Instructions to use CMU-AIR2/code-lora-hard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use CMU-AIR2/code-lora-hard 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-hard") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c849e37824358fc65d10b1d062bf9b1cdf4cbb149fd9ff3c834f0ea5b227a69c
- Size of remote file:
- 4.92 kB
- SHA256:
- 8ad1f0b3a14f0a9c7c3a6415a20056a32092142b44c46b72f6a3a1e5be0f8db8
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