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 last-checkpoint/tokenizer.json from quannh197/9321fefe-853f-483f-826c-4d079fcd3151: direct link, hf CLI and curl.
- Browser
- Download file 17.2 MB
-
https://huggingface.co/quannh197/9321fefe-853f-483f-826c-4d079fcd3151/resolve/main/last-checkpoint/tokenizer.json
- Command line
-
hf download hf://quannh197/9321fefe-853f-483f-826c-4d079fcd3151/last-checkpoint/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/quannh197/9321fefe-853f-483f-826c-4d079fcd3151/resolve/main/last-checkpoint/tokenizer.json
17.2 MB
- Xet hash:
- f50a225d9f53b6d10de736023ad24fa9ce92634801004eb39d881f415a4784cb
- Size of remote file:
- 17.2 MB
- SHA256:
- 3c5cf44023714fb39b05e71e425f8d7b92805ff73f7988b083b8c87f0bf87393
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