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:
- 5de9bf8a7506e9f2c8b190979b43f60932011f7f226962eea31cfee2a094233d
- Size of remote file:
- 11.4 MB
- SHA256:
- 9e2b7649a086fbb9771cd78ec5b7ffa7069e16a82424a382950fedfa1e057861
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