---
license: mit
language:
- en
pipeline_tag: text-generation
---
# Maximum Language Model (218M)

A transformer-based language model inspired by GPT architecture, incorporating RoPE (Rotary Position Embeddings) and GeGLU (Gated Exponential Linear Unit) activations for enhanced performance.

## Model Specifications

- **Parameters**: 218M
- **Training Data**: 3M tokens
- **Key Features**:
  - RoPE (Rotary Position Embeddings) for better position encoding
  - GeGLU activation function for improved gradient flow
  - Transformer-based architecture


### Position Embeddings
The model uses RoPE (Rotary Position Embeddings) instead of traditional positional encodings. RoPE enables:
- Better relative position modeling
- Enhanced extrapolation to longer sequences
- Theoretical backing for position-aware attention

### Activation Function
GeGLU (Gated Exponential Linear Unit) is used as the activation function, which:
- Provides better gradient flow during training
- Combines the benefits of gating mechanisms with ELU's properties
- Helps mitigate vanishing gradient problems