---
base_model: t5-small
license: apache-2.0
datasets:
- open-web-math/open-web-math
tags:
- text-generation
- causal-lm
- mamba
- hrm
- pytorch
language:
- en
pipeline_tag: text-generation
---

# CMBA-768M-OpenWebMath

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.

## Model Architecture

**CMBA** (Causal Mamba-based Architecture) implements a hierarchical processing structure:

- **Hierarchical Design**: Dual-level processing with H-layers (high-level abstraction) and L-layers (low-level specialists)
- **Mamba2 Mixers**: State-space models replace attention for O(n) complexity vs O(n²)
- **Adaptive Computation**: Halting mechanism allows variable compute per token (ACT-style pondering)
- **Parameters**: ~768M total
- **Context Length**: 1024 tokens

### Configuration
```python
Model Dimensions:
  - d_model: 1024
  - n_heads: 16 (for compatibility, not used in Mamba)
  - d_ff: 4096
  - H_layers: 12 (high-level hierarchy)
  - L_layers: 12 (low-level processing)

Mamba2 Settings:
  - d_state: 128
  - expand: 2
  - headdim: 64
  - d_conv: 4
  - ngroups: 1

Training:
  - Max halt steps: 1
  - Block size: 1024
  - Batch size: 64 (effective)
  - Learning rate: 3e-05 → 1e-06
  - Weight decay: 0.1
```

## Training Data

- **Dataset**: [open-web-math/open-web-math](https://huggingface.co/datasets/open-web-math/open-web-math)
- **Tokenizer**: `t5-small` (T5 SentencePiece)
- **Vocab Size**: 32100

## Latest Performance (Epoch 0)

- **Validation Loss**: `10.3766`
- **Validation Perplexity**: `32099.98`

## Usage

```python
from transformers import T5Tokenizer
from hrm_text1_mamba1_donor import HRMText1

tokenizer = T5Tokenizer.from_pretrained("t5-small")
model = HRMText1.from_pretrained("Viharikvs/CMBA-768M-OpenWebMath")

# Generate text
input_ids = tokenizer("Once upon a time", return_tensors="pt").input_ids
outputs = model.generate(input_ids, max_length=100)
print(tokenizer.decode(outputs[0]))
```

## Citation

If you use this model, please cite:

```bibtex
@misc{cmba-768m-openwebmath,
  author = {Vihari},
  title = {CMBA-768M-OpenWebMath: Hierarchical Mamba-based Language Model},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/Viharikvs/CMBA-768M-OpenWebMath}
}
```

## License

Apache 2.0