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 optimizer.pt from lightsource/gemma2_9b_multilabel_lora_adapter_ver1: direct link, hf CLI and curl.
- Browser
- Download file 184 MB
-
https://huggingface.co/lightsource/gemma2_9b_multilabel_lora_adapter_ver1/resolve/main/optimizer.pt
- Command line
-
hf download hf://lightsource/gemma2_9b_multilabel_lora_adapter_ver1/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/lightsource/gemma2_9b_multilabel_lora_adapter_ver1/resolve/main/optimizer.pt
184 MB
- Xet hash:
- 6710566e4c5434b82a5953c28ba920fc0560dd79f665fbc0f4568f9b97eaefbb
- Size of remote file:
- 184 MB
- SHA256:
- e5feeb791b0060befb23f4d474107b8c7289248a9f270cd337979602a2d226f8
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