AI & ML interests

Ontology + Concordance: The meeting of meaning

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felfri authored a paper 23 days ago

No Safe Dose: How Training Data Drives Unsafe Image Generation

PSaiml authored a paper 23 days ago

No Safe Dose: How Training Data Drives Unsafe Image Generation

felfri authored a paper about 1 month ago

LLMs Gaming Verifiers: RLVR can Lead to Reward Hacking

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Papers

MixtureVitae: Open Web-Scale Pretraining Dataset With High Quality Instruction and Reasoning Data Built from Permissive-First Text Sources

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ajibawa-2023

posted an update 7 days ago

Post

3505

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.

felfri

authored a paper 23 days ago

No Safe Dose: How Training Data Drives Unsafe Image Generation

Paper • 2605.28137 • Published May 27 • 2

PSaiml

authored a paper 23 days ago

No Safe Dose: How Training Data Drives Unsafe Image Generation

Paper • 2605.28137 • Published May 27 • 2

ajibawa-2023

posted an update about 1 month ago

Post

6922

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