Instructions to use shibajustfor/a761343e-ba96-419e-b12f-d25922b7ca76 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shibajustfor/a761343e-ba96-419e-b12f-d25922b7ca76 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/mistral-7b-instruct-v0.3") model = PeftModel.from_pretrained(base_model, "shibajustfor/a761343e-ba96-419e-b12f-d25922b7ca76") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from shibajustfor/a761343e-ba96-419e-b12f-d25922b7ca76: direct link, hf CLI and curl.
- Browser
- Download file 336 MB
-
https://huggingface.co/shibajustfor/a761343e-ba96-419e-b12f-d25922b7ca76/resolve/main/adapter_model.bin
- Command line
-
hf download hf://shibajustfor/a761343e-ba96-419e-b12f-d25922b7ca76/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/shibajustfor/a761343e-ba96-419e-b12f-d25922b7ca76/resolve/main/adapter_model.bin
336 MB
- Xet hash:
- 1a36bc6d048c10b25b3bf5fc51030bf8127b73ac37872dd7a73605c2e7604f48
- Size of remote file:
- 336 MB
- SHA256:
- 7c3bb08727e7c89388d36210f269cd0da83756f6cc104707ca0cf7e2e491d2d5
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