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--- |
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base_model: t5-small |
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license: apache-2.0 |
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datasets: |
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- open-web-math/open-web-math |
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tags: |
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- text-generation |
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- causal-lm |
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- mamba |
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- hrm |
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- pytorch |
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language: |
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- en |
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pipeline_tag: text-generation |
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--- |
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# CMBA-768M-OpenWebMath |
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A 768M parameter Hierarchical Recurrent Memory (HRM) language model trained on high-quality math web text from OpenWebMath. This model uses **Mamba2 state-space models** instead of traditional attention mechanisms, enabling efficient long-range sequence modeling. |
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## Model Architecture |
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**CMBA** (Causal Mamba-based Architecture) implements a hierarchical processing structure: |
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- **Hierarchical Design**: Dual-level processing with H-layers (high-level abstraction) and L-layers (low-level specialists) |
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- **Mamba2 Mixers**: State-space models replace attention for O(n) complexity vs O(n²) |
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- **Adaptive Computation**: Halting mechanism allows variable compute per token (ACT-style pondering) |
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- **Parameters**: ~768M total |
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- **Context Length**: 1024 tokens |
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### Configuration |
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```python |
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Model Dimensions: |
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- d_model: 1024 |
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- n_heads: 16 (for compatibility, not used in Mamba) |
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- d_ff: 4096 |
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- H_layers: 12 (high-level hierarchy) |
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- L_layers: 12 (low-level processing) |
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Mamba2 Settings: |
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- d_state: 128 |
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- expand: 2 |
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- headdim: 64 |
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- d_conv: 4 |
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- ngroups: 1 |
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Training: |
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- Max halt steps: 1 |
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- Block size: 1024 |
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- Batch size: 64 (effective) |
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- Learning rate: 3e-05 → 1e-06 |
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- Weight decay: 0.1 |
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``` |
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## Training Data |
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- **Dataset**: [open-web-math/open-web-math](https://huggingface.co/datasets/open-web-math/open-web-math) |
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- **Tokenizer**: `t5-small` (T5 SentencePiece) |
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- **Vocab Size**: 32100 |
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## Latest Performance (Epoch 0) |
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- **Validation Loss**: `10.3766` |
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- **Validation Perplexity**: `32099.98` |
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## Usage |
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```python |
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from transformers import T5Tokenizer |
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from hrm_text1_mamba1_donor import HRMText1 |
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tokenizer = T5Tokenizer.from_pretrained("t5-small") |
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model = HRMText1.from_pretrained("Viharikvs/CMBA-768M-OpenWebMath") |
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# Generate text |
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input_ids = tokenizer("Once upon a time", return_tensors="pt").input_ids |
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outputs = model.generate(input_ids, max_length=100) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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## Citation |
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If you use this model, please cite: |
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```bibtex |
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@misc{cmba-768m-openwebmath, |
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author = {Vihari}, |
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title = {CMBA-768M-OpenWebMath: Hierarchical Mamba-based Language Model}, |
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year = {2025}, |
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publisher = {HuggingFace}, |
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url = {https://huggingface.co/Viharikvs/CMBA-768M-OpenWebMath} |
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} |
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``` |
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## License |
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Apache 2.0 |
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