Instructions to use CMU-AIR2/code-lora-hard-gh-mixture with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use CMU-AIR2/code-lora-hard-gh-mixture 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-gh-mixture") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from CMU-AIR2/code-lora-hard-gh-mixture: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/CMU-AIR2/code-lora-hard-gh-mixture/resolve/main/training_args.bin
- Command line
-
hf download hf://CMU-AIR2/code-lora-hard-gh-mixture/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/CMU-AIR2/code-lora-hard-gh-mixture/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- df62bb2e444c6c41664abab276f912eb8a9b014f39fb2b5bae3cfc9aee08f109
- Size of remote file:
- 4.92 kB
- SHA256:
- 1318554bac5c9740d383c017d6e24e057c161337444188e13c83dba33fc00e24
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