Instructions to use sallywww/codeLLaMA_oneStep_fuzzTargets with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sallywww/codeLLaMA_oneStep_fuzzTargets with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("codellama/CodeLlama-7b-hf") model = PeftModel.from_pretrained(base_model, "sallywww/codeLLaMA_oneStep_fuzzTargets") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-20/optimizer.pt from sallywww/codeLLaMA_oneStep_fuzzTargets: direct link, hf CLI and curl.
- Browser
- Download file 134 MB
-
https://huggingface.co/sallywww/codeLLaMA_oneStep_fuzzTargets/resolve/main/checkpoint-20/optimizer.pt
- Command line
-
hf download hf://sallywww/codeLLaMA_oneStep_fuzzTargets/checkpoint-20/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/sallywww/codeLLaMA_oneStep_fuzzTargets/resolve/main/checkpoint-20/optimizer.pt
134 MB
- Xet hash:
- 67c472ccc846650481c8e757db491596809eb52f20fa51ea35027da6a784965d
- Size of remote file:
- 134 MB
- SHA256:
- 51b3740dfb324e345a0eea52b52c7c4e483dda810c019fad2bf567e2b9726fff
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.