Model Detail
apex-flash-1
—apex-flash-1 is a multimodal model with 160.7B parameters released by cantina-security. The model is registered under the image-text-to-text pipeline tag on Hugging Face, distributed under the permissive mit license.
apex-flash-1 ships with 160.7B parameters. Total weight footprint is approximately 321.3 GB, which is the relevant figure when planning local-inference VRAM. The mit license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
apex-flash-1 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.
APEX: Assumption-free Projection-based Embedding eXamination Metric for Generative Distribution Evaluation
arXiv:2605.07786v4 Announce Type: replace-cross Abstract: As generative models achieve unprecedented visual quality, the gold standard for image evaluation remains traditional feature-distribution metrics. However, these metrics are provably hindered by the closed-vocabulary bottleneck of outdated f
APEX-Harm: Evaluating Agent Safety in Shared Professional Workspaces
arXiv:2610.13623v1 Announce Type: cross Abstract: Professional agents increasingly work through command-line interfaces and multiple tools over shared workspaces containing files, messages, and business applications. These environments create safety risks that are difficult to capture with attacks p
APEX: Asynchronous Prefix Denoising for Extreme Compression
arXiv:2610.12843v1 Announce Type: cross Abstract: Fewer latent tokens promise cheaper diffusion. Yet aggressive latent compression creates a reconstruction-generation dilemma: adding channels improves reconstruction but makes generation harder. We introduce APEX, short for Asynchronous Prefix denois
APEX: Assumption-free Projection-based Embedding eXamination Metric for Image Quality Assessment
arXiv:2605.07786v3 Announce Type: replace-cross Abstract: As generative models achieve unprecedented visual quality, the gold standard for image evaluation remains traditional feature-distribution metrics (e.g., FID). However, these metrics are provably hindered by the closed-vocabulary bottleneck o
APEX: Active Protection at Execution Boundaries for LLM Agents
arXiv:2610.06966v2 Announce Type: cross Abstract: Indirect prompt injection (IPI) hides adversarial instructions in content that large language model (LLM) agents read at runtime. As agents compose heterogeneous capability units, including Tools, MCP servers, and Skills, the carriers of injection mu
