> ## Documentation Index
> Fetch the complete documentation index at: https://docs.sglang.io/llms.txt
> Use this file to discover all available pages before exploring further.

# LingBot Video MoE

> Serve the native LingBot Video MoE 30B-A3B text-to-video model with SGLang Diffusion.

export const DiffusionModelTags = ({tags = []}) => {
  const normalizedTags = Array.isArray(tags) ? tags : [tags];
  return <div className="not-prose sgd-model-tags">
      {normalizedTags.map(tag => <span key={tag} className="sgd-chip">
          {tag}
        </span>)}
    </div>;
};

<DiffusionModelTags tags={["video", "text-to-video", "mixture-of-experts"]} />

## 1. Model introduction

[LingBot Video MoE 30B-A3B](https://huggingface.co/robbyant/lingbot-video-moe-30b-a3b)
is a text-to-video mixture-of-experts model. SGLang Diffusion provides a native
pipeline for the public checkpoint:

| Model ID                             | Task          | Default output               |
| ------------------------------------ | ------------- | ---------------------------- |
| `robbyant/lingbot-video-moe-30b-a3b` | Text to video | 480x480, 81 frames at 16 FPS |

The checkpoint expects a structured JSON caption rather than an unexpanded
natural-language prompt. The JSON is passed as the request's `prompt` string;
it is not an `extra_params` object.

## 2. Installation

Install SGLang with the diffusion dependencies:

```bash Command theme={null}
uv pip install "sglang[diffusion]" --prerelease=allow
```

See the [SGLang Diffusion installation guide](/docs/sglang-diffusion/installation)
for platform-specific setup.

## 3. Serve LingBot Video MoE

Start the server with the Hugging Face model ID:

```bash Command theme={null}
sglang serve \
  --model-path robbyant/lingbot-video-moe-30b-a3b \
  --port 30010
```

## 4. Generate a video

The following request uses the compact 17-frame, 12-step smoke-test profile.
Use the model defaults of 81 frames and 40 steps for the released generation
profile.

```python Python theme={null}
import json
import time
from pathlib import Path

import requests

base_url = "http://127.0.0.1:30010"
prompt = json.dumps(
    {
        "comprehensive_description": {
            "scene_content_description": (
                "A small silver robot arm on a white table slowly reaches "
                "toward a red cube. The background is a softly lit laboratory wall."
            ),
            "camera_movement_description": (
                "The camera is static at eye level in a medium shot."
            ),
        },
        "camera_info": {
            "color": "Neutral",
            "frame_size": "Medium",
            "shot_type_angle": "Eye level",
            "lens_size": "Medium",
            "composition": "Center",
            "lighting": "Soft light",
            "lighting_type": "Artificial light",
        },
        "world_knowledge": [],
        "prominent_elements": [
            {
                "name": "robot arm",
                "description": "A small silver robot arm with a two-finger gripper.",
                "actions": [
                    {
                        "timestamp": "[0.0s - 1.0s]",
                        "action": "reaches toward the red cube",
                    }
                ],
                "location": "center of the frame",
                "relative_size": "dominant",
                "shape_and_color": "articulated silver metal arm",
                "texture": "brushed metal",
                "appearance_details": "two-finger gripper and visible joints",
                "relationship": "reaching toward the red cube on the table",
                "orientation": "upright, base on the table",
                "pose": "reaching",
            }
        ],
    },
    separators=(",", ":"),
)

response = requests.post(
    f"{base_url}/v1/videos",
    json={
        "model": "robbyant/lingbot-video-moe-30b-a3b",
        "prompt": prompt,
        "size": "640x384",
        "num_frames": 17,
        "fps": 16,
        "num_inference_steps": 12,
        "guidance_scale": 6.0,
        "flow_shift": 3.0,
        "seed": 0,
    },
    timeout=60,
)
response.raise_for_status()
video_id = response.json()["id"]

while True:
    job = requests.get(f"{base_url}/v1/videos/{video_id}", timeout=30).json()
    if job["status"] == "completed":
        break
    if job["status"] == "failed":
        raise RuntimeError(job.get("error") or "Video generation failed")
    time.sleep(1)

video = requests.get(
    f"{base_url}/v1/videos/{video_id}/content",
    timeout=300,
)
video.raise_for_status()
Path("lingbot_video_moe.mp4").write_bytes(video.content)
```

## 5. Request constraints

* `num_frames` must be `1` or `4n+1`; examples include 17 and 81.
* Width and height must both be multiples of 16.
* The native defaults are `guidance_scale=6.0`, `flow_shift=3.0`,
  `num_inference_steps=40`, and `fps=16`.
* Keep the prompt as serialized JSON. Raw free text is outside the
  checkpoint's expected caption format.