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README.md
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## π Model Zoo
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We provide multiple pretrained variants of PlanPhys models:
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| Model | Description |
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| πΉ [PlanPhys-Model-Pre-Training-Random](https://huggingface.co/MultimodalAgent/PlanPhys-Model-Pre-Training-Random) | Baseline model pretrained with random data sampling strategy |
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| πΉ [PlanPhys-Model-Pre-Training-Scaling](https://huggingface.co/MultimodalAgent/PlanPhys-Model-Pre-Training-Scaling) | Scaled-up pretraining model with larger training resources and data |
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| πΉ [PlanPhys-Model-Pre-Training-Distribution](https://huggingface.co/MultimodalAgent/PlanPhys-Model-Pre-Training-Distribution) | Model optimized with improved data distribution strategies |
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| πΉ [PlanPhys-Model-Pre-Training-Ensemble](https://huggingface.co/MultimodalAgent/PlanPhys-Model-Pre-Training-Ensemble) | Ensemble model combining multiple pretrained representations |
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## π Overview
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PlanPhys investigates how large-scale pretraining strategies influence the learning of physical knowledge.
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## π οΈ Usage
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- Configuration files
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- Training details
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- Inference examples
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## π Related Resources
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| Resource | Link |
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| Random Pretraining | https://huggingface.co/MultimodalAgent/PlanPhys-Model-Pre-Training-Random |
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| Scaling Study | https://huggingface.co/MultimodalAgent/PlanPhys-Model-Pre-Training-Scaling |
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| Distribution Study | https://huggingface.co/MultimodalAgent/PlanPhys-Model-Pre-Training-Distribution |
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| Ensemble Model | https://huggingface.co/MultimodalAgent/PlanPhys-Model-Pre-Training-Ensemble |
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## π Citation
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## π Overview
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PlanPhys investigates how large-scale pretraining strategies influence the learning of physical knowledge.
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## π Model Zoo
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We provide multiple pretrained variants of PlanPhys models:
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| Model | Description |
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|---|---|
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| πΉ [PlanPhys-Model-Pre-Training-Random](https://huggingface.co/MultimodalAgent/PlanPhys-Model-Pre-Training-Random) | Baseline model pretrained with random data sampling strategy |
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| πΉ [PlanPhys-Model-Pre-Training-Scaling](https://huggingface.co/MultimodalAgent/PlanPhys-Model-Pre-Training-Scaling) | Scaled-up pretraining model with larger training resources and data |
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| πΉ [PlanPhys-Model-Pre-Training-Distribution](https://huggingface.co/MultimodalAgent/PlanPhys-Model-Pre-Training-Distribution) | Model optimized with improved data distribution strategies |
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| πΉ [PlanPhys-Model-Pre-Training-Ensemble](https://huggingface.co/MultimodalAgent/PlanPhys-Model-Pre-Training-Ensemble) | Ensemble model combining multiple pretrained representations |
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## π Citation
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