A Multi-Loop Self-Stabilizing Language Model with Predictive Control Quick Start • Architecture • Commands • Technical Specification • Citation The complete theoretical framework, methodology, and reproducibility details for this model are documented in: Napolitano, L. M. (2025). Controlled Language Models: Decode-Time Behavioral Control and Token Efficiency._** Zenodo. https://doi.org/10.5281/zenodo.18344021 This paper should be cited for any academic or technical use of ARC-Base-8B-Condensed. ARC-Base-8B-Condensed is a fine-tuned version of Hermes-3-Llama-3.1-8B designed for: 1. Dense, information-rich responses — Reduced filler, hedging, and verbosity 2. Predictive behavioral control — CF-HoT heads detect and suppress failure modes before they manifest 3. Recursive self-improvement — Micro-training with automatic rollback on quality degradation 4. Mentor-based learning — Optional consultation with Claude API for continuous improvement — Research into self-improving language models — Applications requiring concise, direct responses — Study of representation engineering and behavioral control — Base for further fine-tuning experiments — Production deployment without evaluation -…
Модальности:
Генерация текста
Области применения:
Диалог / чат
Задача: Генерация текста
Автор: LoganResearch
Теги: llama, dense-responses, self-improvement, representation-engineering, cf-hot, recursive-self-improvement, conversational, en
Лайков: 4 | Загрузок: 17
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
