Instructions to use shibajustfor/f08871d5-ea5c-4a83-bdea-f80c95ddeec4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shibajustfor/f08871d5-ea5c-4a83-bdea-f80c95ddeec4 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Meta-Llama-3.1-8B") model = PeftModel.from_pretrained(base_model, "shibajustfor/f08871d5-ea5c-4a83-bdea-f80c95ddeec4") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from shibajustfor/f08871d5-ea5c-4a83-bdea-f80c95ddeec4: direct link, hf CLI and curl.
- Browser
- Download file 336 MB
-
https://huggingface.co/shibajustfor/f08871d5-ea5c-4a83-bdea-f80c95ddeec4/resolve/main/adapter_model.bin
- Command line
-
hf download hf://shibajustfor/f08871d5-ea5c-4a83-bdea-f80c95ddeec4/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/shibajustfor/f08871d5-ea5c-4a83-bdea-f80c95ddeec4/resolve/main/adapter_model.bin
336 MB
- Xet hash:
- 411b2ea62a0c726e43652974ba8bc75bfc21c0bf44aa55922cdde7217f3b8843
- Size of remote file:
- 336 MB
- SHA256:
- 6053372475540ca9da50e3c9e88df73a3e3172f4fd31217cb1d9a42045c9b4f3
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