Instructions to use mickkhaw/llama38binstruct_summarize with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mickkhaw/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, "mickkhaw/llama38binstruct_summarize") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c58699f7233f6c55bdc47cdafdcea72923e188ea782e9c77127c5bb3e014ef65
- Size of remote file:
- 5.37 kB
- SHA256:
- 32c5c08945f20cb1076a969d5a1b798301732ed80cb2a58aa35a316822aa1d59
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.