Instructions to use cimol/85d5500f-577e-4c16-be0b-9b41451bd06e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cimol/85d5500f-577e-4c16-be0b-9b41451bd06e with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b") model = PeftModel.from_pretrained(base_model, "cimol/85d5500f-577e-4c16-be0b-9b41451bd06e") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from cimol/85d5500f-577e-4c16-be0b-9b41451bd06e: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/cimol/85d5500f-577e-4c16-be0b-9b41451bd06e/resolve/main/training_args.bin
- Command line
-
hf download hf://cimol/85d5500f-577e-4c16-be0b-9b41451bd06e/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cimol/85d5500f-577e-4c16-be0b-9b41451bd06e/resolve/main/training_args.bin
6.84 kB
- Xet hash:
- 82c25e6120a43a1e30269c01340dc7305b8931be4f413ce51798750eecf2dedc
- Size of remote file:
- 6.84 kB
- SHA256:
- f7d6293a8e5bb63f9cfecea6d40e62008077d93e9f40a150b7ac5c1809504309
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.