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@@ -31,7 +31,7 @@ The main steps in knowledge distillation are:
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## Sample notebook
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You can use the [sample notebook](https://aka.ms/meta-llama-3.1-distillation) to see how to perform distillation. In this sample notebook, the teacher model uses the Meta Llama 3.1 405B instruction model, and the student model uses the Meta Llama 3.1 8B instruction model.
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You can use the [sample notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/foundation-models/system/distillation/README.md) to see how to perform distillation. In this sample notebook, the teacher model uses the Meta Llama 3.1 405B instruction model, and the student model uses the Meta Llama 3.1 8B instruction model.
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We used an advanced prompt during synthetic data generation. The advanced prompt incorporates chain-of-thought (CoT) reasoning, which results in higher-accuracy data labels in the synthetic data. This labeling further improves the accuracy of the distilled model.
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