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Quick Review: Norm Tweaking: High-performance Low-bit Quantization of Large Language Models

Norm Tweaking: High-performance Low-bit Quantization of Large Language Models

Steps for Implementation:

  1. Generate Data: Prepare and preprocess the dataset suitable for training the model.
  2. GPTQ: Apply GPTQ method for optimizing the quantization precision of model parameters.
  3. Train LayerNorm Only: Focus on training the Layer Normalization component of the model for fine-tuning and optimization.
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