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
Download scheduler.pt from CMU-AIR2/code-lora-simple: direct link, hf CLI and curl.
- Browser
- Download file 1.06 kB
-
https://huggingface.co/CMU-AIR2/code-lora-simple/resolve/main/scheduler.pt
- Command line
-
hf download hf://CMU-AIR2/code-lora-simple/scheduler.pt
-
curl -L -o scheduler.pt https://huggingface.co/CMU-AIR2/code-lora-simple/resolve/main/scheduler.pt
1.06 kB
- Xet hash:
- 7e85968a35095aff88a8ebfb72aa02da4579e0272b097123998235283a86eea2
- Size of remote file:
- 1.06 kB
- SHA256:
- 630cbfb9743bb7c6479e776b94f77b96bd7d8e338d90fccbd6af6f5871c9dd4b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.