VOLT

Tokenization Model

Tokenization

What VOLT Does

VOLT (Versatile Optimized Language Tokenizer) is a custom tokenization model designed to transform raw text into efficient, compact token representations. Unlike conventional rule-based tokenizers or pre-defined subword approaches (like BPE or WordPiece), VOLT leverages a neural architecture with vector quantization to automatically learn meaningful text units.

Instead of relying on manually defined segmentation rules, VOLT directly learns which patterns of characters or sequences should be grouped together, making it more adaptive and flexible for different domains and writing styles.

Why It Matters

•
Efficiency – Compresses language into fewer, more expressive tokens, reducing sequence lengths and costs
•
Scalability – Designed for large-parameter models (100M+) while keeping memory usage efficient
•
Adaptability – Learns tokenization directly from data, handling slang and domain-specific vocabulary
•
Future-Proofing – Not tied to fixed vocabulary, generalizes to new words without retraining

Other versions: VOLT Medium and VOLT Large coming soon

How to Use

Download and Use the Tokenizer

"keyword">from transformers "keyword">import AutoTokenizer

# Download tokenizer "keyword">from Hugging Face repo
tokenizer = AutoTokenizer.from_pretrained("tokenaii/volt-small")
text = "Hello, this is a test sentence."
tokens = tokenizer.tokenize(text)
input_ids = tokenizer.encode(text)

print("Tokens:", tokens)
print("Input IDs:", input_ids)
print("Decoded:", tokenizer.decode(input_ids))

Download the Model File Only

"keyword">from huggingface_hub "keyword">import hf_hub_download

# Download the model file (e.g., pytorch_model.bin) "keyword">from the repo
model_path = hf_hub_download(
    repo_id="tokenaii/volt-small",
    filename="pytorch_model.bin"
)
print("Model downloaded to:", model_path)

Can I Run This Model?

Enter your system specifications to check if you can run this model (functionality coming soon):

VRAM (GB)

CPU Cores