Instructions to use VenkyPas/llama38binstruct_summarize with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use VenkyPas/llama38binstruct_summarize with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Meta-Llama-3-8B-Instruct") model = PeftModel.from_pretrained(base_model, "VenkyPas/llama38binstruct_summarize") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5496dd9aabd53fd5d030062c05c7dec032df8f5dedf106c459d41ca59c1fb263
- Size of remote file:
- 5.37 kB
- SHA256:
- 25f6ebc5ef94febcb75065d708f236a46bfbb4c62e7234e79e415234e27ad19e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.