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:
- 47a9678dba43d9feba6041c10ea4a974ba94fe77be3f5fd1bb48ebb2ac5906c9
- Size of remote file:
- 48.4 MB
- SHA256:
- c108ed44479c838d33da81cfbfc7bbd272d1b1782970cf558a1325b722fda959
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