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:
- 79af33ccb39524c1e7f2477dd9954202039046e592a20b74b736c61407530701
- Size of remote file:
- 4.79 kB
- SHA256:
- b239d7a844225bee5fb12f6b2f19e82848d14f32e63cfbe5cb7e67644ddeba5c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.