DataBubble·
Model Detail
Marco-Mini-Instruct
—Provider: AIDC-AICategory: llmPipeline: text-generation
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0.3
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1K
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Model Info
Licenseapache-2.0
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Consequentialist Objectives and Catastrophe3h◆EgoMAGIC- An Egocentric Video Field Medicine Dataset for Training Perception Algorithms3h◆ReCast: Recasting Learning Signals for Reinforcement Learning in Generative Recommendation3h◆A Probabilistic Framework for Hierarchical Goal Recognition3h◆CNSL-bench: Benchmarking the Sign Language Understanding Capabilities of MLLMs on Chinese National Sign Language3h◆The Shape of Adversarial Influence: Characterizing LLM Latent Spaces with Persistent Homology3h◆Toward Principled LLM Safety Testing: Solving the Jailbreak Oracle Problem3h◆Learning from Natural Language Feedback for Personalized Question Answering3h◆From Multi-Agent to Single-Agent: When Is Skill Distillation Beneficial?3h◆TS-Arena -- A Live Forecast Pre-Registration Platform3h◆Report for NSF Workshop on AI for Electronic Design Automation3h◆Initial results of the Digital Consciousness Model3h◆Rethinking Retrieval-Augmented Generation as a Cooperative Decision-Making Problem3h◆Sensory-Aware Sequential Recommendation via Review-Distilled Representations3h◆Equivariant Asynchronous Diffusion: An Adaptive Denoising Schedule for Accelerated Molecular Conformation Generation3h◆When AI Agents Learn from Each Other: Insights from Emergent AI Agent Communities on OpenClaw for Human-AI Partnership in Education3h◆SparseBalance: Load-Balanced Long Context Training with Dynamic Sparse Attention3h◆EuropeMedQA Study Protocol: A Multilingual, Multimodal Medical Examination Dataset for Language Model Evaluation3h◆VLAA-GUI: Knowing When to Stop, Recover, and Search, A Modular Framework for GUI Automation3h◆When Cow Urine Cures Constipation on YouTube: Limits of LLMs in Detecting Culture-specific Health Misinformation3h◆Consequentialist Objectives and Catastrophe3h◆EgoMAGIC- An Egocentric Video Field Medicine Dataset for Training Perception Algorithms3h◆ReCast: Recasting Learning Signals for Reinforcement Learning in Generative Recommendation3h◆A Probabilistic Framework for Hierarchical Goal Recognition3h◆CNSL-bench: Benchmarking the Sign Language Understanding Capabilities of MLLMs on Chinese National Sign Language3h◆The Shape of Adversarial Influence: Characterizing LLM Latent Spaces with Persistent Homology3h◆Toward Principled LLM Safety Testing: Solving the Jailbreak Oracle Problem3h◆Learning from Natural Language Feedback for Personalized Question Answering3h◆From Multi-Agent to Single-Agent: When Is Skill Distillation Beneficial?3h◆TS-Arena -- A Live Forecast Pre-Registration Platform3h◆Report for NSF Workshop on AI for Electronic Design Automation3h◆Initial results of the Digital Consciousness Model3h◆Rethinking Retrieval-Augmented Generation as a Cooperative Decision-Making Problem3h◆Sensory-Aware Sequential Recommendation via Review-Distilled Representations3h◆Equivariant Asynchronous Diffusion: An Adaptive Denoising Schedule for Accelerated Molecular Conformation Generation3h◆When AI Agents Learn from Each Other: Insights from Emergent AI Agent Communities on OpenClaw for Human-AI Partnership in Education3h◆SparseBalance: Load-Balanced Long Context Training with Dynamic Sparse Attention3h◆EuropeMedQA Study Protocol: A Multilingual, Multimodal Medical Examination Dataset for Language Model Evaluation3h◆VLAA-GUI: Knowing When to Stop, Recover, and Search, A Modular Framework for GUI Automation3h◆When Cow Urine Cures Constipation on YouTube: Limits of LLMs in Detecting Culture-specific Health Misinformation3h◆
