AI is happening. The future is updating.Signal-3.8-27B
> [!IMPORTANT] > **Update 2026-09-13 — use these settings for stable, short reasoning** > Temperature 0.6, min-p 0.05 (top-p 0.95, top-k 20), KV cache q8_0 or f16, and the MTP draft head on. > Without min-p, long answers can run away into repetition on some draws; a 4-bit value cache makes that > deterministic. Both are fixed by the settings above, on Signal and on the base model alike. > [!WARNING] > The weights in this repository were updated on 2026-09-13 (the GGUF tiers were rebuilt from them). The first release's tiers could, rarely, > repeat an answer line when sampling without the draft head; the rebuilt tiers showed no loop or cap > in 55 traces under the same conditions. If you downloaded before that date, re-download. AgentionAI Signal is a minimally invasive fine-tune of Qwen3.8-27B designed for lower generation latency and better token efficiency. On our held-out general-prompt evaluation, Signal produces **57% fewer answer tokens** and uses **52% fewer thinking tokens**, while matching or improving the measured answer quality of the base model.
> [!IMPORTANT] > **Update 2026-09-13 — use these settings for stable, short reasoning** > Temperature 0.6, min-p 0.05 (top-p 0.95, top-k 20), KV cache q8_0 or f16, and the MTP draft head on. > Without min-p, long answers can run away into repetition on some draws; a 4-bit value cache makes that > deterministic. Both are fixed by the settings above, on Signal and on the base model alike. > [!WARNING] > The weights in this repository were updated on 2026-09-13 (the GGUF tiers were rebuilt from them). The first release's tiers could, rarely, > repeat an answer line when sampling without the draft head; the rebuilt tiers showed no loop or cap > in 55 traces under the same conditions. If you downloaded before that date, re-download. AgentionAI Signal is a minimally invasive fine-tune of Qwen3.8-27B designed for lower generation latency and better token efficiency. On our held-out general-prompt evaluation, Signal produces **57% fewer answer tokens** and uses **52% fewer thinking tokens**, while matching or improving the measured answer quality of the base model.