Instructions to use tcarwash/tinyllama-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use tcarwash/tinyllama-instruct with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("PY007/TinyLlama-1.1B-step-50K-105b") model = PeftModel.from_pretrained(base_model, "tcarwash/tinyllama-instruct") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 354f708d505d8e1d04e065729af480a6f23661dc2b6af29777f6d018e9ebfa89
- Size of remote file:
- 4.4 GB
- SHA256:
- f8e7b7be14721e8c30b7d27be16aa37715b6b476e2884efee6c2f4258208744c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.