Instructions to use predibase/magicoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use predibase/magicoder with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1") model = PeftModel.from_pretrained(base_model, "predibase/magicoder") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.safetensors from predibase/magicoder: direct link, hf CLI and curl.
- Browser
- Download file 13.6 MB
-
https://huggingface.co/predibase/magicoder/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://predibase/magicoder/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/predibase/magicoder/resolve/main/adapter_model.safetensors
13.6 MB
- Xet hash:
- 83db5658f2edef39f4b27d1309d10599091ef1c09b6bd97b253bdb95a12dc672
- Size of remote file:
- 13.6 MB
- SHA256:
- 8a87c71ab037eabcf65dc7d79f4dd909ebf0d086dd0fd21ca27b959424a4a233
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.