The proliferation of Large Language Models (LLMs) has led to a burgeoning ecosystem of specialized, domain-specific models. While this rapid growth accelerates innovation, it has simultaneously created significant challenges in model discovery and adoption. Users struggle to navigate this landscape due to inconsistent, incomplete, and imbalanced documentation across platforms. Existing documentation frameworks, such as Model Cards and FactSheets, attempt to standardize reporting but are often static, predominantly qualitative, and lack the quantitative mechanisms needed for rigorous cross-model comparison. This gap exacerbates model underutilization and hinders responsible adoption. To address these shortcomings, we introduce the Comprehensive Responsible AI Model Card Framework (CRAI-MCF), a novel approach that transitions from static disclosures to actionable, human-aligned documentation. Grounded in Value Sensitive Design (VSD), CRAI-MCF is built upon an empirical analysis of 240 open-source projects, distilling 217 parameters into an eight-module, value-aligned architecture. Our framework introduces a quantitative sufficiency criterion to operationalize evaluation and enables rigorous cross-model comparison under a unified scheme. By balancing technical, ethical, and operational dimensions, CRAI-MCF empowers practitioners to efficiently assess, select, and adopt LLMs with greater confidence and operational integrity.

翻译：大型语言模型（LLM）的激增催生了大量专业化、领域特定模型的生态系统。虽然这种快速增长加速了创新，但同时也给模型的发现与采用带来了重大挑战。由于各平台文档存在不一致、不完整和失衡的问题，用户难以有效驾驭这一复杂局面。现有的文档框架（如模型卡和事实说明书）试图标准化报告内容，但通常呈现为静态、以定性描述为主的形式，缺乏进行严格跨模型比较所需的量化机制。这一缺陷加剧了模型利用率不足的问题，并阻碍了负责任的模型采用。为应对这些不足，我们提出了全面负责任人工智能模型卡框架（CRAI-MCF），这是一种从静态披露转向可操作、人机对齐文档的新方法。该框架以价值敏感设计（VSD）为理论基础，通过对240个开源项目的实证分析，将217项参数提炼为包含八个模块、价值对齐的架构。我们的框架引入了量化充分性准则以实现评估的可操作化，并支持在统一方案下进行严格的跨模型比较。通过平衡技术、伦理与操作维度，CRAI-MCF使从业者能够以更高的置信度和操作完整性，有效评估、选择并采用大型语言模型。