Instructions to use Rakushaking/Qwen4b-SFT-d9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Rakushaking/Qwen4b-SFT-d9 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, "Rakushaking/Qwen4b-SFT-d9") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3e3ce70047ed812f4e9f994257757b1e339678e69e94fa49be7e20dbb89f7880
- Size of remote file:
- 529 MB
- SHA256:
- 4f828e156cd3c8df443ff593f18ef717dbaed2abac3b88ae973bb54f4317862f
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