Instructions to use dada22231/886fc06d-0cbf-4895-88c2-c3007506fd82 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dada22231/886fc06d-0cbf-4895-88c2-c3007506fd82 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-128k-instruct") model = PeftModel.from_pretrained(base_model, "dada22231/886fc06d-0cbf-4895-88c2-c3007506fd82") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/training_args.bin from dada22231/886fc06d-0cbf-4895-88c2-c3007506fd82: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/dada22231/886fc06d-0cbf-4895-88c2-c3007506fd82/resolve/main/last-checkpoint/training_args.bin
- Command line
-
hf download hf://dada22231/886fc06d-0cbf-4895-88c2-c3007506fd82/last-checkpoint/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dada22231/886fc06d-0cbf-4895-88c2-c3007506fd82/resolve/main/last-checkpoint/training_args.bin
6.84 kB
- Xet hash:
- e8ac27138fd39b8ff5137fbea731f015eed470fefd0dc68c3224ce12d0878036
- Size of remote file:
- 6.84 kB
- SHA256:
- 26216bd6d51b79d5fc34cf8e79882ef60bac179faa1b469291db325203ee7820
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