debanganghosh/primus-fined-tuned-llm

🧠 Fine-Tuned Argumentation Model

🔥 Fine-Tuned LLM for Computational Argumentation using Quantitative Bipolar Argumentation Framework (QBAF)!

✅ Enhances LLM outputs using argumentation logic & belief theory
✅ Evaluates claims based on supporting & opposing arguments
✅ Useful for AI-based debate systems, legal reasoning & NLP research

🔗 Model Page: https://huggingface.co/debanganghosh/primus-fined-tuned-llm

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📦 Installation

To use this model, install the required dependencies:

pip install transformers torch huggingface_hub

If you're using a GPU for acceleration, install CUDA for PyTorch:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

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🛠️ How to Use

You can use this model for argument evaluation and text-based reasoning in Python.

🔹 Load the Model

from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_NAME = "YOUR_USERNAME/YOUR_MODEL_NAME"

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)

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📝 Generate Arguments

prompt = "Should AI be regulated for ethical reasons?"
inputs = tokenizer(prompt, return_tensors="pt")

# Generate response
output = model.generate(**inputs, max_new_tokens=150)

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

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💡 Suggested Prompts

Here are some example prompts you can try:

📌 "Does climate change require immediate policy intervention?"
📌 "Should cryptocurrencies be regulated by governments?"
📌 "Is remote work better than office work?"
📌 "Can AI replace human decision-making in law?"
📌 "Should social media platforms be held accountable for misinformation?"
📌 "Are electric vehicles truly better for the environment?"
📌 "Does universal healthcare improve quality of life?"
📌 "Should students be allowed to use AI for academic purposes?"
📌 "Is space exploration worth the investment?"
📌 "Can democracy survive in the digital age?"

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🚀 Uploading & Sharing the Model

If you want to fine-tune and push updates to the model, use:

from huggingface_hub import notebook_login
notebook_login()

model.push_to_hub("YOUR_USERNAME/YOUR_MODEL_NAME")
tokenizer.push_to_hub("YOUR_USERNAME/YOUR_MODEL_NAME")

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📝 License & Contribution

📜 License: MIT
🤝 Contributions: Feel free to fork and improve this model!

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✨ Enjoy using this fine-tuned argumentation model! 🚀
Let me know if you need any modifications! 🎯