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:
- c2aa3c62948cd62a81320985b08f375657322a286dfafc269cd4925cab7e311d
- Size of remote file:
- 349 MB
- SHA256:
- e949ee36800f429ba5dc02b761aa54bf8037af6d406fac9e5a04c7b68cce4a12
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