Instructions to use lightsource/gemma2_9b_multilabel_lora_adapter_ver1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use lightsource/gemma2_9b_multilabel_lora_adapter_ver1 with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("unsloth/gemma-2-9b-it-bnb-4bit") model = PeftModel.from_pretrained(base_model, "lightsource/gemma2_9b_multilabel_lora_adapter_ver1") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from lightsource/gemma2_9b_multilabel_lora_adapter_ver1: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/lightsource/gemma2_9b_multilabel_lora_adapter_ver1/resolve/main/training_args.bin
- Command line
-
hf download hf://lightsource/gemma2_9b_multilabel_lora_adapter_ver1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/lightsource/gemma2_9b_multilabel_lora_adapter_ver1/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- daad26d5ea9ab402d842fa072c2d295c3b0e495edcc80bbcc0f311b1ebbd6e3d
- Size of remote file:
- 5.18 kB
- SHA256:
- 5d7dc7de25ccbb8257479af2b23287619fa75e500a97bb3dfcfa9dd92cc692a3
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