Publish weights and concise inference instructions

README.md

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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.

SHA256SUMS

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+38371710932738bafab6c74bfee32f05acfcc2e19a1c1b016965d09f778cbe8f  model.pt
+74784586a33bfe12ab9e8549ca3b90088412cdd6fe3c3b6a72470c6d2b52fe7a  README.md

model.pt

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