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 adapter_model.bin from ngocquangt2k46/90d10116-9f09-4362-b32c-19aeff53de18: direct link, hf CLI and curl.
- Browser
- Download file 80.1 MB
-
https://huggingface.co/ngocquangt2k46/90d10116-9f09-4362-b32c-19aeff53de18/resolve/main/adapter_model.bin
- Command line
-
hf download hf://ngocquangt2k46/90d10116-9f09-4362-b32c-19aeff53de18/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/ngocquangt2k46/90d10116-9f09-4362-b32c-19aeff53de18/resolve/main/adapter_model.bin
80.1 MB
- Xet hash:
- 685f36b3e06ca80e3b7cbebffe93f12fc3eb1cedbd183c9ff9556757fa02f43d
- Size of remote file:
- 80.1 MB
- SHA256:
- c7a5dae96d7491d08cf9f78a3ceca2d2773687b31cf25517a374a4377f8f3eb5
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