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AI モデル 84759

• Wan2.2-T2V-A14B↗

  Wan-AI

  💜 Wan &nbsp&nbsp ｜ &nbsp&nbsp 🖥️ GitHub &nbsp&nbsp | &nbsp&nbsp🤗 Hugging Face &nbsp&nbsp | &nbsp&nbsp🤖 ModelScope &nbsp&nbsp | &nbsp&nbsp 📑 Technical Report &nbsp&nbsp | &nbsp&nbsp 📑 Blog &nbsp&nbsp | &nbsp&nbsp💬 WeChat Group &nbsp&nbsp | &nbsp&nbsp 📖 Discord &nbsp&nbsp We are excited to introduce **Wan2.2**, a major upgrade to our foundational video models. With **Wan2.2**, we have focused on incorporating the following innovations: This repository contains our T2V-A14B model, which supports generating 5s videos at both 480P and 720P resolutions. Built with a Mixture-of-Experts (MoE) architecture, it delivers outstanding video generation quality. On our new benchmark Wan-Bench 2.0, the model surpasses leading commercial models across most key evaluation dimensions.

• c67-h17↗

  wandb09

• wavenet-parametric-2400-speaker-overlapping↗

  wq2012

• Xing4.0-29B-Intent↗

  XingChen-AGI

  For full project documentation, deployment instructions, SDK usage, and data details, visit the GitHub repository. **Xing4.0-29B-Intent** is a **schema-guided model for intent recognition and structured information extraction**, developed from Xing4.0-29B-A4B, the Xing semantic large language model from China Telecom Artificial Intelligence Technology Co., Ltd. It converts natural language into structured results for business systems, supporting intelligent assistants, request routing, and document information extraction. For example, a request such as “Find flights from Beijing to Shanghai tomorrow” needs to be parsed into a flight-search intent and parameters such as origin, destination, and date. Xing4.0-29B-Intent performs recognition and extraction according to the supplied category and field definitions, returning results in a unified JSON format.

• YandexGPT-5-Lite-8B-instruct-GGUF↗

  yandex

  Квантизованная версия YandexGPT 5 Lite 8B Instruct. Информация о модели доступна в основном репозитории: YandexGPT-5-Lite-8B-instruct. **UPD**: Мы обновили `.gguf` файл в изначальном репозитории на наиболее близкий по качеству к оригинальной модели. Мы используем нестандартный шаблон диалога — модель обучена генерировать только одну реплику после последовательности `Ассистент:[SEP]`, завершая её токеном ` `. При этом диалог в промпте может быть любой длины.

• Smol-Llama-101M-Chat-v1-GGUF↗

  afrideva

• OLMo-7B-0424-hf-Quantized↗

  aifoundry-org

• albert-large-v2↗

  albert

• Qwen2D-Anime-VAE↗

  Anzhc

  Reduces oversharpening artifacts, can help with small features, like pupils (even in lowres), as well as overall reduction in noise.

• vjepa2.1-vit-base-384↗

  apiantonio