Instructions to use EdBerg/Baha_9MB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use EdBerg/Baha_9MB with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct") model = PeftModel.from_pretrained(base_model, "EdBerg/Baha_9MB") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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@@ -44,7 +44,7 @@ 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: cosine
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- lr_scheduler_warmup_ratio: 0.03
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- training_steps:
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- mixed_precision_training: Native AMP
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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: cosine
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- lr_scheduler_warmup_ratio: 0.03
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- training_steps: 620
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- mixed_precision_training: Native AMP
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### Training results
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