AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work combines vocabulary surgery for tokenizer adaptation without full retraining, cross-lingual transfer of quality classifiers via translation, enabling filtering without target-language annotations, and generation of instruction data through translation, task conversion, and targeted synthesis.
Pretrained byte-level BPE tokenizers can segment underrepresented languages inefficiently. Replacing a tokenizer changes the meaning of nearly every token ID, while vocabulary expansion enlarges the model's embedding and output matrices. We study post-hoc adaptation that keeps the model-vocabulary size fixed and preser...
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