Model Detail
Marco-Nano-Instruct
—Marco-Nano-Instruct is a large language model with 4.0B parameters released by AIDC-AI. The model is registered under the text-generation pipeline tag on Hugging Face, distributed under the permissive apache-2.0 license.
Marco-Nano-Instruct ships with 4.0B parameters. Total weight footprint is approximately 8.0 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.
Marco-Nano-Instruct is best fit for general-purpose chat and instruction-following workloads. 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.
MARCO: The Radioactive Watermark for Protein Generative Models
arXiv:2610.08316v1 Announce Type: cross Abstract: Protein Generative Models (PGMs) have revolutionized structural biology by enabling the design of complex 3D protein structures from sequence data. However, this breakthrough introduces a dual-use challenge, exposing high-value PGMs to economic risks
MARCO: Multi-Round Agentic Reinforcement for Conditional Molecular Optimization
arXiv:2609.36683v1 Announce Type: new Abstract: Molecular optimization is inherently iterative: a candidate is proposed, evaluated against several objectives, and revised while preserving a relationship to the source molecule. Most instruction-following models instead emit one edited molecule, forci
Marco-MoE: Open Multilingual Mixture-of-Expert Language Models with Efficient Upcycling
arXiv:2604.25578v1 Announce Type: new Abstract: We present Marco-MoE, a suite of fully open multilingual sparse Mixture-of-Experts (MoE) models. Marco-MoE features a highly sparse design in which only around 5\% of the total parameters are activated per input token. This extreme sparsity, combined w
Marco DeepResearch: Unlocking Efficient Deep Research Agents via Verification-Centric Design
arXiv:2603.28376v1 Announce Type: cross Abstract: Deep research agents autonomously conduct open-ended investigations, integrating complex information retrieval with multi-step reasoning across diverse sources to solve real-world problems. To sustain this capability on long-horizon tasks, reliable v
