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 last-checkpoint/optimizer.pt from dimasik87/4e28cde6-8ba7-4064-8ab0-0b6c6ee61037: direct link, hf CLI and curl.
- Browser
- Download file 501 MB
-
https://huggingface.co/dimasik87/4e28cde6-8ba7-4064-8ab0-0b6c6ee61037/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://dimasik87/4e28cde6-8ba7-4064-8ab0-0b6c6ee61037/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dimasik87/4e28cde6-8ba7-4064-8ab0-0b6c6ee61037/resolve/main/last-checkpoint/optimizer.pt
501 MB
- Xet hash:
- 8f2c67a9bbe97ed1a6c837c6c88eb90c0040d2fd1b69c26eff839104ada0b0dc
- Size of remote file:
- 501 MB
- SHA256:
- 1bff5306f475fabd1d421bfed71a99e54922eafc437d92d8cce62fb1ef2106e8
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