| base_model: "Qwen/Qwen2-VL-7B-Instruct" | |
| library_name: peft | |
| model_name: "AgriAssist" | |
| tags: | |
| - vision-language | |
| - agriculture | |
| - crop-detection | |
| - pest-detection | |
| - weed-detection | |
| - fine-tuning | |
| - sft | |
| - lora | |
| - transformers | |
| license: apache-2.0 | |
| pipeline_tag: text-generation | |
| # AgriAssist: Domain-Specific Vision-Language Model for Indian Agriculture | |
| AgriAssist is a fine-tuned vision-language model (VLM) built on **Qwen2-VL-7B-Instruct**, designed specifically for Indian agricultural applications. It is trained on curated datasets covering major **crops, weeds, pests, and diseases**, enabling robust recognition and basic reasoning over agricultural images. | |
| ## Features | |
| - Domain-specific fine-tuning for Indian agriculture | |
| - Recognition of crops, weeds, and pests | |
| - Instruction-tuned for multimodal reasoning | |
| - Trained on multiple public datasets: MH-Weed16, PlantVillage, AgroBench | |
| - Ready for integration in applications requiring agricultural image understanding | |
| ## Usage | |
| ```python | |
| from transformers import AutoProcessor, AutoModelForCausalLM | |
| processor = AutoProcessor.from_pretrained("your-username/AgriAssist") | |
| model = AutoModelForCausalLM.from_pretrained("your-username/AgriAssist") | |
| # Example usage | |
| inputs = processor(images=image_list, text="Identify the pest in the image", return_tensors="pt") | |
| outputs = model.generate(**inputs) | |