  • โ€”๐Ÿ”„ RAG with government agriculture and veterinary knowledge