Peter and Dani, during their MATS Fellowship, shared a modification to synthetic document fine-tuning (SDF) called grafting: fine-tune a pre-training checkpoint, then add the resulting weight difference onto the post-trained model you want to deploy. They validated it on five model families up to 284B parameters.
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