---
language:
- en
- hi
- ta
- te
- es
- fr
- de
- zh
license: mit
tags:
- tokenizer
- bpe
- byte-level
- code
- stem
- neuromorphic
- synaptic-edge
- pytorch
- triton
pipeline_tag: text-generation
---

# Synaptic Edge 65,536 ByteLevel BPE Tokenizer 

The official **65,536-vocabulary ByteLevel BPE Tokenizer** custom-engineered for the **Synaptic Edge 150M & 1B Neuromorphic Foundation Models**.

Trained on over **1.6+ Billion tokens** of source code, AI system kernels, formal mathematics, and multilingual texts.

---

## Key Highlights

- **Exact $2^{16}$ Alignment**: Vocabulary size is strictly **65,536**, ensuring exact power-of-2 alignment with GPU warp registers and Triton megakernel tensor cores.
- **Zero OOV / Zero `<unk>`**: ByteLevel fallback guarantees that every byte of UTF-8 text can be processed losslessly without out-of-vocabulary errors.
- **Code & Kernel Optimized (~70% Mixture)**:
  - Multi-space indentation tokens (`  `, `    `, `        `, `\t`) eliminate token explosion in nested code blocks.
  - Pre-seeded with Andrej Karpathy's `nanoGPT`/`llm.c`, OpenAI Triton megakernels, BitNet 1.58b STE, and Lean 4 formal proofs.
  - Delivers a **~2.2× compression ratio improvement** over standard NLP tokenizers on Python, C++, Rust, and CUDA.
- **Structural Repo-Level Scoping**: Built-in first-class scoping tags (`<repo>`, `</repo>`, `<file>`, `</file>`, `<think>`, `</think>`) for continuous multi-file repository pretraining.
- **1,000,000-Token Native Context**: Calibrated for ultra-long context streams paired with **3D Bit-RoPE** and **Continual Dynamic Synaptic Plasticity**.

---

## Domain Mixture Breakdown

| Domain | Ingested Samples / Files | Key Sources |
| :--- | :--- | :--- |
| **Code & AI Systems (45%)** | 455,000+ files | `codeparrot-clean`, `Magicoder-OSS-75K`, `CodeFeedback`, `Evol-Instruct-Code` |
| **PCMB & Formal Math (25%)** | 300,000+ docs | `open-web-math`, LaTeX equations, Lean 4 theorems, SMILES molecular formulas |
| **Natural Language & Multi (30%)** | 330,000+ articles | `fineweb-edu`, Aya Multilingual (65 languages), Wikipedia (7 global languages) |

---

## Quickstart

### 1. Using Hugging Face `transformers`
```python
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('SurendraVB/Synaptic-Edge-Tokenizer-65K')

code_snippet = '''@triton.jit
def fused_kernel(a_ptr, b_ptr, BLOCK_M: tl.constexpr):
    pass'''

tokens = tokenizer(code_snippet)
print('Token IDs:', tokens.input_ids)
print('Decoded:', tokenizer.decode(tokens.input_ids))
```

### 2. Using Fast Rust `tokenizers`
```python
from tokenizers import Tokenizer

tokenizer = Tokenizer.from_pretrained('SurendraVB/Synaptic-Edge-Tokenizer-65K')
output = tokenizer.encode('def forward(self, x: torch.Tensor):\n    return x * 2')
print('Subwords:', output.tokens)
```

---

## Special Scoping & Indentation Tokens

| Token | Purpose | Impact |
| :--- | :--- | :--- |
| `    ` (4 spaces) | Standard indentation | Encodes a 4-space tab in a single token instead of 4 separate tokens |
| `        ` (8 spaces) | Kernel / Deep indentation | Compresses 8 spaces into a single atomic ID |
| `<repo>`, `</repo>` | Repository scoping | Signals repository boundaries in streaming pretraining |
| `<file>`, `</file>` | File boundaries | Delimits multi-file continuous context streams |
| `<think>`, `</think>` | Cognitive attractor reasoning | Wraps cognitive latent attractor settling traces |

---

## Citation & Architecture

```bibtex
@misc{synaptic_edge_2026,
  author = {Surendra V B},
  title = {Synaptic Edge: 1-Bit Ternary Continual Learning Neuromorphic Foundation Architecture},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/SurendraVB/Synaptic-Edge-Tokenizer-65K}}
}
```