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:
- 7f25f85cd18e92698a69154d20678bf17a91e0d00327e2443fcfdbb9809e454b
- Size of remote file:
- 436 MB
- SHA256:
- d7af61e94373c6b0c073f522d7d56cda72e3f74c86d808147f0e3a140bac801d
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