Agri-llama: Official Vision-Language Model by Aqib Mehedi
Agri-llama is a powerful multimodal model designed for advanced agricultural analysis, combining visual perception with sophisticated language reasoning. It is optimized for both precision vision tasks and interactive agricultural consultation.
🌟 Key Features
Vision-Language Integration : Analyze agricultural imagery (crops, pests, soil) alongside textual queries.High Performance : Optimized for efficiency and accuracy in specialized domains.Multimodal Chat : Interactive dialogue support for complex agricultural problem-solving.Flexible Deployment : Available in both Hugging Face Safetensors (FP16) and GGUF (Quantized) formats.
💻 System Configuration (Development Environment)
This model was developed and verified on the following configuration:
OS : Microsoft Windows 11 Pro (Build 26200)GPU : NVIDIA GeForce RTX 3060 (12GB VRAM)CUDA : 13.0Driver : 581.29Python : 3.11.9
Recommended Hardware for Inference
GGUF (Quantized) : 8GB+ VRAM (Full GPU offloading) or 16GB+ System RAM (CPU only).Safetensors (FP16) : 12GB+ VRAM (NVIDIA RTX 3060 or better recommended).
🚀 Beginner's Quick Start Guide
Follow these steps to get Agri-llama running on your local machine.
1. Prerequisites
Ensure you have
Python 3.10+ and
Git installed. If you have an NVIDIA GPU, install the
CUDA Toolkit .
2. Setup Environment
Open your terminal (PowerShell or Command Prompt) and run:
1 # Clone the repository
2 git clone https://huggingface.co/aqibcareer007/agri-llama
3 cd agri-llama
4
5 # Create a virtual environment (recommended)
6 python -m venv venv
7 venv \ Scripts \ activate
8
9 # Install requirements
10 pip install -r requirements.txt
3. Running the Model
Option A: Interactive Chat (Easiest for Beginners)
This uses the quantized GGUF model which is fast and memory-efficient.
python scripts/run_chat.pyWait for the 🤖 Model Loaded! message and start typing your agricultural questions.
Option B: Vision Analysis (Python API)
Use this for programmatically analyzing images.
1 import torch
2 from transformers import AutoProcessor , AgriLlamaForConditionalGeneration
3 from PIL import Image
4
5 model_id = "aqibcareer007/agri-llama"
6
7 # Load Model
8 model = AgriLlamaForConditionalGeneration . from_pretrained (
9 model_id ,
10 torch_dtype = torch . float16 ,
11 device_map = "auto"
12 )
13 processor = AutoProcessor . from_pretrained ( model_id )
14
15 # Example: Analyze a crop image
16 image = Image . open ( "path_to_your_crop_image.jpg" )
17 prompt = "<bos><start_of_turn>user\n<image>\nWhat is wrong with this leaf?<end_of_turn>\n<start_of_turn>model\n"
18
19 inputs = processor ( text = prompt , images = image , return_tensors = "pt" ) . to ( "cuda" )
20 output = model . generate ( ** inputs , max_new_tokens = 200 )
21
22 print ( processor . decode ( output [ 0 ] , skip_special_tokens = True ) )
🛠 Technical Specifications
Model Type : Multimodal (Vision + Text)Base Architecture : 4B ParametersContext Window : 131,072 tokensQuantization : GGUF Q4_K_M (Included for efficiency)
📄 License
[Insert License Here - e.g., Apache 2.0]
🤝 Acknowledgments
Developed by Aqib Mehedi , Senior AI Engineer at Kamal-Paterson Ltd .
For support or inquiries, please visit the official Hugging Face repository.
