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license: other
library_name: pytorch
pipeline_tag: image-to-video
tags: [longlive-2.0, robotics, autoregressive]
---
# LongLive 2.0 Robot-M
Autoregressive robot-video generation weights.
M is trained on a mixture of short and medium-length sequences.
S and M describe training sequence length, not model size.
## Download
```bash
hf download Efficient-Large-Model/LongLive2.0-Robot-M --local-dir ./weights/LongLive2.0-Robot-M
```
## Inference
Instantiate the matching LongLive 2.0 generator, then load the FP32 weights:
```python
import torch
state_dict = torch.load("model.pt", map_location="cpu", mmap=True, weights_only=True)
model.load_state_dict(state_dict, strict=True)
```
Run the loading example from the downloaded directory. The matching inference
runtime, VAE and text encoder are required separately; this is not a standalone
pipeline. Optimizer and other training states are not included.
Original upstream model terms continue to apply.
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