Transformers 发布: Release v5.13.0

来源摘要

# Release v5.13.0 ## New Model additions ### KimiK 2.5, 2.6, and 2.7 This release includes the architecture for Kimi 2.5 which is used by 2.5-2.7: Kimi K2.5 is an open-source, native multimodal agentic model that advances practical capabilities in long-horizon coding, coding-driven design, proactive autonomous execution, and swarm-based task orchestration. The model was proposed in [Kimi K2.5: Visual Agentic Intelligence](https://www.kimi.com/en/blog/kimi-k2-5) and further improved in [Kimi K2.6: Advancing Open-Source Coding](Kimi K2.5: Visual Agentic Intelligence). Kimi K2.5 achieves significant improvements on complex, end-to-end coding tasks, generalizing robustly across programming languages (Rust, Go, Python) and domains spanning front-end, DevOps, and performance optimization. The model is capable of transforming simple prompts and visual inputs into production-ready interfaces and lightweight full-stack workflows, generating structured layouts, interactive elements, and rich animations with deliberate aesthetic precision. **Links:** [Documentation](https://huggingface.co/docs/transformers/main/en/model_doc/kimi_k25) * Add new model: Kimi2-6 (#45630) by @zucchini-nlp in [#45630](https://github.com/huggingface/transformers/pull/45630) ### MiMo-V2-Flash **MiMo-V2-Flash** is a Mixture-of-Experts (MoE) language model developed by the Xiaomi MiMo team. Designed to establish a new balance between long-context modeling capabilities and inference efficiency, the model is built for strong performance in complex reasoning and agentic tasks. Trained on 27T tokens with native 32k sequence lengths, MiMo-V2-Flash seamlessly supports an extended **256K context window** while significantly reducing KV-cache storage compared to standard global attention models. **Links:** [Documen