Instructions to use shibajustfor/26f86435-80d4-4b02-8a0b-3ac4a0a12e77 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shibajustfor/26f86435-80d4-4b02-8a0b-3ac4a0a12e77 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Yarn-Llama-2-13b-128k") model = PeftModel.from_pretrained(base_model, "shibajustfor/26f86435-80d4-4b02-8a0b-3ac4a0a12e77") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from shibajustfor/26f86435-80d4-4b02-8a0b-3ac4a0a12e77: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/shibajustfor/26f86435-80d4-4b02-8a0b-3ac4a0a12e77/resolve/main/training_args.bin
- Command line
-
hf download hf://shibajustfor/26f86435-80d4-4b02-8a0b-3ac4a0a12e77/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/shibajustfor/26f86435-80d4-4b02-8a0b-3ac4a0a12e77/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- e52338cb86d0e57f3c0ff041bc85fce05f70c8d9074c787e7b8270d7bb90a085
- Size of remote file:
- 6.78 kB
- SHA256:
- 1ca255d8b270b0d1712fee1a4142ac5c9820c3abf7ce428bcbb0e8ca5243db43
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