Instructions to use eeeebbb2/a6525279-79c1-4568-9071-4ce869a9df4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use eeeebbb2/a6525279-79c1-4568-9071-4ce869a9df4b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Hermes-2-Pro-Mistral-7B") model = PeftModel.from_pretrained(base_model, "eeeebbb2/a6525279-79c1-4568-9071-4ce869a9df4b") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/rng_state_2.pth from eeeebbb2/a6525279-79c1-4568-9071-4ce869a9df4b: direct link, hf CLI and curl.
- Browser
- Download file 15 kB
-
https://huggingface.co/eeeebbb2/a6525279-79c1-4568-9071-4ce869a9df4b/resolve/main/last-checkpoint/rng_state_2.pth
- Command line
-
hf download hf://eeeebbb2/a6525279-79c1-4568-9071-4ce869a9df4b/last-checkpoint/rng_state_2.pth
-
curl -L -o rng_state_2.pth https://huggingface.co/eeeebbb2/a6525279-79c1-4568-9071-4ce869a9df4b/resolve/main/last-checkpoint/rng_state_2.pth
15 kB
- Xet hash:
- 1793108ff09aee534e044d8fb6d21a016a418ce068e0e4f443e58b892685c513
- Size of remote file:
- 15 kB
- SHA256:
- 6a48ed6df648f1c60705a288541493f3ba730cd4b851d9879098fb8b0dbcbf22
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