AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Post-training quantization (PTQ) methods typically use sequential quantization that partitions a pre-trained LLM into a series of units (e.g., transformer blocks), with one unit quantized at each stage. State-of-the-art PTQ methods are predominantly learning-based, optimizing auxiliary quantization parameters (e.g., sc...
ScaleQ-1.58 is proposed, a scalable ternary post-training quantization (PTQ) framework for reasoning LLMs that demonstrates strong generalization across tasks of varying difficulty levels, including mathematics, coding and scientific logic reasoning, as well as commonsense reasoning and basic language generation.
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