Instructions to use quannh197/8e0fd235-c40a-4533-b8a4-14cc3cb82a5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use quannh197/8e0fd235-c40a-4533-b8a4-14cc3cb82a5b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Llama-3.2-3B") model = PeftModel.from_pretrained(base_model, "quannh197/8e0fd235-c40a-4533-b8a4-14cc3cb82a5b") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from quannh197/8e0fd235-c40a-4533-b8a4-14cc3cb82a5b: direct link, hf CLI and curl.
- Browser
- Download file 48.8 MB
-
https://huggingface.co/quannh197/8e0fd235-c40a-4533-b8a4-14cc3cb82a5b/resolve/main/adapter_model.bin
- Command line
-
hf download hf://quannh197/8e0fd235-c40a-4533-b8a4-14cc3cb82a5b/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/quannh197/8e0fd235-c40a-4533-b8a4-14cc3cb82a5b/resolve/main/adapter_model.bin
48.8 MB
- Xet hash:
- 3980d82fa5aac3d8f9580bbd16e69c5ae237f3c1e3fb587d3495f08ecae3552d
- Size of remote file:
- 48.8 MB
- SHA256:
- 5cbe561f92f96891293a399994b0e68e893cd821b9dd6df5f80110ed99f3a91d
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