Text Generation
PEFT
Safetensors
English
qlora
lora
intent-classification
banking77
aegis
conversational
Instructions to use MCMZ10/Qwen2.5-7B-Banking77-Router-QLoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use MCMZ10/Qwen2.5-7B-Banking77-Router-QLoRA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/root/ml/Qwen2.5-7B-Instruct") model = PeftModel.from_pretrained(base_model, "MCMZ10/Qwen2.5-7B-Banking77-Router-QLoRA") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4d272cd0ce1484bc7fcd4ec85b3778d1781fac6cd2c0d0bc6ef8917c193e4906
- Size of remote file:
- 11.4 MB
- SHA256:
- 145191e3b102f64e84dab1926006ee71528c1ea536df048ef9ff6c48d01f95de
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