Instructions to use shibajustfor/712e19a3-bcea-41ce-b45a-f4ec75414de3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shibajustfor/712e19a3-bcea-41ce-b45a-f4ec75414de3 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0") model = PeftModel.from_pretrained(base_model, "shibajustfor/712e19a3-bcea-41ce-b45a-f4ec75414de3") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from shibajustfor/712e19a3-bcea-41ce-b45a-f4ec75414de3: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/shibajustfor/712e19a3-bcea-41ce-b45a-f4ec75414de3/resolve/main/training_args.bin
- Command line
-
hf download hf://shibajustfor/712e19a3-bcea-41ce-b45a-f4ec75414de3/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/shibajustfor/712e19a3-bcea-41ce-b45a-f4ec75414de3/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 788e57106735569eab56e4a3b7c13b1df442c49964667ac219f4c843ffc0f8e6
- Size of remote file:
- 6.78 kB
- SHA256:
- d51b31fb682d3376bc48818a4b9b6a4b551f27c767642a9922761d7f46ae91ef
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