Instructions to use dipikakhullar/olmo-code-python2-3-tagged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dipikakhullar/olmo-code-python2-3-tagged with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("allenai/OLMo-1B-hf") model = PeftModel.from_pretrained(base_model, "dipikakhullar/olmo-code-python2-3-tagged") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 003581aa1b181302876e4c070b6db088f3b73d7ecf90d4beaa2255a7601eb892
- Size of remote file:
- 1.47 kB
- SHA256:
- 44c2de80e956cfc3ea5d04d4e370b19d0919c77a14fcf0a9f825c7630b85c85d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.