3 रिपॉजिटरी
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.
Explore 3 awesome GitHub repositories matching artificial intelligence & ml · Continuous Text Generation. Refine with filters or upvote what's useful.
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 is a tool for generating long-form fiction using large language models. It functions as a narrative architect and writing assistant, automating the creation of multi-chapter novels while managing the overall story structure and character tracking. The project distinguishes itself through a semantic context retrieval system and an AI story consistency checker. These tools use semantic search to recall specific story details from previous chapters and scan generated text for plot contradictions or behavioral inconsistencies. The system covers a full narrative lifecycle, inclu
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.