Instructions to use besimray/miner1_ba9938bd-5490-4a39-90ab-976131f92334_1730940656 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use besimray/miner1_ba9938bd-5490-4a39-90ab-976131f92334_1730940656 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Llama-3.2-1B-Instruct") model = PeftModel.from_pretrained(base_model, "besimray/miner1_ba9938bd-5490-4a39-90ab-976131f92334_1730940656") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ed21728072d244408e259bfbb4103e36a8b444047d59f9575bbd7073cea91bae
- Size of remote file:
- 90.2 MB
- SHA256:
- 171112ccba9344c686ea185bbd6a2296276e47237baba76d8ff8ead4dd654fbf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.