Instructions to use mrhunghd/19a98db4-293c-436b-86ab-ad1b047916e1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mrhunghd/19a98db4-293c-436b-86ab-ad1b047916e1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-2-2b-it") model = PeftModel.from_pretrained(base_model, "mrhunghd/19a98db4-293c-436b-86ab-ad1b047916e1") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from mrhunghd/19a98db4-293c-436b-86ab-ad1b047916e1: direct link, hf CLI and curl.
- Browser
- Download file 41.7 MB
-
https://huggingface.co/mrhunghd/19a98db4-293c-436b-86ab-ad1b047916e1/resolve/main/adapter_model.bin
- Command line
-
hf download hf://mrhunghd/19a98db4-293c-436b-86ab-ad1b047916e1/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/mrhunghd/19a98db4-293c-436b-86ab-ad1b047916e1/resolve/main/adapter_model.bin
41.7 MB
- Xet hash:
- ea30b65d8fa9a1a60795ee084c010c3469c3fe8f2739c1142e31c81cfdf34574
- Size of remote file:
- 41.7 MB
- SHA256:
- 20e084cf63d51323b46058ffb23d373986a124788937555e02d26a5cd2b8510d
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