Model Detail
Mage-VL
—Mage-VL is a code generation model with 2.4B parameters released by Microsoft. The model is registered under the image-text-to-text pipeline tag on Hugging Face, distributed under the permissive apache-2.0 license.
Mage-VL ships with 2.4B parameters. Total weight footprint is approximately 4.7 GB, which is the relevant figure when planning local-inference VRAM. The apache-2.0 license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
Mage-VL is best fit for code completion, repository-scale Q&A, and pair-programming integrations. It is a less obvious choice for one-shot generation of security-critical code without review. 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.
Mage-VL: An Efficient Codec-Native Streaming Multimodal Foundation Model
arXiv:2607.24904v1 Announce Type: cross Abstract: Standard vision-language models (VLMs) suffer from Moravec's paradox: they excel at complex offline visual reasoning but struggle with simple streaming perception tasks and process them inefficiently. We present Mage-VL, an efficient codec-native str
MAGE: Multi-Agent Self-Evolution with Co-Evolutionary Knowledge Graphs
arXiv:2605.10064v3 Announce Type: replace Abstract: Self-evolving language-model agents must decide what to learn next and how to preserve what they have learned across iterations. Existing systems typically carry this cross-iteration knowledge as natural-language feedback, flat episodic memory, or
