Original Summary

Hey everyone,<p>Jovan from UkisAI here! Today, we are introducing Swift, a family of efficient reasoning LLMs based on Qwen, trained by penalizing tokens related to pathological overthinking patterns and restoring accuracy via RL and OPD.<p>After amazing feedback and 350k+ downloads in 13 days on our Swift Qwen 3.8 27B we are releasing the entire model family as well as the highly requested GSQ-RCO quants for 27B and Flash-Next.<p>This release includes: Swift1.5 27B, an improved version of our last model, with even lower token usage, fixed bugs and better agentic performance, with -58.5% thinking tokens while scoring 0.35% higher while outperfoming base on Terminal Bench 2.1 by not falling into &quot;overthinking error&quot; loops Swift Flash Next, with 63.4% fewer thinking tokens and a 1.8x speed up scoring -0.2% vs base on xhigh Swift Bonsai 2, with 39.8% fewer thinking tokens while scoring 0.19% higher (although we&#x27;d still like to note it as experimental)<p>Our benchmarks are ran x5 on Base and Swift, averaging across five seeds and various domains, including General (GPQA, AIME26), Coding (LiveCodeBench), Vision (ERQA), Agentic (Terminal Bench 2.1). One note is that the Terminal Bench 2.1 scores are misleadingly low at first glance. It is not a bug, but a simple matter of the Swift models not falling into overthinking loops and failing the task, rather pursuing it until the end, leading to higher average token usage. The token reduction still falls in the -38.7% range when compared apples-to-apples.<p>We are including a Free Research API and HuggingFace Spaces to give the models a spin before downloading or if you don&#x27;t have enough compute to run them right now! You can find both on the model cards.<p>We have also made GGUF, NVFP4, MLX and W4A16 quants for relevant model versions.<p>More details on our training approach and community feedback can be seen here: https:&#x2F;&#x2F;www.reddit.com&#x2F;r&#x2F;LocalLLaMA&#x2F;comments&#x2F;1wg7dd5&#x2F;ukisai_swiftqwen3827b_583_thinking_x195_speed&#x2F;<p>All of the various quantization and model versions are available in their respective collections: Swift1.5 27B: https:&#x2F;&#x2F;huggingface.co&#x2F;collections&#x2F;ukisai&#x2F;swift-15-27b Swift Flash Next: https:&#x2F;&#x2F;huggingface.co&#x2F;collections&#x2F;ukisai&#x2F;swift-flash-next Swift Bonsai 2: https:&#x2F;&#x2F;huggingface.co&#x2F;collections&#x2F;ukisai&#x2F;swift-bonsai-2<p>We would greatly appreciate your feedback via independent evaluations. As per last release, we operate on a candy-shop basis, trying to fulfill as many Swift model requests and quants as possible, so please do share your needs in the comments!

中文概览

中文标题： UkisAI Swift 系列 / 27B、Flash Next 与 Bonsai 2 / 思考量 -63.4%，速度 x1.95

UkisAI 发布基于 Qwen 的高效推理模型家族 Swift，通过惩罚过度思考模式并用 RL 与 OPD 恢复准确率：Swift1.5 27B 思考 token 减少 58.5%；Flash Next 减少 63.4%、速度约 1.8 倍；Bonsai 2 减少 39.8%（标为实验性）。另提供免费研究 API、HuggingFace Spaces 及 GGUF、NVFP4、MLX、W4A16 量化版本。

  • 情报分类：技术学习与提效
  • 分类依据：开源 LLM 模型家族发布，含训练方法与基准表现，属技术内容。
  • 信息来源：Hacker News 新项目
  • 发布时间：2026/9/25 00:33:21