Instructions to use monsterapi/llama2_7b_DolphinCoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use monsterapi/llama2_7b_DolphinCoder 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, "monsterapi/llama2_7b_DolphinCoder") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 52bb276edf027c91d79ed10dafef9c77b8aae6653efe8f08e262868ad67ec35d
- Size of remote file:
- 134 MB
- SHA256:
- f13dce186f72ef52db77b348c0dc305f817134a3d9c01135cf5295b5cebac7f0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.