Instructions to use Deepamparmar/Bloom3B-LORA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Deepamparmar/Bloom3B-LORA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigscience/bloom-3b") model = PeftModel.from_pretrained(base_model, "Deepamparmar/Bloom3B-LORA") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ead3d82b9194f8051cbe0c148d8c1938d816e12b11bc80964e2cdcd20416937a
- Size of remote file:
- 9.85 MB
- SHA256:
- 8e5dcef38e77c4c3e3187d54fdbe1b1b0da713d96b3ab6d45a3e73d3c3c3c045
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.