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 training_args.bin from CMU-AIR2/code-lora-simple: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/CMU-AIR2/code-lora-simple/resolve/main/training_args.bin
- Command line
-
hf download hf://CMU-AIR2/code-lora-simple/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/CMU-AIR2/code-lora-simple/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- d81731ac1912c88eb4fce5c9d5bde2829e46f219722a4327eb943c5cac46890d
- Size of remote file:
- 4.92 kB
- SHA256:
- 4553ff79189783bcb04f145ae84bf364a5c686fb299ad724558db7cf34063fc1
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