Instructions to use quannh197/9321fefe-853f-483f-826c-4d079fcd3151 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use quannh197/9321fefe-853f-483f-826c-4d079fcd3151 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct") model = PeftModel.from_pretrained(base_model, "quannh197/9321fefe-853f-483f-826c-4d079fcd3151") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from quannh197/9321fefe-853f-483f-826c-4d079fcd3151: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/quannh197/9321fefe-853f-483f-826c-4d079fcd3151/resolve/main/training_args.bin
- Command line
-
hf download hf://quannh197/9321fefe-853f-483f-826c-4d079fcd3151/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/quannh197/9321fefe-853f-483f-826c-4d079fcd3151/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 9f2381f96ddb75bb0c68ee4adc298d2799325f50db0fb356bac5f6324ac656a3
- Size of remote file:
- 6.78 kB
- SHA256:
- 16bca4fa3630fe3662f9fb6d7715157c189975362959fd675854feabb9e4fa83
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