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Transformers
Safetensors
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prts_qwen3_vl
feature-extraction
vision-language-action
vla
contrastive-reinforcement-learning
goal-conditioned-rl
qwen3-vl
prts
custom_code
Instructions to use TeleEmbodied/PRTS-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TeleEmbodied/PRTS-4B with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TeleEmbodied/PRTS-4B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -90,7 +90,6 @@ PRTS expects a **single user turn** containing camera images, a discretized prop
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| `<\|im_start\|>` `<\|im_end\|>` | Qwen-style turn delimiters |
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| `<\|vision_start\|>` `<\|image_pad\|>` `<\|vision_end\|>` | One image placeholder block per camera |
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| `<\|goal_repr\|>` | CRL value-head anchor tokens |
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| `<\|action_start\|>` `<\|action_pad\|>` `<\|action_end\|>` | Slot the action expert fills with the predicted action chunk token |
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### Layout of one rollout step
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| `<\|im_start\|>` `<\|im_end\|>` | Qwen-style turn delimiters |
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| `<\|vision_start\|>` `<\|image_pad\|>` `<\|vision_end\|>` | One image placeholder block per camera |
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| `<\|goal_repr\|>` | CRL value-head anchor tokens |
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### Layout of one rollout step
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