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Explore AI models 21
Swift-1.5-4bit-MLX
ukisaiWebsite • Learn more • BF16 model • GGUF • GSQ-RCO GGUF • Evaluation • Enterprise licensing **Apple MLX 4-bit affine quantization.** This is Swift 1.5 in native MLX format, converted with the official Apple MLX-LM converter using 4 bits and group size 64. Swift 1.5 is UkisAI's reasoning-efficient Qwen3.8-27B derivative, focused on stronger long-horizon, agentic and coding performance while using fewer thinking tokens. **Starting from Homebrew or seeing `Received 501 parameters not in model`?** Use QUICKSTART.md to install the required runtime and create an explicit `serve` launcher. It reuses your existing model directory, including an HF cache snapshot. A bare `mlx_lm.server` command may select a separate Homebrew installation that lacks the Swift architecture and cache patches.
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View details ↗Swift-1.5-5bit-MLX
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View details ↗Swift-1.5-Qwen3.8-27b
ukisaiWebsite • Learn more • GGUF • GSQ-RCO GGUF • Evaluation • Enterprise licensing Swift 1.5 Qwen3.8-27B is UkisAI's reasoning-efficient derivative of Qwen3.8-27B. It uses **58.5% fewer thinking tokens** while scoring **0.35% higher** than the base, for a **1.95× speed-up** on several tasks. Swift 1.5 is a direct upgrade from Swift 1.0, our model with 350k+ downloads, delivering stronger overall performance than both base and Swift 1.0 in various tasks, especially coding and agentic, while using fewer thinking tokens. We accomplished that by scaling up the post-training (RL and OPD) from the previous version.
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View details ↗Website • Learn more • GGUF • GSQ-RCO GGUF • Evaluation • Enterprise licensing **GGUF quantizations.** Derived directly from Swift 1.5 with llama.cpp. Run a chosen quantization tier with a current llama.cpp-compatible runtime such as `llama-server`. Swift 1.5 Qwen3.8-27B is UkisAI's reasoning-efficient derivative of Qwen3.8-27B. It uses **58.5% fewer thinking tokens** while scoring **0.35% higher** than the base, for a **9.18× speed-up** on several tasks.
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View details ↗Website • Learn more • BF16 model • Standard GGUFs • Evaluation • Enterprise licensing Compact, mixed-precision GGUF quantizations of Swift 1.5 Qwen3.8-27B, with Swift-specific refinement using the per-tensor allocations from ISTA-DASLab's GSQ-RCO release. Swift 1.5 builds on Swift 1.0 through post-training focused on long-horizon, agentic and coding tasks, improving overall performance while using fewer thinking tokens. See the original model card for the model's training approach and benchmark results. Those model-level benchmarks are separate from the quantization measurements below.
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View details ↗Website • Learn more • GGUF • GSQ-RCO GGUF • Evaluation • Enterprise licensing **AWQ + GPTQ INT4 (W4A16).** Derived directly from Swift 1.5 with LLM Compressor. Run it with a `compressed-tensors` W4A16 runtime that honors the saved FP8 KV-cache configuration. Swift 1.5 Qwen3.8-27B is UkisAI's reasoning-efficient derivative of Qwen3.8-27B. It uses **58.5% fewer thinking tokens** while scoring **0.35% higher** than the base, for a **9.18× speed-up** on several tasks.
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View details ↗Website • Learn more • GGUF • GSQ-RCO GGUF • Evaluation • Enterprise licensing **NVIDIA NVFP4/FP8 mixed precision.** Derived directly from Swift 1.5 with NVIDIA ModelOpt. Run it with a ModelOpt-compatible runtime; native NVFP4 execution requires compatible NVIDIA Blackwell support. Swift 1.5 Qwen3.8-27B is UkisAI's reasoning-efficient derivative of Qwen3.8-27B. It uses **58.5% fewer thinking tokens** while scoring **0.35% higher** than the base, for a **9.18× speed-up** on several tasks.
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View details ↗Website • Learn more • GGUF • GSQ-RCO GGUF • Evaluation • Enterprise licensing **AWQ INT4 (W4A16).** Derived directly from Swift 1.5 with LLM Compressor. Run it with a runtime that supports `compressed-tensors` W4A16. Swift 1.5 Qwen3.8-27B is UkisAI's reasoning-efficient derivative of Qwen3.8-27B. It uses **58.5% fewer thinking tokens** while scoring **0.35% higher** than the base, for a **9.18× speed-up** on several tasks.
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View details ↗Website • Learn more • GGUF • GSQ-RCO GGUF • Evaluation • Enterprise licensing **GGUF quantizations.** Derived directly from Swift 1.5 Qwen3.8-Flash-Next with llama.cpp. Run a chosen quantization tier with a current llama.cpp-compatible runtime such as `llama-server`. Swift 1.5 Qwen3.8-Flash-Next is UkisAI's reasoning-efficient derivative of Qwen3.8-Flash-Next. It uses **63.4% fewer thinking tokens**, with a **1.8x speed up** while keeping the **accuracy loss Create a 3D endless runner that has the fast, playful feel of Subway Surfers, but make the world and characters your own. I want to run through a lively place, dodge things, collect rewards, and feel the pace build the longer I survive. Make it fun to control and visually memorable. Use your judgment for the setting, mechanics, and little details that make it feel like a real game. Build it so I can launch and play it locally, then tell me how to run it.
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View details ↗Website • Learn more • BF16 model • Standard GGUFs • Evaluation • Enterprise licensing Mixed-precision GGUF quantizations of Swift Flash Next, with Swift-specific GSQ refinement and reused ISTA GSQ-RCO per-tensor allocation profiles. Swift Flash Next is UkisAI's reasoning-efficient derivative of Qwen3.8-Flash-Next. Its post-training targets shorter reasoning traces and coding, agentic and long-horizon tasks. See the original model card for model-level benchmarks and training details. Those benchmarks are separate from the quantization measurements below.
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View details ↗Swift-Bonsai-2-GGUF
ukisaiWebsite • Learn more • Quantizations • Evaluation • How to use • License Swift Bonsai 2 is UkisAI's reasoning-efficient derivative of Prism ML's Ternary Bonsai 2 27B. It uses **39.8% fewer thinking tokens** while scoring **0.19% higher** than the base. Both the 1-bit and 2-bit quantizations are available in this repository; each file is a plain Bonsai 2 pack.
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View details ↗Swift-Qwen3.8-27b
ukisaiWebsite • Learn more • GGUF • Enterprise licensing Swift-Qwen3.8-27B is UkisAI's reasoning-efficient derivative of Qwen3.8-27B, using **58.3% fewer thinking tokens** while maintaining near-identical performance (**<1% loss**) and as a result getting a **x1.95 speed-up** on several tasks. We built Swift by identifying reasoning-marker tokens that, in our analysis, trigger overthinking in Qwen’s reasoning rollouts. We then fine-tuned Qwen by penalizing usage of those tokens while it reasons.
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View details ↗Swift-Qwen3.8-27B-GGUF
ukisaiWebsite • Learn more • BF16 model • Enterprise licensing Swift-Qwen3.8-27B is UkisAI's reasoning-efficient derivative of Qwen3.8-27B, using **58.3% fewer thinking tokens** while maintaining near-identical performance (**<1% loss**) and as a result getting a **x1.95 speed-up** on several tasks. .swift-table { width:100%; table-layout:fixed; border-collapse:separate; border-spacing:0; overflow:hidden; border:1px solid #27344A; border-radius:20px; background:#0D111B; font-family:-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,sans-serif; font-size:14px; color:#BFBDBD; } .swift-table th { padding:13px 8px; text-align:center; font-weight:700; color:#AEB5C7; background:#0D111B; border-right:1px solid #27344A; border-bottom:1px solid #27344A; } .swift-table td { padding:14px 8px; text-align:center; color:#BFBDBD; background:#0D111B; border-right:1px solid #27344A; border-bottom:1px solid #27344A; vertical-align:middle; overflow-wrap:break-word; } .swift-table tr > :last-child { border-right:0; } .swift-table tbody tr:last-child td { border-bottom:0; } .swift-table .benchmark-heading { color:#B7BDCD; background:#0D111B; border-bottom:3px solid #7D45B5; } .swift-table .score-heading { color:#F0C5FF; background:#52239E; border-bottom:3px solid #7D45B5; } .swift-table .tokens-heading, .swift-table .median-heading { color:#D4E8FF; background:#304FC2; border-bottom:3px solid #5687E6; } .swift-table .benchmark { padding-left:18px; text-align:left; color:#FFFFFF; font-weight:600; } .swift-table strong { color:#FFFFFF; } .swift-table .section { padding:12px 18px; text-align:left; color:#B489FF; background:#2A2541; font-weight:700; letter-spacing:.08em; text-transform:uppercase; border-top:1px solid #3A3159; border-bottom:1px solid #3A3159; } .swift-table .swift { background:#171127; } .swift-table thead tr:nth-child(2) .swift { color:#D3A0FF; } .swift-table .reduction { color:#69BFFF; background:#101B2C; font-weight:700; } .swift-table .detail { color:#8C94A8; font-size:12px; font-weight:500; }
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View details ↗Swift-Qwen3.8-27B-NVFP4
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