Push model using huggingface_hub.

README.md

@@ -1,83 +1,9 @@
-# Skip-BART
-
-The description is generated by Grok3.
-
-## Model Details
-
-- **Model Name**: Skip-BART
-
-- **Model Type**: Transformer-based model (BART architecture) for automatic stage lighting control
-
-- **Version**: 1.0
-
-- **Release Date**: August 2025
-
-- **Developers**: Zijian Zhao, Dian Jin
-
-- **Organization**: HKUST, PolyU
-
-- **License**: Apache License 2.0
-
-- **Paper**: [Automatic Stage Lighting Control: Is it a Rule-Driven Process or Generative Task?](https://arxiv.org/abs/2506.01482)
-
-- **Citation:**
-
-  ```
-  @article{zhao2025automatic,
-    title={Automatic Stage Lighting Control: Is it a Rule-Driven Process or Generative Task?},
-    author={Zhao, Zijian and Jin, Dian and Zhou, Zijing and Zhang, Xiaoyu},
-    journal={arXiv preprint arXiv:2506.01482},
-    year={2025}
-  }
-  ```
-
-- **Contact**: [email protected]
-
-- **Repository**: https://github.com/RS2002/Skip-BART
-
-## Model Description
-
-Skip-BART is a transformer-based model built on the Bidirectional and Auto-Regressive Transformers (BART) architecture, designed for automatic stage lighting control. It generates lighting sequences synchronized with music input, treating stage lighting as a generative task. The model processes music data in an octuple format and outputs lighting control parameters, leveraging a skip-connection-enhanced BART structure for improved performance.
-
-- **Architecture**: BART with skip connections
-- **Input Format**: Encoder input (batch_size, length, 512), decoder input (batch_size, length, 2), attention masks (batch_size, length)
-- **Output Format**: Hidden states of dimension [batch_size, length, 1024]
-- **Hidden Size**: 1024
-- **Training Objective**: Pre-training on music data, followed by fine-tuning for lighting sequence generation
-- **Tasks Supported**: Stage lighting sequence generation
-
-## Training Data
-
-The model was trained on the **RPMC-L2** dataset:
-
-- **Dataset Source**: [RPMC-L2](https://zenodo.org/records/14854217?token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6IjM5MDcwY2E5LTY0MzUtNGZhZC04NzA4LTczMjNhNTZiOGZmYSIsImRhdGEiOnt9LCJyYW5kb20iOiI1YWRkZmNiMmYyOGNiYzI4ZWUxY2QwNTAyY2YxNTY4ZiJ9.0Jr6GYfyyn02F96eVpkjOtcE-MM1wt-_ctOshdNGMUyUKI15-9Rfp9VF30_hYOTqv_9lLj-7Wj0qGyR3p9cA5w)
-- **Description**: Contains music and corresponding stage lighting data in a format suitable for training Skip-BART.
-- **Details**: Refer to the [paper](https://arxiv.org/abs/2506.01482) for dataset specifics.
-
-## Usage
-
-### Installation
-
-```shell
-git clone https://huggingface.co/RS2002/Skip-BART
-```
-
-### Example Code
-
-```python
-import torch
-from model import Skip_BART
-
-# Load the model
-model = Skip_BART.from_pretrained("RS2002/Skip-BART")
-
-# Example input
-x_encoder = torch.rand((2, 1024, 512))
-x_decoder = torch.randint(0, 10, (2, 1024, 2))
-encoder_attention_mask = torch.zeros((2, 1024))
-decoder_attention_mask = torch.zeros((2, 1024))
-
-# Forward pass
-output = model(x_encoder, x_decoder, encoder_attention_mask, decoder_attention_mask)
-print(output.last_hidden_state.size())  # Output: [2, 1024, 1024]
-```
+---
+tags:
+- model_hub_mixin
+- pytorch_model_hub_mixin
+---
+
+This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
+- Library: [More Information Needed]
+- Docs: [More Information Needed]

config.json

@@ -0,0 +1,12 @@
+{
+  "class_num": [
+    180,
+    256
+  ],
+  "ffn_dims": 2048,
+  "heads": 8,
+  "hidden_size": 1024,
+  "layers": 8,
+  "max_position_embeddings": 1024,
+  "pretrain": false
+}

model.safetensors

@@ -0,0 +1,3 @@
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+size 894159240