AI Model2026
F16 GGUF release packaged for llama.cpp and similar runtimes that applies UkisAI's Swift adapter to Qwen3.8-27B. Cuts reasoning/thinking tokens by ~58% with <1% performance loss, includes multimodal projector, MTP layers, multiple GGUF quant tiers, and is distributed under the Swift Open License v1.0.
AI Model2026
Compresses a 27B-class multimodal model into end-to-end ternary weights to run 27B reasoning on-device: 5.9–8.6 GB deployed footprint, 262K-token context, ~98.2% of FP16 benchmark performance; ships MLX and GGUF packs and runs on Apple MLX and CUDA.
AI Model2026
A 27B-class language model packaged in GGUF with end-to-end ternary weights for on-device or single-GPU llama.cpp inference; reduces FP16 footprint to ~5.9–7.2 GB while retaining ~98% of baseline performance and supporting up to 262K tokens.
AI Image2026
Provides GGUF-quantized builds of Qwen-Image-2.1 for local text-to-image generation and ComfyUI workflows; includes multiple quantizations (Q4_K_M recommended), companion text encoders (BF16/Int8) and a VAE, and ships without a built-in safety filter.
AI Model2026
Provides quantized Qwen-Image-2.1-Turbo weights for text-to-image generation across GGUF, Safetensors, and MLX formats. It retains the model’s 8-step Turbo workflow, with options for different hardware and runtimes.
AI Model2026
GGUF-format, quantized (Q4_K_M) and refusal-ablated (Heretic) text encoder build for Qwen-Image-2.1, supplied with a separate f16 vision mmproj so the full multimodal pipeline runs on llama.cpp/ComfyUI (Mac/non-CUDA friendly); multiple formats and quantizations included.
AI Image2026
Provides multiple quantized releases (GGUF, safetensors, MLX) of Qwen-Image-2.1 for local text-to-image generation; includes GPU- and Apple-Silicon–optimized quantizations, companion text encoders/VAEs, and recommended ComfyUI configurations.
AI Audio2026
Performs speaker diarization (who spoke when) for live and recorded audio using an open-weight, 100M-parameter streaming-capable model that supports up to eight anonymous speaker channels, overlapping speech, chunked processing, and configurable latency for ASR integration.
AI Model2026
Provides compact mixed-precision GGUF quantizations of UkisAI's Swift 1.5 (derived from Qwen3.8-27B), using ISTA GSQ-RCO per-tensor allocations with Swift-specific refinement. Offers multiple 8–12 GB tiers, optional MTP heads, and KLD evaluation against the Swift BF16 baseline.
AI Model2026
Capability-targeted compression of Qwen3.8-Flash-Next: half the experts are removed and remaining weights quantized to 3.5 bpw, producing a 58.4 GB GGUF (29.6 GB resident) that preserves coding and multimodal ability while trading off other domains.
AI Model2026
Provides a high-fidelity mixed-precision (≈3-bit) GGUF quant of Qwen3.8-27B tailored for long-horizon agent workloads and cyber-focused red-teaming. Preserves reasoning, thinking mode, MTP speculative decoding and vision (via a separate mmproj); released as an uncensored/abliterated research build under Apache-2.0.
AI Model2026
Delivers fast, calibrated 'System 1' decisions over text and images (yes/no, 2–256 choices, 0–5 scores) in one forward pass, plus full 'System 2' Qwen3.8-27B multimodal reasoning. Highlights: zero-shot vision judging, 256K-token context, vLLM decision API, Apache-2.0.