Instructions to use windmaple/gemma-chinese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use windmaple/gemma-chinese with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2b") model = PeftModel.from_pretrained(base_model, "windmaple/gemma-chinese") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files
README.md
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@@ -46,7 +46,7 @@ The following hyperparameters were used during training:
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: constant
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- lr_scheduler_warmup_ratio: 0.03
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- num_epochs:
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### Training results
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: constant
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- lr_scheduler_warmup_ratio: 0.03
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- num_epochs: 1
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### Training results
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