Delineate Anything v2: A Global Foundation Model for Field Delineation
Paper • 2607.19069 • Published • 5
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3513
Technical-Architectures-Large
Dataset: ajibawa-2023/Technical-Architectures-Large
This dataset provides over 210,000 distinct enterprise software architectures generated using two open source models: GPT-OSS-120B and Qwen3-Coder-Next-FP8.
These architectures model realistic enterprise systems complete with client layers, edge security, API gateways, service meshes, compliance boundaries, and multi-cloud infrastructure topologies.
Dataset: ajibawa-2023/Technical-Architectures-Large
This dataset provides over 210,000 distinct enterprise software architectures generated using two open source models: GPT-OSS-120B and Qwen3-Coder-Next-FP8.
These architectures model realistic enterprise systems complete with client layers, edge security, API gateways, service meshes, compliance boundaries, and multi-cloud infrastructure topologies.
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Shell-Code-Large
Dataset: ajibawa-2023/Shell-Code-Large
Shell-Code-Large is a large-scale corpus of Shell scripting source code comprising approximately 640,000 code samples stored in JSON Lines (.jsonl) format. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, DevOps automation, cloud infrastructure engineering, system administration, and software engineering automation.
By providing a high-volume, language-specific corpus focused exclusively on Shell scripting, Shell-Code-Large enables systematic experimentation in automation workflows, deployment pipelines, infrastructure management, and command-line tooling. These domains remain foundational to Linux systems, cloud-native platforms, CI/CD environments, and modern DevOps practices.
Shell-Code-Large addresses the need for a dedicated Shell-focused dataset at substantial scale, enabling targeted research into scripting patterns, command composition, workflow orchestration, infrastructure automation, and operational engineering practices
Dataset: ajibawa-2023/Shell-Code-Large
Shell-Code-Large is a large-scale corpus of Shell scripting source code comprising approximately 640,000 code samples stored in JSON Lines (.jsonl) format. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, DevOps automation, cloud infrastructure engineering, system administration, and software engineering automation.
By providing a high-volume, language-specific corpus focused exclusively on Shell scripting, Shell-Code-Large enables systematic experimentation in automation workflows, deployment pipelines, infrastructure management, and command-line tooling. These domains remain foundational to Linux systems, cloud-native platforms, CI/CD environments, and modern DevOps practices.
Shell-Code-Large addresses the need for a dedicated Shell-focused dataset at substantial scale, enabling targeted research into scripting patterns, command composition, workflow orchestration, infrastructure automation, and operational engineering practices
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🚀 VQAScore now supports text-to-video evaluation!
VQAScore scores how well a generated image or video matches a prompt by asking a VLM "does this show {prompt}?" and using P(Yes). It became a go-to evaluation metric and reward model for image generation (2M+ downloads), and we just added text-to-video support across 20+ VLMs (GPT, Gemini, Qwen). Free and open-source, and it keeps improving as VLMs improve.
💻 Code: https://github.com/linzhiqiu/t2v_metrics
📄 Paper: https://arxiv.org/abs/2404.01291
🧵 Launch thread + demo video: https://x.com/ZhiqiuLin/status/2064316582461841499
VQAScore scores how well a generated image or video matches a prompt by asking a VLM "does this show {prompt}?" and using P(Yes). It became a go-to evaluation metric and reward model for image generation (2M+ downloads), and we just added text-to-video support across 20+ VLMs (GPT, Gemini, Qwen). Free and open-source, and it keeps improving as VLMs improve.
💻 Code: https://github.com/linzhiqiu/t2v_metrics
📄 Paper: https://arxiv.org/abs/2404.01291
🧵 Launch thread + demo video: https://x.com/ZhiqiuLin/status/2064316582461841499
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