Twinkle团队 投稿 量子位 | 公众号 QbitAI 大模型后训练的"易用性"与"灵活性"，真没法兼得？ ModelScope 团队最新开源的 Twinkle✨ 框架，给出了一条新路径。 它采用Client-Server架构，目前已支持包括Dataset、Model、Sampler的20余种算法组件。开发者可以用约150行代码，像写本地PyTorch 一样编排复杂的RL训练循环，同时底层调度、资源分配全交给框架。 要充分挖掘模型在各类场景下的应用潜力，针对性的训练微调至关重要。不可否认，以强化学习 （RL） 为代表的后训练范式，是模型生命周 期中复杂度最高的环节之一：其实现方式高度定制化，难以通用；组件耦合度高，导致源码层面的理解门槛极高；此外，多模型协作的架构也 极大地增加了代码编写的难度。 除了OpenAI提供的"数据进，模型出"的黑盒训练模式外，业界开源训练框架大致可分为两类： 通用型训练框架 ：以LLaMA-Factory和ms-swift为代表。这类框架基于Transformers和TRL的Trainer开发，深度适配safetensors模型生 态，用户通常通过命令行配置来快速启动训练。 定 ...

Core Insights - The narrative around women's influence has fundamentally changed over the past year, showcasing women as powerful figures in various fields rather than seeking empowerment from others [1][38]. Part 1: The World Modeler - Fei-Fei Li, founder of World Labs, has focused on "Spatial Intelligence," launching the Marble product, which creates high-fidelity 3D worlds from images, videos, or text prompts [1][2][3]. Part 2: The Pain Translator - Han Kang, a Nobel laureate, sparked global discussions on "female bodily sovereignty" and "historical trauma" with her works, including "The Vegetarian" and "The White Book," which became bestsellers [5][7]. Part 3: The Gold Standard - Caitlin Clark, a WNBA star, doubled viewership and sponsorship fees, proving that female athletes can generate significant commercial value when given equal exposure [11][13]. - Qinwen Zheng, a tennis champion, became a global brand ambassador for Dior and earned $22.6 million in 2025, with 93% from endorsements, redefining the public image of East Asian female athletes [13][17]. Part 4: The Heritage Hacker - Zong Fuli, president of Hongsheng Beverage Group, undertook digital reforms and brand rejuvenation, applying for a new trademark "Wawa Xiaozong" to establish her own identity separate from her father's legacy [14][16][17]. Part 5: The AI Ethicist - Mira Murati, former CTO of OpenAI, founded Thinking Machines Lab with a $12 billion valuation, focusing on creating safer and more reliable AI systems, addressing the gap in public understanding of AI [18][20][21]. Part 6: The Invisible Heroine - Female data annotators in rural China are crucial in training AI models, providing stable income and connecting with modern technology, thus becoming visible contributors to the AI evolution [22][24]. Part 7: The Strategy Sovereign - Meng Wanzhou, rotating chairwoman of Huawei, shifted the company's focus from survival to leadership in AI, achieving significant milestones in various sectors, including the Harmony ecosystem and AI computing [25][27][28]. Part 8: The Grassroots Healer - Dr. Lu Shengmei, a pediatrician, has dedicated her life to serving the community, significantly reducing infant mortality rates and becoming a symbol of enduring value in a rapidly changing world [30][31]. Part 9: The Supply Chain Queen - Wang Laichun, chairwoman of Luxshare Precision, transformed the company from a traditional manufacturer to a technology platform, focusing on high-precision manufacturing and expanding into new markets [32][33][34]. Part 10: The Wilderness Chronicler - Li Juan, author of "My Altay," received the 2025 China Copyright Golden Award, solidifying her status as a literary figure who connects individual souls with nature, providing a counter-narrative to modern anxieties [35][37].

Core Insights - Thinking Machines has launched Tinker, an API designed for fine-tuning open-weight language models, aimed at reducing infrastructure costs for developers [2][5] - Tinker supports various model architectures, allowing developers to fine-tune models with simple Python code modifications [2][3] - The platform integrates LoRA to enhance GPU memory utilization during parallel fine-tuning, making it practical for research teams with limited resources [2] Summary by Sections Tinker API - Tinker provides managed scheduling, GPU allocation, and checkpoint handling, abstracting cluster management for developers [2] - It offers low-level primitives like forward_backward and sample, enabling developers to create new methods without managing infrastructure [3] Tinker Cookbook - The Tinker Cookbook is an open-source repository that implements common fine-tuning techniques, including reinforcement learning methods and preference optimization workflows [3] - Early users from prestigious institutions have applied Tinker to tasks such as theorem proving and multi-agent reinforcement learning [3] Community Feedback - Initial community feedback highlights a balance between flexibility and simplicity, with professionals noting that RLaaS (Reinforcement Learning as a Service) addresses a significant gap for enterprises [4] Founder Insights - The founder of Thinking Machines emphasizes that Tinker provides cutting-edge tools for researchers, simplifying the complexity of distributed training while supporting innovative research and model customization [5] - Tinker is currently in closed testing, with early access being free and a pay-per-use model planned for the future [5]