UkisAI

Headlines · 2

1. 2d
   UkisAI releases two Swift 1.5 reasoning-efficiency models

   UkisAI announced Swift 1.5 GLM-5.3-Flash and Swift 1.5 Ornith-9B, with GGUF and other quantized weights available for local inference. The company says GLM-5.3-Flash cut thinking-token usage by 61.4% on MMLU-PRO with a 0.46% average accuracy loss, while Ornith-9B reduced thinking-token usage by 33.5% without an accuracy loss. These results are vendor claims based on UkisAI's own benchmarks and have not been independently verified.

   r/LocalLLaMA · 🔥 0
2. 6d
   Swift 1.5 benchmark shows coding-agent token savings on Strix Halo

   A r/LocalLLaMA user compared UkisAI’s Swift 1.5 fine-tune of Qwen3.8-Flash-Next with Unsloth’s quantization across Halogen, gufo and llama.cpp on an Asus ROG Flow Z13 with 128 GB of memory and a 70 W power limit. Swift produced 56,000–63,000 output tokens across 11 Pi coding-agent tasks, versus 75,000–99,000 for Qwen3.8-Flash-Next, and the gufo Swift setup completed them in 24 minutes compared with 33 minutes for gufo with Unsloth’s UD-Q4_K_XL. The results were mixed on math and LiveCodeBench, and the author cautioned that they came from a single run, so differences of one or two solved problems could reflect chance.

   r/LocalLLaMA · 🔥 0

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