Instructions to use vedkcoder/peft-language-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use vedkcoder/peft-language-code with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf") model = PeftModel.from_pretrained(base_model, "vedkcoder/peft-language-code") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.safetensors from vedkcoder/peft-language-code: direct link, hf CLI and curl.
- Browser
- Download file 67.1 MB
-
https://huggingface.co/vedkcoder/peft-language-code/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://vedkcoder/peft-language-code/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/vedkcoder/peft-language-code/resolve/main/adapter_model.safetensors
67.1 MB
- Xet hash:
- 5f3f17f26150ac7c6426010ec650a0542451842e71518bee82818dae723861d7
- Size of remote file:
- 67.1 MB
- SHA256:
- ba0fdc0b2c301ec01e7a010b3eacaa6f3efb96a78253c97a362c25c74344d426
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.