Instructions to use cimol/50a30448-b07e-488d-b687-462b4f7ad10f with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cimol/50a30448-b07e-488d-b687-462b4f7ad10f with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-2-7b-chat") model = PeftModel.from_pretrained(base_model, "cimol/50a30448-b07e-488d-b687-462b4f7ad10f") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from cimol/50a30448-b07e-488d-b687-462b4f7ad10f: direct link, hf CLI and curl.
- Browser
- Download file 640 MB
-
https://huggingface.co/cimol/50a30448-b07e-488d-b687-462b4f7ad10f/resolve/main/adapter_model.bin
- Command line
-
hf download hf://cimol/50a30448-b07e-488d-b687-462b4f7ad10f/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/cimol/50a30448-b07e-488d-b687-462b4f7ad10f/resolve/main/adapter_model.bin
640 MB
- Xet hash:
- 0fe4e913e79e21e684731ea43847993c79a9750cff00f150d46fac6d58874541
- Size of remote file:
- 640 MB
- SHA256:
- 0d8b6dbd63b9f83e75f99af39b4d2a73f057ef8a94e5f3adafd636ef0ea7cc28
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