Finetuned from model: unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit
This Qwen2 model was trained 2x faster using Unsloth and Hugging Face's TRL library. 🌟
Finetuning Details 🌿
This model has been finetuned for text classification. Specifically, it was trained on a rich set of prompts along with their complexity labels. It is designed to assist LLM routing algorithms in selecting the most suitable agent framework (e.g., ReAct, etc.) for a given prompt. 🌱
Average inference time: 0.38 seconds per batch of 10 data points ⚡ — extremely fast for routing tasks.
Optimized for growth and success in multi-agent LLM systems 📈🏆
This model is ideal for scenarios where prompt complexity classification is required to efficiently optimize multi-agent LLM frameworks. 🚀
