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AI Models 84759

• aya-vision-8b↗

  unsloth

• MiniMax-H3-comfyUI-GGUF↗

  vantagewithai

  MiniMax H3 is a general-purpose, omni-modal generative system. It supports unified understanding of multimodal contexts composed of text, images, video, and audio, and can generate video with native stereo audio at resolutions up to 2K and durations of up to 15 seconds. Thanks to its task-generalization-oriented system design, H3 already possesses broad multimodal context understanding and generation capabilities at the pre-training stage, enabling outstanding performance in following complex multimodal instructions. The complete H3 system consists of the following three modules: - H3-Context-IR: As inputs become increasingly complex, we build a dedicated system to deeply understand and refine the input multimodal instructions, then convert them into a form that H3 can readily understand—the Context Intermediate Representation—for generation. **H3-Context-IR is critical to the quality of the final output, so we strongly recommend incorporating it into your generation pipeline or following the “Prompting Guidance” to build your own context-processing system.** - H3-Base: Generates audio and video based on the H3-Context-IR output, producing results at 768p resolution. - H3-Regenerate-2K: Feeds the 768p result together with the original context back into H3 to regenerate the output at 2K resolution. This process leverages both H3’s powerful generative capabilities and the rich information contained in the original context, enabling it to produce high-resolution outputs with more accurate details and greater visual fidelity. H3\-Context\-IR is a hosted preprocessing and orchestration system designed for free\-form multimodal inputs\.

• Wan2.2-I2V-A14B-Diffusers↗

  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: - 👍 **Effective MoE Architecture**: Wan2.2 introduces a Mixture-of-Experts (MoE) architecture into video diffusion models. By separating the denoising process cross timesteps with specialized powerful expert models, this enlarges the overall model capacity while maintaining the same computational cost.

• Qwen3.5-9B-Uncensored-GGUF↗

  xCloudinfo

• Qwen3-7B-Instruct-Q4_K_M-GGUF↗

  Ygz-08123

• bart-large-snli_mnli_fever_anli_R1_R2_R3-nli↗

  ynie

• c67-h30↗

  aalv2

• qwq-plus↗

  阿里巴巴 · Qwen

• InstantMeshGGuf↗

  Asher-1

• tiny-random-NemotronForCausalLM↗

  badaoui

  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).