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Venkatrajan247/citizen-science-monitoring-dpo-trained

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🚀 Citizen Science Monitoring - Fine-tuned Model

![License: CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

📌 Model Overview

This is a fine-tuned Mistral-based language model designed for citizen science monitoring.

—Base Model: Mistral
—Fine-tuned On: Custom dataset related to security and monitoring
—License: CC BY 4.0

🔧 How to Use the Model

1️⃣ Install Dependencies

bash

pip install transformers accelerate bitsandbytes torch

2️⃣ Load the Model & Tokenizer

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch

model_name = "Venkatrajan247/citizen-science-monitoring-dpo-trained"

# Enable 4-bit quantization for efficiency
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True,
)

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=quantization_config,
    device_map="auto",
)

# Generate a response
input_prompt = "How should invasive species be mapped?"
inputs = tokenizer(input_prompt, return_tensors="pt").to("cuda")
output = model.generate(**inputs, max_new_tokens=100)

print(tokenizer.decode(output[0], skip_special_tokens=True))

📜 License
This model is licensed under CC BY 4.0.
For more details, refer to: CC BY 4.0 License

💡 Citation
If you use this model, please cite:

@misc{citizen-science-monitoring-2024,
  author = {Venkatrajan247},
  title = {Citizen Science Monitoring - Fine-tuned Model},
  year = {2025},
  url = {https://huggingface.co/Venkatrajan247/citizen-science-monitoring-dpo-trained},
  license = {CC-BY-4.0}
}


📬 Contact
For any issues or questions, feel free to reach out! 🚀
Name : Venkatrajan N
Email : venkatrajan2407@gmail.com