Instructions to use SM0rc/mathleaks_desc_to_code_llama70bl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SM0rc/mathleaks_desc_to_code_llama70bl with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-70b-chat-hf") model = PeftModel.from_pretrained(base_model, "SM0rc/mathleaks_desc_to_code_llama70bl") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from SM0rc/mathleaks_desc_to_code_llama70bl: direct link, hf CLI and curl.
- Browser
- Download file 1.05 GB
-
https://huggingface.co/SM0rc/mathleaks_desc_to_code_llama70bl/resolve/main/adapter_model.bin
- Command line
-
hf download hf://SM0rc/mathleaks_desc_to_code_llama70bl/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/SM0rc/mathleaks_desc_to_code_llama70bl/resolve/main/adapter_model.bin
1.05 GB
- Xet hash:
- 98a6a169e471a8ffb75829522ea8c78891e8f9b2994e4bd0c1a535c9a181ec14
- Size of remote file:
- 1.05 GB
- SHA256:
- f903b2223b675304c4801b5956e3db9820a493d9e578837177ff0ad5bcd68b62
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.