Text Generation
PEFT
Safetensors
English
qwen3
lora
qlora
education
mathematics
middle-school
diagnostic-assessment
conversational
Instructions to use j2ampn/qwen3-8b-distractor-lora-v8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use j2ampn/qwen3-8b-distractor-lora-v8 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3-8B-bnb-4bit") model = PeftModel.from_pretrained(base_model, "j2ampn/qwen3-8b-distractor-lora-v8") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 95d6313ccf0b58caabbfa7fecb8e320adc7abe873d69d335637229dc0cb3a383
- Size of remote file:
- 11.4 MB
- SHA256:
- 476870a1f2fb6f6a2759a6ede2383bf9d5d738f17844563b65c91965b722ae09
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.