LocalLLaMA

Maybe it's overkill, idk, but if you want higher accuracy, it's an option

You can just list examples from your dataset and let the LLM complete the last one

Example:

# Classify text

(a) advertisement
(b) poetry
(c) information

Ignore real-time Al and customers will do the same to you. Our vector database is AI-ready and proven at scale.
Class: (a)

I find no peace, and all my war is done. I fear and hope. I burn and freeze like ice. I fly above the wind, yet can I not arise
Class: (b)

YOUR BEST COMES OUT OF THE BLUE. EXPLORE BOISE STATE
Class: (a)

Two-month ramp closure: northbound OR 99W onto OR 217 north Starts May 31 of Transportation Oregon Department OR 217 AUXILIARY LANES
Class: (c)

Staying healthy. Staying active. We have it all right here. IN YOUR PRIME LEARN MORE LIVING YOUR BEST LIFE
Class: (a)

Go further, FASTER. Take the world's premier English- proficiency test in less than 2 hours!
Class: (a)

A rhinoceros beetle is a living thing. Rhinoceros beetles grow and respond to their environment. They need food and water.
Class: (c)

Our vice runs beyond all that old men saw, And far authentically above our laws, And scorning virtues safe and golden mean, Sits uncontrolled upon the high extreme.
Class: (b)

{your text here}
Class: ({generate one token}

I don't know about the task you have in mind specifically, but you can do just about anything with a 13B llama model. Picking a fine-tune doesn't matter if you use examples instead of instructions. 7B Mistral seems to do fine with this example (even GPT2 can do some classification), but in-context learning is remarkably better at 13B, picking up a lot more nuance