Instructions to use fats-fme/927ae2d2-3cd2-4120-b464-724bcc47d45b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use fats-fme/927ae2d2-3cd2-4120-b464-724bcc47d45b 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, "fats-fme/927ae2d2-3cd2-4120-b464-724bcc47d45b") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/optimizer.pt from fats-fme/927ae2d2-3cd2-4120-b464-724bcc47d45b: direct link, hf CLI and curl.
- Browser
- Download file 501 MB
-
https://huggingface.co/fats-fme/927ae2d2-3cd2-4120-b464-724bcc47d45b/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://fats-fme/927ae2d2-3cd2-4120-b464-724bcc47d45b/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/fats-fme/927ae2d2-3cd2-4120-b464-724bcc47d45b/resolve/main/last-checkpoint/optimizer.pt
501 MB
- Xet hash:
- ea775e17562ed1c28eeee4da1c9d13470068572f2ff5e1cb4ff009cedd7cdf2f
- Size of remote file:
- 501 MB
- SHA256:
- 556d16bb6aae79c6f06e3f2b46b8e0433216e37e93f5a8a74d9897d77f66f311
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