Instructions to use kbrdek37/llama38binstruct_summarize with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kbrdek37/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, "kbrdek37/llama38binstruct_summarize") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1e5c8a2673e3ddfd2570c3139d4e006bb0e40e7cc6fc737eb8127f0d3259f5ff
- Size of remote file:
- 5.37 kB
- SHA256:
- f37cd9f3a2f0788d7620efd534358d70753e83dc5e34247b2ecf5e3a4302e81f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.