Instructions to use shibajustfor/a620d886-b805-48bd-8615-2a268f332be8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shibajustfor/a620d886-b805-48bd-8615-2a268f332be8 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/CodeLlama-7b-hf-flash") model = PeftModel.from_pretrained(base_model, "shibajustfor/a620d886-b805-48bd-8615-2a268f332be8") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from shibajustfor/a620d886-b805-48bd-8615-2a268f332be8: direct link, hf CLI and curl.
- Browser
- Download file 80.1 MB
-
https://huggingface.co/shibajustfor/a620d886-b805-48bd-8615-2a268f332be8/resolve/main/adapter_model.bin
- Command line
-
hf download hf://shibajustfor/a620d886-b805-48bd-8615-2a268f332be8/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/shibajustfor/a620d886-b805-48bd-8615-2a268f332be8/resolve/main/adapter_model.bin
80.1 MB
- Xet hash:
- 673732ab3bb3199910ad20c98d8a667d5ce8094b5099574b39a5bc64bd9f3cee
- Size of remote file:
- 80.1 MB
- SHA256:
- d669b5954655c1e57fb3fc3fc022dd34fdcac08a7445b3655f0e09dfff39afdc
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