Instructions to use HamadaMayu/qwen3-4b-structured-output-lora-v4-CoT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use HamadaMayu/qwen3-4b-structured-output-lora-v4-CoT 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/qwen3-4b-structured-output-lora-v4-CoT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d5269ce6f1ce435538302d082d7b60698b473c7905b719309838c2836aec0dc9
- Size of remote file:
- 529 MB
- SHA256:
- f21ccff7cbc4bb5f919a7eb11db6c6459b2e455f0865d62752b560afcc3d866c
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