Instructions to use prodigyhuh/atomicvision-hard-recall-micro-boost-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use prodigyhuh/atomicvision-hard-recall-micro-boost-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B") model = PeftModel.from_pretrained(base_model, "prodigyhuh/atomicvision-hard-recall-micro-boost-lora") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0d2a1257631695d6a509881732856fc41002b2f14d1bbff3df3d6452935d8ebb
- Size of remote file:
- 69.8 MB
- SHA256:
- 745af6dec82baa429af53c2feba7c5832ef2f990bcc8d97f680822e5ea33f110
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.