SwiGLU
PulseAugur coverage of SwiGLU — every cluster mentioning SwiGLU across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
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Transformer LLM Architectures Converge on Standard Stack
A recent analysis of 53 large language models from 2017 to 2025 reveals a significant convergence in transformer architectures. Key elements of this de facto standard include pre-normalization (RMSNorm), Rotary Position…
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New meta-learning LLM uses hypernetwork for adaptive textual conditioning
Researchers have developed a novel meta-learning approach for Large Language Models (LLMs) that addresses issues of corpus heterogeneity and condition changes. This method utilizes a hypernetwork to dynamically generate…
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IBM releases Granite 4.1 LLMs with 512K context and Apache 2.0 license
IBM has released the Granite 4.1 family of large language models, comprising 3B, 8B, and 30B parameter versions. These models were trained on approximately 15 trillion tokens through a five-stage pre-training process th…
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Research: Removing LayerNorm in LLMs acts as implicit regularizer, impacting performance based on training data size.
Researchers have investigated the impact of removing Layer Normalization (LayerNorm) from neural network architectures, particularly in models like GPT-2 and Llama. Their findings indicate that replacing LayerNorm with …
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MLP skip connections can't be absorbed into residual-free models
Researchers have investigated whether a skip connection around a single-hidden-layer MLP can be absorbed into a residual-free MLP of the same width. They found that for certain activation functions like ReLU^2 and ReGLU…