Instructions to use HamadaMayu/unsloth_qwen3-4b-structured-output-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use HamadaMayu/unsloth_qwen3-4b-structured-output-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "HamadaMayu/unsloth_qwen3-4b-structured-output-lora") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2f1e3441f7599e250381d578b6074779ded6f907e9f4d9727057774c9222b67e
- Size of remote file:
- 529 MB
- SHA256:
- f65acd27f015e094a6dfb8d47ecf920d3480f059be9e5ecb2aa4f64dfd2aeb9e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.