Load
model.pt
with the matching Long-WAM runtime, using
config.yaml
and
the supplied
dataset_stats.json
for preprocessing and normalization.
P48 context uses 48 past control steps plus the current observation, encoded into four clean latent frames.
Generate two future-video latents and a 32-step action chunk together using 4 joint denoising steps (sigma 1 to 0).
Replan every 24 executed actions. Reset history at the start of each episode.
The matching inference runtime and base-model VAE/text assets are required
separately. This release contains weights and inference settings only;
optimizer states and training artifacts are not included.
Runs of Efficient-Large-Model Long-WAM-RoboTwin2.0-COD on huggingface.co
52
Total runs
0
24-hour runs
3
3-day runs
13
7-day runs
13
30-day runs
More Information About Long-WAM-RoboTwin2.0-COD huggingface.co Model
Long-WAM-RoboTwin2.0-COD huggingface.co is an AI model on huggingface.co that provides Long-WAM-RoboTwin2.0-COD's model effect (), which can be used instantly with this Efficient-Large-Model Long-WAM-RoboTwin2.0-COD model. huggingface.co supports a free trial of the Long-WAM-RoboTwin2.0-COD model, and also provides paid use of the Long-WAM-RoboTwin2.0-COD. Support call Long-WAM-RoboTwin2.0-COD model through api, including Node.js, Python, http.
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Long-WAM-RoboTwin2.0-COD is an open source model from GitHub that offers a free installation service, and any user can find Long-WAM-RoboTwin2.0-COD on GitHub to install. At the same time, huggingface.co provides the effect of Long-WAM-RoboTwin2.0-COD install, users can directly use Long-WAM-RoboTwin2.0-COD installed effect in huggingface.co for debugging and trial. It also supports api for free installation.
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