Instructions to use ngocquangt2k46/90d10116-9f09-4362-b32c-19aeff53de18 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ngocquangt2k46/90d10116-9f09-4362-b32c-19aeff53de18 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Yarn-Llama-2-7b-64k") model = PeftModel.from_pretrained(base_model, "ngocquangt2k46/90d10116-9f09-4362-b32c-19aeff53de18") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ngocquangt2k46/90d10116-9f09-4362-b32c-19aeff53de18: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/ngocquangt2k46/90d10116-9f09-4362-b32c-19aeff53de18/resolve/main/training_args.bin
- Command line
-
hf download hf://ngocquangt2k46/90d10116-9f09-4362-b32c-19aeff53de18/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ngocquangt2k46/90d10116-9f09-4362-b32c-19aeff53de18/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 0d0464fb2dcf6e14f36fb78905b717b34114212e3f7d0d1acc5725e4203b85e3
- Size of remote file:
- 6.78 kB
- SHA256:
- 91c2c9f1cbe0b0e18801ab04f7de6f8426b0cdc981de5b9507570654cf1c5af5
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