--- language: - en tags: - agriculture - farming - qa - lora - peft - qwen license: mit datasets: - shchoi83/agriQA base_model: Qwen/Qwen1.5-1.8B-Chat --- # 🌾 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 ```python 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 ```python 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. --- **Built with ❤️ for the agricultural community**