Text Generation
PEFT
Safetensors
English
qlora
lora
fine-tuning
reasoning
qwen2.5
openthoughts
4-bit precision
nf4
conversational
Instructions to use rahmasaber/qwen2.5-iq-Finetuning-qlora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use rahmasaber/qwen2.5-iq-Finetuning-qlora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct") model = PeftModel.from_pretrained(base_model, "rahmasaber/qwen2.5-iq-Finetuning-qlora") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eaaf4a63f4ca80098fd9b5736251816816df0288aa599779d4d7851a99e13c86
- Size of remote file:
- 73.9 MB
- SHA256:
- 744620c49b221f9a3c541c559c96efbd5f1f8b7365f4a1ee13880056f34a2bd9
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