Instructions to use Krisbiantoro/llama3_orpo_llmbotika with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Krisbiantoro/llama3_orpo_llmbotika with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct") model = PeftModel.from_pretrained(base_model, "Krisbiantoro/llama3_orpo_llmbotika") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.safetensors from Krisbiantoro/llama3_orpo_llmbotika: direct link, hf CLI and curl.
- Browser
- Download file 168 MB
-
https://huggingface.co/Krisbiantoro/llama3_orpo_llmbotika/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://Krisbiantoro/llama3_orpo_llmbotika/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/Krisbiantoro/llama3_orpo_llmbotika/resolve/main/adapter_model.safetensors
168 MB
- Xet hash:
- b5c945788fc5ea60a2d571fdf95128b1df043585fc3ee92910ae07575b23de61
- Size of remote file:
- 168 MB
- SHA256:
- dd1619c0a919a67ec700893db3ffe496a983e72ef17b05b723321a8f3f8c735c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.