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README.md

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+
+            ---
+            library_name: custom
+            tags:
+            - robotics
+            - diffusion
+            - mixture-of-experts
+            - multi-modal
+            license: mit
+            datasets:
+            - CALVIN
+            language:
+            - en
+            pipeline_tag: robotics
+            ---
+            # MoDE (Mixture 1of Diffusion Experts) Model
+
+            This model implements a Mixture of Diffusion Experts architecture for robotic manipulation, combining transformer-based processing with expert routing and diffusion-based action prediction.
+
+            ## Model Architecture
+            - Base Architecture: MoDE with custom Mixture of Experts Transformer
+            - Vision Encoder: {getattr(model_instance, 'resnet_type', 'ResNet')} with FiLM conditioning
+            - EMA: Enabled
+            - Action Window Size: {model_instance.act_window_size}
+            - Sampling Steps: {model_instance.num_sampling_steps}
+            - Sampler Type: {model_instance.sampler_type}
+
+            ## Input/Output Specifications
+            - RGB Static Camera: (B, T, 3, H, W) tensor
+            - RGB Gripper Camera: (B, T, 3, H, W) tensor
+            - Language Instructions: Text strings
+            - Output: (B, T, 7) tensor representing 7-DoF actions
+
+            ## Usage Example
+            ```python
+            from huggingface_hub import hf_hub_download
+            import torch
+
+            weights_path = hf_hub_download(repo_id="{repo_name}", filename="model_cleaned.safetensors")
+            model.load_pretrained_parameters(weights_path)
+
+            obs = {
+                "rgb_obs": {
+                    "rgb_static": static_image,
+                    "rgb_gripper": gripper_image
+                }
+            }
+            goal = {"lang_text": "pick up the blue cube"}
+            action = model.step(obs, goal)
+            ```
+
+            ## Training Configuration
+            - Optimizer: AdamW
+            - Learning Rate: {config.optimizer.learning_rate}
+            - Weight Decay: {config.optimizer.transformer_weight_decay}
+            

model_cleaned.safetensors

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