Instructions to use brildev7/gemma-7b-polite-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-polite-summarization-ko-sft-qlora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-7b") model = PeftModel.from_pretrained(base_model, "brildev7/gemma-7b-polite-summarization-ko-sft-qlora") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -85,4 +85,5 @@ outputs = model.generate(**inputs,
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do_sample=True,
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use_cache=False)
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print(tokenizer.decode(outputs[0]))
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do_sample=True,
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use_cache=False)
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print(tokenizer.decode(outputs[0]))
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- 14μΌ(νμ§μκ°) μ μ μμ 보μ₯μ΄μ¬νκ° μ΄μ€λΌμμ μμ²μΌλ‘ κΈ΄κΈνμλ₯Ό μμ§νμ¬ μ΄λκ³Ό μ΄μ€λΌμ λμ¬κ° μλ‘λ₯Ό 겨λ₯ν΄ μ€λ ννμ μνμ΄λΌκ³ κ°νκ² λΉλνλ λ
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```
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