Marco-DeepResearch-8B
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Explore AI models 25
Marco-DeepResearch-8B-GGUF
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View details ↗Marco-DeepResearch-8B-i1-GGUF
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View details ↗Marco-LLM-AR-V2
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View details ↗Marco-LLM-ES
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View details ↗Marco-Mini-Instruct
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View details ↗Marco-MT-Algharb
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View details ↗Marco-Nano-Instruct
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View details ↗Marco-o1
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View details ↗Ovis-Image-7B
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View details ↗Ovis-Omni-Embedding-3B
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View details ↗Ovis-VL-Embedding-2B
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View details ↗Ovis-VL-Embedding-9B
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View details ↗Ovis1.6-Gemma2-9B
ATH-MaaSWe are excited to announce the open-sourcing of **Ovis-1.6**, our latest multi-modal large language model. Ovis is a novel Multimodal Large Language Model (MLLM) architecture, designed to structurally align visual and textual embeddings. Built upon Ovis1.5, **Ovis1.6** further enhances high-resolution image processing, is trained on a larger, more diverse, and higher-quality dataset, and refines the training process with DPO training following instruction-tuning. With just **10B** parameters, **Ovis1.6-Gemma2-9B** leads the OpenCompass benchmark among open-source MLLMs within **30B** parameters.
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View details ↗Ovis1.6-Llama3.2-3B
ATH-MaaSWe are thrilled to announce the open-sourcing of **Ovis1.6-Llama3.2-3B**, an integral part of the Ovis1.6 family. This cutting-edge model currently sets the benchmark as the state-of-the-art (SOTA) solution for edge-side multimodal tasks. The Ovis family employs an innovative Multimodal Large Language Model (MLLM) architecture, specifically designed to structurally align visual and textual embeddings. Ovis1.6-Llama3.2-3B excels in common industry benchmarks, surpassing numerous open-source and proprietary multimodal models. Moreover, it is also particularly well-suited for local intelligence, on-device computing, and edge computing scenarios. Built upon Ovis1.5, **Ovis1.6** further enhances high-resolution image processing, is trained on a larger, more diverse, and higher-quality dataset, and refines the training process with DPO training following instruction-tuning.
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View details ↗Ovis2-1B
ATH-MaaSWe are pleased to announce the release of **Ovis2**, our latest advancement in multi-modal large language models (MLLMs). Ovis2 inherits the innovative architectural design of the Ovis series, aimed at structurally aligning visual and textual embeddings. As the successor to Ovis1.6, Ovis2 incorporates significant improvements in both dataset curation and training methodologies. We use VLMEvalKit, as employed in the OpenCompass multimodal and reasoning leaderboard, to evaluate Ovis2. Below is a code snippet demonstrating how to run Ovis with various input types. For additional usage instructions, including inference wrapper and Gradio UI, please refer to Ovis GitHub.
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View details ↗Ovis2-2B
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View details ↗Ovis2-4B
ATH-MaaSWe are pleased to announce the release of **Ovis2**, our latest advancement in multi-modal large language models (MLLMs). Ovis2 inherits the innovative architectural design of the Ovis series, aimed at structurally aligning visual and textual embeddings. As the successor to Ovis1.6, Ovis2 incorporates significant improvements in both dataset curation and training methodologies. We use VLMEvalKit, as employed in the OpenCompass multimodal and reasoning leaderboard, to evaluate Ovis2. Below is a code snippet demonstrating how to run Ovis with various input types. For additional usage instructions, including inference wrapper and Gradio UI, please refer to Ovis GitHub.
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View details ↗Ovis2.5-2B
ATH-MaaSWe are pleased to announce the release of **Ovis2.5**, the successor to Ovis2, designed for native-resolution visual perception and enhanced multimodal reasoning. It integrates a native-resolution vision transformer (NaViT) that processes images at their original, variable resolutions, eliminating the need for fixed-resolution tiling and preserving both fine details and global layout—crucial for visually dense content such as charts and diagrams. To strengthen reasoning, Ovis2.5 is trained not only on linear chain-of-thought (CoT) but also on reflective reasoning, including self-checking and revision. This advanced capability is available at inference as an optional *thinking mode*, enabling users to trade latency for higher accuracy on complex inputs. Building on these advances, **Ovis2.5-9B** achieves an average score of 78.3 on the OpenCompass multimodal evaluation suite (SOTA among open-source MLLMs under 40B parameters), while the lightweight **Ovis2.5-2B** scores 73.9, continuing the “small model, big performance” philosophy for resource-constrained scenarios. **Key Features** * **Native-Resolution Perception** — NaViT vision encoder preserves fine details and global structure without lossy tiling. * **Deep-Reasoning Capability** — Optional *thinking mode* for self-checking and revision beyond linear CoT. *Thinking budget* supported. * **Chart & Document OCR** — State-of-the-art at its scale for complex chart analysis, document understanding (including tables and forms), and OCR. * **Broad Task Coverage** — Demonstrates leading performance on image reasoning, video understanding, and grounding benchmarks, showcasing strong general multimodal capability.
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View details ↗Ovis2.5-9B
ATH-MaaSWe are pleased to announce the release of **Ovis2.5**, the successor to Ovis2, designed for native-resolution visual perception and enhanced multimodal reasoning. It integrates a native-resolution vision transformer (NaViT) that processes images at their original, variable resolutions, eliminating the need for fixed-resolution tiling and preserving both fine details and global layout—crucial for visually dense content such as charts and diagrams. To strengthen reasoning, Ovis2.5 is trained not only on linear chain-of-thought (CoT) but also on reflective reasoning, including self-checking and revision. This advanced capability is available at inference as an optional *thinking mode*, enabling users to trade latency for higher accuracy on complex inputs. Building on these advances, **Ovis2.5-9B** achieves an average score of 78.3 on the OpenCompass multimodal evaluation suite (SOTA among open-source MLLMs under 40B parameters), while the lightweight **Ovis2.5-2B** scores 73.9, continuing the “small model, big performance” philosophy for resource-constrained scenarios. **Key Features** * **Native-Resolution Perception** — NaViT vision encoder preserves fine details and global structure without lossy tiling. * **Deep-Reasoning Capability** — Optional *thinking mode* for self-checking and revision beyond linear CoT. *Thinking budget* supported. * **Chart & Document OCR** — State-of-the-art at its scale for complex chart analysis, document understanding (including tables and forms), and OCR. * **Broad Task Coverage** — Demonstrates leading performance on image reasoning, video understanding, and grounding benchmarks, showcasing strong general multimodal capability.
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View details ↗Ovis2.6-30B-A3B
ATH-MaaSWe introduce **Ovis2.6-30B-A3B**, the latest advancement in the Ovis series of Multimodal Large Language Models (MLLMs). Building on the strong foundation of Ovis2.5, Ovis2.6 upgrades the LLM backbone to a **Mixture-of-Experts (MoE)** architecture, delivering superior multimodal performance at a fraction of the serving cost. It also brings major improvements in long-context and high-resolution understanding, visual reasoning with active image analysis, and information-dense document comprehension. The following table presents a detailed performance comparison. Please note that superscripted results are sourced from external technical reports, and Qwen scores represent the highest value between its Think and Instruct versions. For quick reference, the best results are highlighted in red, and the second-best results are underlined. All values are rounded to one decimal place. The thinking and thinking budget logic can be applied in the same way for multi-image, video and pure text scenarios.
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View details ↗Ovis2-8B
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View details ↗OvisOCR
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View details ↗OvisOCR2
ATH-MaaSWe are pleased to announce the release of OvisOCR2, a compact 0.8B end-to-end model for page-level document parsing. Given a document page image, OvisOCR2 generates a Markdown representation in natural reading order, covering text, formulas, tables, and visual regions. OvisOCR2 is developed by post-training Qwen3.5-0.8B using a carefully designed data engine that combines real-world and synthetic data, together with a multi-stage training recipe integrating SFT, RL, and OPD. The model delivers strong document parsing performance while maintaining a small deployment footprint. OvisOCR2 achieves an overall score of 96.58 on OmniDocBench v1.6, establishing a new state of the art and **becoming the first end-to-end model to top this leaderboard previously dominated by pipeline methods**. On PureDocBench, OvisOCR2 also achieves the highest Avg3 score of 75.06.
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