MaAIos
/

Henyo-153M

@@ -1,199 +1,60 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
+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.