Instructions to use radius27/llama-7b-code-feedback with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use radius27/llama-7b-code-feedback with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf") model = PeftModel.from_pretrained(base_model, "radius27/llama-7b-code-feedback") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3f00dcd7a1a32360d7487ee0b17d63c8a71b9358196e3fdde1f31d11aa6f13bb
- Size of remote file:
- 1.8 GB
- SHA256:
- f68df80e9d6bc82e4a1b641aee6d962956f3c7d1d5101a2319e2c8b28118abb8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.