Instructions to use cimol/85d5500f-577e-4c16-be0b-9b41451bd06e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cimol/85d5500f-577e-4c16-be0b-9b41451bd06e with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b") model = PeftModel.from_pretrained(base_model, "cimol/85d5500f-577e-4c16-be0b-9b41451bd06e") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from cimol/85d5500f-577e-4c16-be0b-9b41451bd06e: direct link, hf CLI and curl.
- Browser
- Download file 671 MB
-
https://huggingface.co/cimol/85d5500f-577e-4c16-be0b-9b41451bd06e/resolve/main/adapter_model.bin
- Command line
-
hf download hf://cimol/85d5500f-577e-4c16-be0b-9b41451bd06e/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/cimol/85d5500f-577e-4c16-be0b-9b41451bd06e/resolve/main/adapter_model.bin
671 MB
- Xet hash:
- c667c5e7a8454c0f2123d232eacabadae74361d819d074c11830b4ba9aae3947
- Size of remote file:
- 671 MB
- SHA256:
- 13ef5ca57c2e082dcf26919ddc154f6ce74ce039105c3ab77747d39fc68b51cd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.