Instructions to use brildev7/gemma-7b-it-summarization-ko-sft-qlora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use brildev7/gemma-7b-it-summarization-ko-sft-qlora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-7b-it") model = PeftModel.from_pretrained(base_model, "brildev7/gemma-7b-it-summarization-ko-sft-qlora") - Notebooks
- Google Colab
- Kaggle
Download .gitattributes from brildev7/gemma-7b-it-summarization-ko-sft-qlora: direct link, hf CLI and curl.
- Browser
- Download file 162 Bytes
-
https://huggingface.co/brildev7/gemma-7b-it-summarization-ko-sft-qlora/resolve/main/.gitattributes
- Command line
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hf download hf://brildev7/gemma-7b-it-summarization-ko-sft-qlora/.gitattributes
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curl -L -o .gitattributes https://huggingface.co/brildev7/gemma-7b-it-summarization-ko-sft-qlora/resolve/main/.gitattributes
162 Bytes
| adapter_model.safetensors filter=lfs diff=lfs merge=lfs -text | |
| optimizer.pt filter=lfs diff=lfs merge=lfs -text | |
| tokenizer.json filter=lfs diff=lfs merge=lfs -text | |