Laguna XS 2.1, self-quantized to MLX by Atomic Chat. Built straight from Poolside’s original weights with a per-tensor importance matrix, so this is not a repack of somebody else’s files. Runs fully offline. — 33.4B parameters: the weights this repo quantizes. — Context length: 262,144 tokens (256K), as published by Poolside. — 40 layers: Mixture-of-Experts, hybrid sliding-window (512) and global attention. — Full imatrix ladder: every quant is calibrated with an importance matrix. — Mixed SWA and global attention layout: Laguna XS 2.1 uses sigmoid gating with per-layer rotary scales, enabling mixed SWA (Sliding Window Attention) and global attention layers in a 3:1 ratio (across 40 total layers). — KV cache in FP8: KV cache quantized to FP8, reducing memory per token. — Native reasoning support: Interleaved thinking between tool calls with support for enabling and disabling thinking per-request. > [!NOTE] > These MLXs are self-quantized from the original weights, not a repack. The importance matrix keeps low-bit quants closer to the full-precision model. Scores are Poolside’s published results for the base poolside/Laguna-XS-2.1, not our own measurements. Quantization preserves…
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Автор: AtomicChat
Теги: mlx, laguna, atomic-chat, poolside, apple-silicon, quantized, conversational, custom_code
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Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
