Instructions to use sn56z0/f92f7a48-1095-43e5-8f53-0f4dc97e400c with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sn56z0/f92f7a48-1095-43e5-8f53-0f4dc97e400c with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("huggyllama/llama-7b") model = PeftModel.from_pretrained(base_model, "sn56z0/f92f7a48-1095-43e5-8f53-0f4dc97e400c") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from sn56z0/f92f7a48-1095-43e5-8f53-0f4dc97e400c: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/sn56z0/f92f7a48-1095-43e5-8f53-0f4dc97e400c/resolve/main/training_args.bin
- Command line
-
hf download hf://sn56z0/f92f7a48-1095-43e5-8f53-0f4dc97e400c/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/sn56z0/f92f7a48-1095-43e5-8f53-0f4dc97e400c/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- d19206bfc31113d2b25cd9bc1fb740940bdd130d65d1daf6bc3a7b61db9f8141
- Size of remote file:
- 6.78 kB
- SHA256:
- e7fba9e3f7c24ec285d658acf35c6a5eecbf735ed435ee87105a39635715736c
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