Signal-3.8-Flash-Next-GGUF

モデル紹介

AgentionAI Signal is a minimally invasive fine-tune of Qwen3.8-Flash-Next for lower latency and better token efficiency. On MATH-500 it spends **26% fewer reasoning tokens** than the base model with **half as many hesitation markers**, and scores higher (99.2% against 97.5%). As a coding agent on Terminal-Bench 2.0 it finishes the same tasks in **25% less wall time**. With speculative decoding it is faster still: the multi-token-prediction drafter agrees with Signal 90% of the time against 69% for the base model, so more tokens are accepted per forward pass. Signal gets there by being direct, not by cutting corners. Reasoning models spend a large share of their tokens on hesitation and re-checking rather than on new steps. Signal keeps every step that does work and drops the ones that only describe the process, and in answers it drops preambles, sign-offs, excess formatting and narration while keeping the substance. The 177B mixture-of-experts backbone, the vision encoder and the multi-token-prediction draft head all work exactly as in the base model, so it is a drop-in replacement. Signal is trained by self-distillation, on Qwen3.8-Flash-Next's own answers generated under an instruction to be direct that the released model no longer needs. No external data and no other model's outputs went into it, so it keeps the base model's knowledge and voice intact.

主な特徴

特点摘要待依据来源补齐，不凭模型名称推断能力。

制約と注意点

具体的な制約を判断する出典が不足しています。実際の性能、遅延、データ方針、ライセンス、提供期間を確認してください。

出典別料金と費用分析

料金はチャネル・地域・設定によって異なります。各チャネルの料金は個別に表示し、提供元の統一公式料金とは見なしません。

公開評価と実行設定

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

よくある質問

出典と追跡記録

许可证：other。模型介绍与第三方评测均以原来源的具体版本和方法为准。