---
language:
- en
license: mit
tags:
- onnx
- causal-lm
- grammar-correction
- typo-correction
- character-aware
- rope
- gqa
- swiglu
- int8
- offline-nlp
pipeline_tag: text-generation
inference: false
---

<p align="center">
  <img src="typoformer_logo.png" alt="TypoFormer Logo" width="520"/>
</p>

# TypoFormer (Stage 2 INT8)

**TypoFormer** is a 100% offline, character-aware hybrid Transformer designed for ultra-low latency (<10ms on CPU) next-word autocomplete and non-autoregressive grammatical error correction (GEC).

Companion Windows desktop app and training source code available on [GitHub](https://github.com/fahiiim/TyPoTaKer).

## Model Architecture
- **Trunk**: 10 Transformer blocks (768 hidden dimension, 2048 FFN dimension)
- **Attention**: Grouped Query Attention (12 Query heads, 4 Key-Value heads) + Rotary Position Embeddings (RoPE)
- **Non-Linearity**: SwiGLU + Pre-RMSNorm
- **Hybrid Input**: Gated fusion of 16,000 Byte-Level BPE embeddings with Multi-Scale Character 1D-Convolutions (kernel sizes 2, 3, 5, 8)
- **Quantization**: Symmetric INT8 ONNX graph (130 MB footprint)

## Files in this Repository
- `lm_stage2_int8.onnx`: Causal language model for next-word suggestion (~138 MB)
- `gec_stage2_int8.onnx`: Non-autoregressive edit-tagger for grammar & typo correction (~130 MB)
- `tokenizer.json`, `vocab.json`, `merges.txt`: 16k Byte-Level BPE tokenizer
- `edit_maps.json`: High-frequency confusion dictionary for atomic replacements & appends
- `infer.py`: Self-contained Python runner using ONNX Runtime

## Quickstart

```bash
pip install onnxruntime tokenizers numpy
git clone https://huggingface.co/fahiiim/TypoFormer
cd TypoFormer
python infer.py
```

### Python Example
```python
from infer import TypoFormer

model = TypoFormer(".")

# 1. Autocomplete (Causal LM)
suggestions = model.autocomplete("Could you please review the configuration file before the ", top_k=3)
print("Autocomplete suggestions:", suggestions)

# 2. Grammar & Typo Fix (GEC)
clean = model.correct_grammar("This is teh wrong way to write.")
print("Fixed text:", clean)
```

## License
MIT