Instructions to use eeeebbb2/5737beba-a6cf-4751-a823-f1b80f1b9204 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use eeeebbb2/5737beba-a6cf-4751-a823-f1b80f1b9204 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("peft-internal-testing/tiny-dummy-qwen2") model = PeftModel.from_pretrained(base_model, "eeeebbb2/5737beba-a6cf-4751-a823-f1b80f1b9204") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from eeeebbb2/5737beba-a6cf-4751-a823-f1b80f1b9204: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/eeeebbb2/5737beba-a6cf-4751-a823-f1b80f1b9204/resolve/main/training_args.bin
- Command line
-
hf download hf://eeeebbb2/5737beba-a6cf-4751-a823-f1b80f1b9204/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/eeeebbb2/5737beba-a6cf-4751-a823-f1b80f1b9204/resolve/main/training_args.bin
6.84 kB
- Xet hash:
- 716ca531a5c0466e6726ee05134a0961c0b2c5f331ae090a2317a31811d68ffe
- Size of remote file:
- 6.84 kB
- SHA256:
- 240248190013bb23b18210eca6094c36e4fc1e0d90ff9aff82674cc99feda3c8
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