Instructions to use k1h0/codellama-7b-ptuning-java with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use k1h0/codellama-7b-ptuning-java with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("codellama/CodeLlama-7b-hf") model = PeftModel.from_pretrained(base_model, "k1h0/codellama-7b-ptuning-java") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4d0fb4325c3439e92ce82aea055618cbe12c64a0ace39cb106f0a6eafdfe6afc
- Size of remote file:
- 164 kB
- SHA256:
- 00f37f77293921179ae12a6d2feaf1d2b101fe8e7809ef4f7fcfcff5c1dcc3ed
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.