README.md

---
language:
- tl
dataset:
- MaAIos/culturax-filipino-subset
library_name: transformers
tags:
- text-generation
- pytorch
- custom-architecture
- henyo
license: mit
---

# Henyo-153M-CulturaX

**Henyo** is a 153M parameter Tagalog Language Model trained on the `MaAIos/culturax-filipino-subset` dataset. It utilizes a custom efficient architecture heavily inspired by Llama 2/3 and PaLM.

## Architecture Details
This model uses a custom Decoder-Only Transformer architecture built from scratch in PyTorch.

| Hyperparameter | Value |
| :--- | :--- |
| **Parameters** | ~153M |
| **Context Window** | 1024 tokens |
| **Embedding Dim** | 768 |
| **Layers (Depth)** | 12 |
| **Attention Heads** | 12 |
| **KV Heads (GQA)** | 4 |
| **Vocab Size** | 50,257 (GPT-2 tokenizer) |

### Key Features
1.  **SwiGLU Activation**: High-performance gated linear unit activation.
2.  **Grouped Query Attention (GQA)**: 12 Query heads sharing 4 KV heads (3:1 ratio) for efficient inference.
3.  **Rotary Positional Embeddings (RoPE)**: For better generalization on sequence lengths.
4.  **RMSNorm**: Pre-normalization for training stability.

## Training Configuration
- **Dataset**: [MaAIos/culturax-filipino-subset](https://huggingface.co/datasets/MaAIos/culturax-filipino-subset)
- **Mode**: Streaming (Iterable Dataset)
- **Optimizer**: AdamW
- **Scheduler**: Cosine Decay
- **Gradient Accumulation**: 8 steps (Effective batch size ~32)
- **Precision**: Mixed Precision (FP16)

## Usage

Since this model uses a custom architecture, you must include the class definitions (provided in the `train_henyo.py` file in this repo) or use the inference script below.

```python
# See inference_henyo.py in files for full class definitions
from transformers import AutoTokenizer

model_id = "marcuscedricridia/Henyo-153M-CulturaX"
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Load model using custom class wrapper...
```

### Reproducibility
The full training script (train_henyo.py) is included in the file listing of this repository.