Sriram-Gov/Sarcastic-Headline-Llama2: Sarcasm Generation Model

This model, developed by Sriram Govardhanam, is a fine-tuned Llama 2 (7B parameters) designed to generate sarcastic and satirical news headlines. Unlike other sarcasm datasets, this model was trained on a unique dataset where a larger Llama 2 13B model generated sarcastic versions of real news headlines, ensuring a direct mapping from normal to sarcastic text.

Key Capabilities

• Sarcastic Headline Generation: Transforms standard news headlines into humorous, witty, and often "disrespectful" sarcastic versions.
• Custom Dataset Training: Utilizes a proprietary dataset generated by an LLM, focusing on news headlines for diverse and grammatically sound input.
• PEFT Fine-tuning: Employs Parameter-Efficient Fine-Tuning (PEFT) on the Llama 2 7B base model.

Good For

• Enhanced Natural Language Understanding: Can provide more contextually relevant and engaging responses in applications like chatbots or virtual assistants by understanding and generating sarcasm.
• Niche Content Generation: Supports creative writing and content creation for platforms requiring humor and sarcasm, such as satirical news websites (e.g., The Onion-style content).
• Brand Persona & Social Media: Assists brands and influencers in maintaining a humorous or sarcastic tone in marketing campaigns and social media interactions to boost engagement.

Limitations

• The training dataset size is relatively small (2100 examples), and the model was trained for only 8 epochs due to GPU memory constraints, indicating room for improvement with more data and compute.
• The model's output can be "brutal/humiliating," which might be considered offensive by some users, highlighting the double-edged nature of its specialized sarcastic behavior.