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lingbot-video-moe-30b-a3b

We are excited to introduce **LingBot-Video**, the first open-source large-scale MoE (Mixture-of-Experts) video generation model dedicated to embodied intelligence. As a top-tier video model, LingBot-Video is designed to bridge the gap between video synthesis and physical world understanding. > **๐Ÿ’ก Rewriter deployment**: the bundled rewriter uses the single-process > `transformers` backend. For higher throughput, deploy the VLM yourself and call > it through an OpenAI-compatible API. Preserve the two-stage semantics: step 1 > must use the base VLM without the rewriter LoRA, while step 2 must use the same > base VLM with the rewriter LoRA enabled. This can be implemented with two > endpoints, or with one server that can select the adapter per request. See > vLLM / SGLang official docs. Install the optional SGLang dependencies only when using SGLang Diffusion or the fused / FP8 MoE runtime:

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We are excited to introduce **LingBot-Video**, the first open-source large-scale MoE (Mixture-of-Experts) video generation model dedicated to embodied intelligence. As a top-tier video model, LingBot-Video is designed to bridge the gap between video synthesis and physical world understanding. > **๐Ÿ’ก Rewriter deployment**: the bundled rewriter uses the single-process > `transformers` backend. For higher throughput, deploy the VLM yourself and call > it through an OpenAI-compatible API. Preserve the two-stage semantics: step 1 > must use the base VLM without the rewriter LoRA, while step 2 must use the same > base VLM with the rewriter LoRA enabled. This can be implemented with two > endpoints, or with one server that can select the adapter per request. See > vLLM / SGLang official docs. Install the optional SGLang dependencies only when using SGLang Diffusion or the fused / FP8 MoE runtime:

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