Instructions to use dada22231/93902ce4-614c-4d1d-95c0-ae13dc4f6f7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dada22231/93902ce4-614c-4d1d-95c0-ae13dc4f6f7b 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, "dada22231/93902ce4-614c-4d1d-95c0-ae13dc4f6f7b") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from dada22231/93902ce4-614c-4d1d-95c0-ae13dc4f6f7b: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/dada22231/93902ce4-614c-4d1d-95c0-ae13dc4f6f7b/resolve/main/training_args.bin
- Command line
-
hf download hf://dada22231/93902ce4-614c-4d1d-95c0-ae13dc4f6f7b/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dada22231/93902ce4-614c-4d1d-95c0-ae13dc4f6f7b/resolve/main/training_args.bin
6.84 kB
- Xet hash:
- 65775fce6c3297d305643aad99344e2fdfec0d6606e8485fffb672333f28799f
- Size of remote file:
- 6.84 kB
- SHA256:
- 5780c4a5991c2de709b19b42f9042a5400af4e6d4d562b7c04f5e99b47dbf131
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