Model Detail
Swift-1.5-Qwen3.8-27b
—Swift-1.5-Qwen3.8-27b is a multimodal model with 27B parameters released by ukisai. The model is registered under the image-text-to-text pipeline tag on Hugging Face, distributed under a other license.
Swift-1.5-Qwen3.8-27b ships with 27B parameters. Total weight footprint is approximately 27.8 GB, which is the relevant figure when planning local-inference VRAM. Distribution is governed by the other license — review the exact terms before commercial deployment.
Swift-1.5-Qwen3.8-27b is best fit for mixed text-and-image reasoning tasks such as document understanding. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
A rigor-matched audit of periodic-step layer skipping for efficient llm inference: conflayers versus swift, with a supplemental analysis of trained routing alternatives
arXiv:2608.28846v2 Announce Type: replace-cross Abstract: Layer-skipping methods for efficient LLM inference decide, at some granularity, which transformer layers to execute for a given input. We present a rigor-matched, three-seed audit of two periodic-step, search-based methods that make this deci
SwiftExplorer: Training-free Diffusion Model Alignment with Swift Diversity Exploration
arXiv:2609.06651v1 Announce Type: cross Abstract: Diffusion models have general generative abilities but struggle to align with specific objectives. Fine-tuning can improve alignment, yet its training cost is often prohibitive. This led to training-free methods that apply objective-guided terms in s
SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving
arXiv:2607.09741v1 Announce Type: cross Abstract: Accurate trajectory prediction in autonomous driving hinges on modeling dynamic and context-dependent interactions among traffic agents. However, most existing approaches are purely data-driven and lack structural priors, which limits their generaliz
SWIFT: Prompt-Adaptive Memory for Efficient Interactive Long Video Generation
arXiv:2605.09442v1 Announce Type: cross Abstract: Streaming long-video generation faces a central challenge in continuous semantic switching, requiring adaptive memory to preserve coherent visual evolution. Current approaches rely on cache rebuilding at prompt boundaries or fixed memory budgets, but
Taylor Swift deepfakes are pushing scams on TikTok
Scammers are using AI-generated videos of celebrities including Taylor Swift and Rihanna to promote shady services on TikTok, according to authentication company Copyleaks. The ads typically show celebrities in interview settings, such as red carpets, podcasts, or talk shows, and often manipulate re
