Model Detail
lift
▲ 6.5%lift is a multimodal model with 4.8B parameters released by datalab-to. The model is registered under the image-text-to-text pipeline tag on Hugging Face, released under the openrail license.
lift ships with 4.8B parameters. Total weight footprint is approximately 9.7 GB, which is the relevant figure when planning local-inference VRAM. Distribution is governed by the openrail license — review the exact terms before commercial deployment.
Downloads of lift have moved +6.5% over the past 24 hours, +1278.5% over the trailing seven days. That puts the model in active uptrend territory; a sustained move of this size usually reflects a recent release, a viral integration, or a benchmark surprise rather than steady-state demand. These numbers are signal, not guarantee — week-over-week download counts on Hugging Face also reflect mirror traffic, CI scrapes, and one-off benchmarking runs.
lift 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.
Certified Safety Radii in Forecast-Error Space for Wasserstein Distributionally Robust Small Signal Stability-Constrained AC Optimal Power Flow via Lifted Spectrahedral Containment
arXiv:2608.30201v1 Announce Type: new Abstract: Directly robustifying small-signal stability in AC optimal power flow is challenging since the stability boundary in the original uncertainty space is implicit, highly nonconvex, and changes with the operating decision. This paper exploits an alternati
Budget-Constrained Causal Bandits: Bridging Uplift Modeling and Sequential Decision-Making
arXiv:2604.26169v2 Announce Type: replace Abstract: Treatment allocation under budget constraints is a central challenge in digital advertising. The standard approach trains an offline uplift model on historical data, then solves a constrained optimization to allocate budget. This fails in cold-star
SinkSLOT: Sinkhorn via Sparse Lifted Optimal Transport
arXiv:2608.28262v1 Announce Type: new Abstract: Entropic optimal transport (EOT) has been shown to offer a computationally tractable approximation to exact optimal transport. However, the standard Sinkhorn-Knopp algorithm has two main limitations. First, given discrete measures with $N$ points, each
Lifted Model Construction under Approximate Commutativity
arXiv:2608.24713v1 Announce Type: new Abstract: Lifted inference algorithms enable scalable probabilistic inference even for large object domains by leveraging the indistinguishability of objects in a probability distribution. An essential prerequisite for constructing a lifted representation is to
CLIFT: Turning Gemini Robotics On-Device into Humanoid Specialists via Non-Invasive Closed-Loop Iterative Fine-Tuning
arXiv:2607.29172v1 Announce Type: cross Abstract: While robot foundation models are growing increasingly capable, the strongest models are typically trained on proprietary data and remain closed-source, limiting downstream users' ability to adapt them to new tasks, embodiments, and deployment settin
Multi-channel Uplift Policy Learning
arXiv:2607.28182v1 Announce Type: new Abstract: E-commerce platforms must allocate fixed marketing budgets across multiple channels to maximize business utility. However, standard predict-then-optimize (PTO) paradigms fail in this compositional space due to observational confounding and severe extra
