Instructions to use CMU-AIR2/code-lora-simple with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use CMU-AIR2/code-lora-simple with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-1.3b-instruct") model = PeftModel.from_pretrained(base_model, "CMU-AIR2/code-lora-simple") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- da72d03c5e61b35e62af3d39e73dbc41e718f9a18700b4e9895453ae042a87d2
- Size of remote file:
- 120 MB
- SHA256:
- a4ee0f999b85a3d6d55c6eff3c1f5085106ba7c630b586eb0a280e4c5a7d419b
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