nada013/agriqa-assistant

🌾 AgriQA Assistant

An intelligent agricultural expert assistant fine-tuned on the agriQA dataset using Qwen1.5-1.8B-Chat with PEFT + LoRA.

🚀 Features

• Clear, practical steps you can apply directly in the field
• Specific measurements and quantities for accurate application
• Safety precautions when needed
• Expert tips for better results
• Structured responses with numbered steps

🔧 Technical Details

• Base Model: Qwen/Qwen1.5-1.8B-Chat
• Fine-tuning Method: PEFT + LoRA (Parameter Efficient Fine-tuning)
• Dataset: agriQA (agricultural Q&A pairs)
• Training Data: 50,000 samples with structured prompts
• LoRA Rank: 2
• LoRA Alpha: 4

📱 Usage

Direct Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-1.8B-Chat", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-1.8B-Chat", trust_remote_code=True)

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "nada013/agriqa-assistant")

Chat Format

messages = [
    {"role": "system", "content": "You are AgriQA, an agricultural expert assistant..."},
    {"role": "user", "content": "How to control aphid infestation in mustard crops?"}
]

# Generate response
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt")
outputs = model.generate(inputs, max_new_tokens=512, temperature=0.3)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

🎯 Response Format

The model provides structured responses:

1. Direct answer to the question
2. Numbered step-by-step solution
3. Specific details (measurements, quantities, product names)
4. Safety precautions if needed
5. Extra tip or follow-up advice

💡 Example Questions

• "How to control aphid infestation in mustard crops?"
• "What fertilizer should I use for coconut plants?"
• "How to increase milk production in cows?"
• "What is the treatment for white diarrhoea in poultry?"
• "How to preserve potato tubers for 7-8 months?"

🔒 Safety Note

Always follow safety guidelines when applying agricultural practices. The assistant provides general advice - consult local agricultural experts for region-specific recommendations.

📊 Training Details

• Epochs: 1
• Learning Rate: 5e-4
• Batch Size: 1 (with gradient accumulation)
• Max Length: 256 tokens
• Optimizer: AdamW with fused implementation
• Hardware: 8GB GPU with 4-bit quantization

🤝 Contributing

This model is trained on the agriQA dataset. For improvements or questions, please refer to the original dataset source.

📄 License

This project uses the Qwen1.5-1.8B-Chat model and agriQA dataset. Please refer to their respective licenses for usage terms.

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Built with ❤️ for the agricultural community