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
Delete rng_state_2.pth with huggingface_hub
Browse files- rng_state_2.pth +0 -3
rng_state_2.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:f4e481d4ef1546694da7337f6bb6c658b866dcb79b85deeb477da0d27ebe851e
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size 15984
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