3 Repos
Flow-matching architectures specifically designed to generate text via continuous embeddings.
Distinct from Flow-Matching Frameworks: Specializes flow-matching for text-token generation rather than the common image-based application.
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This project is a machine learning research automation system designed to manage the full research lifecycle, from idea discovery to final paper submission. It utilizes markdown-based skill templates to execute autonomous research tasks and manage iterative loops of deep review and experimentation. The system distinguishes itself through integrated capabilities for academic communication and integrity auditing. It can automate the generation of LaTeX papers, conference slide decks, and evidence-grounded peer review rebuttals. To ensure rigor, it employs cross-model review routing and adversar
Transforms noise into clean embeddings using flow matching for continuous text generation.
AI NovelGenerator ist ein Tool zur Generierung von Belletristik in Romanlänge unter Verwendung von Large Language Models. Es fungiert als narrativer Architekt und Schreibassistent, der die Erstellung von Romanen mit mehreren Kapiteln automatisiert, während er die gesamte Story-Struktur und das Charakter-Tracking verwaltet. Das Projekt zeichnet sich durch ein semantisches Kontext-Retrieval-System und einen KI-Story-Konsistenz-Checker aus. Diese Tools nutzen semantische Suche, um spezifische Story-Details aus vorherigen Kapiteln abzurufen und generierten Text auf Plot-Widersprüche oder Verhaltensinkonsistenzen zu scannen. Das System deckt den gesamten narrativen Lebenszyklus ab, einschließlich des Entwurfs des Story-Fundaments, Worldbuilding und der Planung der Romanstruktur. Es nutzt eine mehrstufige Pipeline zum Entwerfen kohärenter Kapitel und integriert eine kreative Workflow-Workbench zur Verwaltung von Einstellungen und Korrekturlesen.
Provides automated scanning of generated text to identify logical plot contradictions and character inconsistencies.
ExecuTorch is a lightweight C++ runtime for deploying PyTorch models on mobile, embedded, and edge hardware. It provides an ahead-of-time compilation pipeline that exports, quantizes, and lowers model graphs into compact serialized programs, then executes them through a minimal runtime with hardware acceleration and on-device large language model inference capabilities. The project distinguishes itself through a hardware accelerator delegate system that partitions model subgraphs and offloads computation to specialized backends including NPUs, GPUs, and DSPs from Apple, Arm, Intel, MediaTek,
ExecuTorch continues text generation from a specific point in the cache, enabling stateful continuation.