--- language: - en license: mit library_name: onnxruntime tags: - text-generation - token-classification - grammar-correction - autocomplete - typo-correction - character-aware - edge-ai - onnx - int8 - rope - grouped-query-attention - swiglu - rmsnorm - offline-nlp pipeline_tag: text-generation inference: false widget: - text: "Could you please confirm if the proposed implementation timeline aligns with your" example_title: "Client Inquiry Completion" - text: "We decoupled the event processing pipeline to achieve sub-" example_title: "Engineering Autocomplete" - text: "We dont expect any downtime during the blue-green deployment phase." example_title: "Grammar & Typo Correction" ---

TypoFormer Banner

TypoFormer (Stage 2 INT8)

High-Precision Character-Aware Hybrid Transformer for Edge Autocomplete & Non-Autoregressive Grammar Correction
_{Created & Authored by Md. Fahim Sarker Mridul}

--- ## Overview **TypoFormer** is a purpose-engineered, character-aware neural backbone designed from the ground up to solve two conflicting natural language problems inside a single unified **94M parameter** trunk: 1. **Causal Next-Word Autocomplete**: Low-latency autoregressive token prediction under strict sub-15ms local keystroke budgets without cloud connectivity. 2. **Non-Autoregressive Grammar & Typo Correction (GEC)**: High-precision edit-tag sequence labeling that repairs punctuation slips, confused vocabulary, subject-verb agreement, and severe typos without generative hallucination. By fusing **Byte-Level BPE subwords** with **Multi-Scale Character 1D-Convolutions** through a learned gating highway, TypoFormer eliminates out-of-vocabulary token fractures and delivers enterprise-grade real-time writing assistance completely offline on standard desktop CPUs. --- ## Model Architecture

TypoFormer Full Internal Neural Architecture Diagram

Figure 1: TypoFormer Full Internal Neural Architecture: Fusing byte-level subwords with multi-scale character convolutions, Pre-RMSNorm grouped-query attention (GQA), rotary embeddings (RoPE), SwiGLU feed-forward networks, and multi-task dual heads for offline autocomplete and grammar error correction (Visualized in NVIDIA Emerald Green #76B900).
[View High-Resolution 300 DPI Architecture Diagram (PNG)]

### 1. Dual-Channel Gated Input Highway Standard tokenizers fail when words are corrupted: mistyping `"definitely"` as `"definatly"` converts a single semantic token into multiple rare subword fragments. TypoFormer prevents this tokenization distortion through a hybrid input representation: * **Byte-Level BPE**: Words are split into standard BPE tokens from a compact 16,000-vocabulary dictionary ($x_{\text{tok}} \to \mathbf{E}_{\text{tok}} \in \mathbb{R}^{d_{\text{model}}}$). * **Multi-Scale Character-CNN**: Concurrently, the exact UTF-8 character bytes of each token ($x_{\text{char}}$) are fed into parallel 1D convolutional banks with kernel sizes $k \in \{2, 3, 5, 8\}$: $$\mathbf{C}_k = \mathrm{MaxPool}\left(\mathrm{GELU}\left(\mathrm{Conv1D}_k(\text{Bytes})\right)\right)$$ * **Learned Gating Highway**: $$\mathbf{h}_0 = \mathbf{E}_{\text{tok}} + \alpha \cdot \mathbf{W}_{\text{proj}}\left([\mathbf{C}_2 \,\|\, \mathbf{C}_3 \,\|\, \mathbf{C}_5 \,\|\, \mathbf{C}_8]\right)$$ where $\alpha$ is a learnable scalar gate initialized to ensure robust character-level fallback when unknown or miscoded subwords appear. ### 2. Transformer Trunk (10 Blocks, $d=768$, $d_{\text{ffn}}=2048$) * **Attention Mechanism**: Grouped-Query Attention (**GQA**) with 12 Query heads and 4 Key-Value heads (3:1 ratio), cutting KV-cache memory traffic by 66% during sequence generation. * **Rotary Position Embeddings (RoPE)**: Frequency-based relative query-key rotation for superior relative token distance awareness across cursor movements without absolute position limits. * **Feed-Forward Backbone**: **SwiGLU** non-linearity: $$\text{SwiGLU}(x) = W_2\left(\text{SiLU}(W_1 x) \odot W_3 x\right)$$ * **Pre-Normalization**: **RMSNorm** applied prior to each sub-layer for stable gradient propagation and reduced memory overhead. ### 3. Multi-Task Dual Output Heads * **Task 1: Causal LM Autocomplete Head**: Linear projection ($d_{\text{model}} \to 16{,}000$, weight-tied with token embeddings) yielding next-token probability distribution $P(w_{t+1} \mid w_{\le t})$. * **Task 2: Keystroke GEC Edit Head**: Linear tagging projection ($d_{\text{model}} \to 6{,}148$) mapping each token position to atomic edits: * `KEEP` (0): Token is grammatically and lexically correct. * `DELETE` (1): Redundant or erroneous token removal. * `CASE_CAP` (2) / `CASE_LOWER` (3): Casing adjustments. * `REPLACE` (4 .. 4099): Atomic swap with one of 4,096 high-frequency correction tokens. * `APPEND` (4100 .. 6147): Atomic right-insertion with one of 2,048 suffix/continuation tokens. --- ## Key Highlights * **Ultra-Low Keystroke Latency**: Mean end-to-end CPU inference of **9.97 ms** (P95: **12.12 ms**), fitting comfortably inside the 16.6 ms 60 FPS display frame deadline. * **Typo Immunity**: Gated character-level convolutions preserve word roots even under severe phonetic and fat-finger keyboard slips. * **100% Air-Gapped and Offline**: Zero network calls, zero telemetry, zero risk of leaking confidential documents, code, or private emails. * **Guaranteed Content Fidelity**: Non-autoregressive tagging guarantees zero generative hallucination and achieves **100.0% clean negative sentence preservation**. * **Compact Edge Footprint**: Symmetric INT8 quantization packs the complete 94M dual-task architecture into just **130.5 MB** ONNX graphs. --- ## Architectural Comparison: TypoFormer vs. BERT vs. Cloud LLMs | Architectural Dimension | Classical BERT-Base (110M) | Cloud Generative LLMs (7B - 70B) | TypoFormer (94M INT8) | TypoFormer Advantage | | :--- | :--- | :--- | :--- | :--- | | **Generative Autocomplete** | **Incapable** (Bidirectional MLM only) | **Yes** (High quality autoregression) | **Native Causal Mode** (Tied causal head) | **Unified Trunk**: Single model serves both autocomplete & GEC. | | **Typo & OOV Robustness** | **Brittle** (`teh` to `['te', '##h']`) | **Fair** (Relies on large BPE dictionary) | **Gated Multi-Scale Char-CNN** ($k=2,3,5,8$) | **Typo Immunity**: Never blinded by unknown character corruptions. | | **Position Encoding** | **Absolute Learned** (Max 512 tokens) | **RoPE** / ALiBi | **Rotary Position Embeddings (RoPE)** | **Sequence Extrapolation**: Relative distance aware across cursor hops. | | **Attention Mechanism** | **Multi-Head (MHA)** (12 Q, 12 K, 12 V) | **GQA / MQA** | **Grouped Query Attention (GQA)** (12 Q, 4 KV) | **3x Memory Reduction**: Cuts KV-cache bandwidth by 66%. | | **FFN Non-Linearity** | **GeLU + LayerNorm** (Additive bias) | **SwiGLU + RMSNorm** | **SwiGLU + Pre-RMSNorm** | **LLaMA/Mistral-Grade Efficiency**: Higher expressivity per parameter. | | **GEC Formulation** | **Seq2Seq / MLM** (Slow, iterative) | **Generative Prompting** (Prone to hallucinations) | **Constrained Atomic Edit-Tagging** (6,148 tags) | **Zero Hallucination**: 100% preservation of correct text. | | **End-to-End Latency** | **150ms - 400ms** on CPU | **300ms - 2,500ms** via Cloud API | **< 10ms** on CPU (INT8 ONNX) | **Real-Time Keystroke Budget**: Executes within 16ms display frame. | | **Network & Privacy** | Local or Cloud | **Mandatory Cloud API** (Data leaves host) | **100% Offline Air-Gapped** | **Complete Privacy**: Zero telemetry, password field suppression. | --- ## Benchmark and Evaluation Metrics Live CPU benchmarks measured on standard Intel/AMD consumer hardware ([scripts/evaluate_gec_live.py](https://github.com/fahiiim/TyPoTaKer)): | Metric | Measured Value | Benchmark Description | | :--- | :--- | :--- | | **Mean CPU Latency** | **`9.97 ms`** | Per-sentence end-to-end inference | | **P95 CPU Latency** | **`12.12 ms`** | 95th percentile latency (well below 16.6 ms display frame) | | **P99 CPU Latency** | **`14.80 ms`** | 99th percentile worst-case latency | | **GEC Stage 2 Validation Loss** | **`0.2597`** | Down from 4.1067 (93.7% loss reduction across training) | | **LM Stage 2 Loss** | **`1.2054`** | Fine-tuned on curated engineering and client communications | | **Clean Negative Preservation** | **`100.0%`** | Zero false-positive edits on grammatically correct input text | | **GEC Model Footprint** | **`130.5 MB`** | Symmetric INT8 ONNX graph (`gec_stage2_int8.onnx`) | | **LM Model Footprint** | **`138.1 MB`** | Symmetric INT8 ONNX graph (`lm_stage2_int8.onnx`) | | **Active Working Set RAM** | **`< 250 MB`** | Total memory footprint during active inference | --- ## Repository Contents | File | Size | Format | Description | | :--- | :--- | :--- | :--- | | **`gec_stage2_int8.onnx`** | 130.5 MB | ONNX INT8 | INT8 Quantized graph for grammar, punctuation, and typo correction | | **`lm_stage2_int8.onnx`** | 138.1 MB | ONNX INT8 | INT8 Quantized graph for causal next-word autocomplete | | **`edit_maps.json`** | 228 KB | JSON | High-frequency confusion dictionary (4,096 replacements + 2,048 appends) | | **`tokenizer.json`** | 1.11 MB | JSON | Byte-Level BPE tokenizer config (16,000 vocabulary) | | **`vocab.json` / `merges.txt`** | 526 KB | Text/JSON | BPE vocabulary and merge table | | **`config.json`** | 520 B | JSON | Architecture parameters & model metadata | | **`infer.py`** | 6.5 KB | Python | Standalone, dependency-minimal Python inference engine | | **`typoformer_architecture.png`**| 235 KB | PNG (300 DPI) | Full neural architecture diagram (NVIDIA emerald theme) | | **`typoformer_full_logo.png`** | 188 KB | PNG | Official project branding logo | | **`TypoFormer-win-x64.zip`** | 221.0 MB | ZIP | Standalone Windows distribution package | --- ## Quickstart and Usage ### 1. Minimal Installation ```bash pip install onnxruntime tokenizers numpy git clone https://huggingface.co/fahiiim/TypoFormer cd TypoFormer python infer.py ``` ### 2. Standalone Python Example ```python from infer import TypoFormer # Initialize the standalone inference engine (loads ONNX models locally) model = TypoFormer(".") # 1. Autocomplete (Next-Word Generation) prompt = "Could you please confirm if the proposed implementation timeline aligns with your" suggestions = model.autocomplete(prompt, top_k=3) print("Autocomplete suggestions:", suggestions) # Output: [(' business', 0.9978), (' goals', 0.0002), (' company', 0.0001)] # 2. Grammar, Punctuation & Typo Correction (GEC) dirty_text = "We decoupled the event processing pipeline to achieve sub-" suggestions = model.autocomplete(dirty_text, top_k=2) print("Engineering completion:", suggestions) # Output: [(' millisecond', 0.8841), (' second', 0.0712)] # 3. Clean Text Preservation (Zero Hallucination) clean_text = "The quick brown fox jumps over the lazy dog." result = model.correct_grammar(clean_text) assert result == clean_text # 100% preservation ``` --- ## Windows Desktop Keystroke Companion (1-Line Install) TypoFormer includes a native Windows x64 .NET 8 desktop daemon that runs silently in the system tray and renders a floating caret overlay across any active text editor (Word, Notepad, VS Code, Slack, Browser): Run in PowerShell: ```powershell irm https://raw.githubusercontent.com/fahiiim/TyPoTaKer/main/install.ps1 | iex ``` * **Offline & Private**: Zero telemetry; no keystrokes ever leave your device. * **Password Aware**: Automatically suppresses buffering on password and credential fields via Windows UI Automation. * **Instant Action**: Press `[Tab]` to accept autocomplete suggestions or grammar corrections. --- ## Citation and Author Attribution ```bibtex @software{typoformer2026, author = {Md. Fahim Sarker Mridul}, title = {TypoFormer: High-Precision Character-Aware Hybrid Transformer for Edge Autocomplete & Non-Autoregressive Grammar Correction}, year = {2026}, publisher = {Hugging Face}, url = {https://huggingface.co/fahiiim/TypoFormer}, howpublished = {\url{https://github.com/fahiiim/TyPoTaKer}} } ``` ## License This model and its associated weights are distributed under the [MIT License](LICENSE). Authored by **Md. Fahim Sarker Mridul**.