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Gemma 3 270M

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Headlines · 2

  1. Paper: frozen 16 MB embedding table can match learned embeddings in small models

    A paper posted to r/LocalLLaMA, titled "Embeddings Are Overpriced?", proposes replacing a small language model's trainable embedding table with a frozen 16 MB table of 512-bit codes that has no trainable parameters, derived once from a pretrained embedding via principal-component projection and sign binarisation. The authors trained the same Gemma-shaped body from scratch on 2.05B tokens with and without the table; they report the learned table buys 0.20 nats of validation loss for 2.6 times the trainable parameters, while the frozen-table model is better or tied on LAMBADA, HellaSwag and PIQA, with only ARC-Easy favouring the learned table. They also report that with a ternary BitNet b1.58 body the whole model is a 46 MB file that ties the float model on LAMBADA and PIQA and generates 615 to 740 tokens per second on a laptop CPU in 62 MB of memory, and that weights, data and evaluation answers are released.

    r/LocalLLaMA · 🔥 0
  2. Synaptics unveils Astra SR200 edge AI chips with Google Coral NPU

    Synaptics announced the Astra SR200 series of edge AI "xMCU" processors on Oct. 20, built around Google Research's open-source Coral NPU and Synaptics' Torq AI engine, with three pin-compatible models rated at 200, 300 and 400 GOPS. The chips pair Arm Cortex-M85 and Cortex-M52 cores with up to 8MB of SRAM and custom DRAM scalable to hundreds of megabytes, targeting wearables, connected cameras, industrial vision and voice systems; Synaptics says the family remains in sampling with no public pricing or firm production schedule. The Coralboard SL developer kit, based on the separate Astra SL2619 processor and a 1-TOPS Coral NPU implementation, reached general availability through DigiKey the same day, while a Coralboard SR board based on the SR250 was announced without an order date or price.

    cnBeta · 🔥 0

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