Instructions to use dimasik87/4e28cde6-8ba7-4064-8ab0-0b6c6ee61037 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dimasik87/4e28cde6-8ba7-4064-8ab0-0b6c6ee61037 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("OpenBuddy/openbuddy-llama2-13b-v8.1-fp16") model = PeftModel.from_pretrained(base_model, "dimasik87/4e28cde6-8ba7-4064-8ab0-0b6c6ee61037") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from dimasik87/4e28cde6-8ba7-4064-8ab0-0b6c6ee61037: direct link, hf CLI and curl.
- Browser
- Download file 568 kB
-
https://huggingface.co/dimasik87/4e28cde6-8ba7-4064-8ab0-0b6c6ee61037/resolve/main/tokenizer.model
- Command line
-
hf download hf://dimasik87/4e28cde6-8ba7-4064-8ab0-0b6c6ee61037/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/dimasik87/4e28cde6-8ba7-4064-8ab0-0b6c6ee61037/resolve/main/tokenizer.model
568 kB
- Xet hash:
- ff6970e94c4c59e458687b36e5877799c1a6bbb2e5b66fff5ded5bbcee3a82a6
- Size of remote file:
- 568 kB
- SHA256:
- f440c53d2cc6f14a7ed7124dea5f5a7402fb4fc95bccb5d8be6d0f7e74d327ed
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.