Instructions to use cimol/daae6fca-7305-4b21-9b88-210da89e28b3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cimol/daae6fca-7305-4b21-9b88-210da89e28b3 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct") model = PeftModel.from_pretrained(base_model, "cimol/daae6fca-7305-4b21-9b88-210da89e28b3") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/training_args.bin from cimol/daae6fca-7305-4b21-9b88-210da89e28b3: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/cimol/daae6fca-7305-4b21-9b88-210da89e28b3/resolve/c112cfd726bce2b43734b34e91f8d20a8ac5c993/last-checkpoint/training_args.bin
- Command line
-
hf download hf://cimol/daae6fca-7305-4b21-9b88-210da89e28b3@c112cfd726bce2b43734b34e91f8d20a8ac5c993/last-checkpoint/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cimol/daae6fca-7305-4b21-9b88-210da89e28b3/resolve/c112cfd726bce2b43734b34e91f8d20a8ac5c993/last-checkpoint/training_args.bin
6.84 kB
- Xet hash:
- b00b3c4485ecd3b8d33cf68afc7cfe49217d7a2ba0eb8fda30a7ffa5157a1d87
- Size of remote file:
- 6.84 kB
- SHA256:
- 9bc92f04dccc59aa588eee43cbd3c816ae7304e26a3456c3f35e37266f80456e
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