Instructions to use nikvi/llama38binstruct_summarize with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nikvi/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, "nikvi/llama38binstruct_summarize") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8e1ccd5cf82fed0ee8871e8328cbc90ba7b91d38dc80db2b730765eead04725c
- Size of remote file:
- 5.37 kB
- SHA256:
- fde116ceba1e106571ac336b6c5d68f4a93b98021176411ab78fcc52ce8ba72b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.