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README.md

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+---
+license: mit
+tags:
+- arc
+- abstract-reasoning
+- neural-network
+- transformer
+- pytorch
+library_name: pytorch
+pipeline_tag: other
+---
+
+# ARC Neural Network - Rule Paradigm
+
+This model is part of an Abstraction and Reasoning Corpus (ARC) neural network project focused on developing models capable of solving abstract reasoning challenges.
+
+## Model Information
+
+- **Checkpoint**: model_rule_paradigm_epoch_10_20250629_081422_best_val_loss.pth
+- **Type**: rule_paradigm
+- **Framework**: PyTorch
+- **Architecture**: Two-stage rule-imprinted attention with RuleGenerator and RuleApplier transformers
+
+## Training Details
+
+- **Epoch**: 10
+- **Loss**: 1.165186
+
+## Architecture Details
+
+### RuleGenerator Configuration
+- **Model Dimension**: 512
+- **Attention Heads**: N/A
+- **Encoder Layers**: N/A
+- **Rule Token Dimension**: 512
+
+### RuleApplier Configuration
+- **Model Dimension**: 512
+- **Attention Heads**: 16
+- **Rule Imprint Layers**: 3
+- **Spatial Layers**: 6
+
+## Usage
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+
+# Download the checkpoint
+checkpoint_path = hf_hub_download(
+    repo_id="artnoage/your-model-name",
+    filename="model.pth"
+)
+
+# Load the checkpoint
+checkpoint = torch.load(checkpoint_path, map_location='cpu')
+
+# The checkpoint contains model configurations for easy loading
+if 'rule_generator_config' in checkpoint:
+    # Model configs are included - can reconstruct architecture automatically
+    print("Model configurations found in checkpoint")
+    print(f"Epoch: {checkpoint['epoch']}, Loss: {checkpoint['loss']}")
+else:
+    # Legacy checkpoint - requires manual architecture specification
+    print("Legacy checkpoint - manual architecture specification needed")
+```
+
+## Project Overview
+
+This is part of an ARC (Abstraction and Reasoning Corpus) neural network project that implements a novel rule-based paradigm using a revolutionary two-stage rule-imprinted attention architecture.
+
+### Key Features
+- **Two-stage rule-imprinted attention**: Stage 1 for rule imprinting via cross-attention, Stage 2 for spatial reasoning via self-attention
+- **Rule consistency training**: Multiple rule extractions per task with consistency loss
+- **Configurable tokenization**: Row-based (30 tokens) or meta-pixel (900 tokens) strategies
+- **Mixed precision training**: AMP training with robust gradient scaling
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@misc{arc-neural-network,
+  title={ARC Neural Network with Rule-Imprinted Attention},
+  author={Your Name},
+  year={2025},
+  url={https://github.com/your-username/ARC_NN}
+}
+```
+
+## License
+
+MIT License - See repository for full license details.