voxyne 0.1.0

A tiny byte-level conversational language model with the RahuKetu sigma_K channel.

voxyne

A tiny byte-level conversational language model with the RahuKetu sigma_K control channel. Loads on CPU and runs offline.

The code is Apache-2.0. The released research weights are hosted on Hugging Face under a separate non-commercial model license. See the model card.

Project

Voxyne is a small byte-level conversational model package.

• rahuketu - the zero-free encoding framework behind the sigma_K control channel.
• voxyne - this repository contains the model code, marker format, sigma_K encoder, generation helpers, and tests. It does not include a training loop, retrieval system, mutable memory, or autonomous learning runtime.
• Released research weights and quantized artifacts are published separately on Hugging Face with their own model card and license.

Install

pip install voxyne

Usage

Smoke test with random weights:

import torch
from voxyne import smoke_config, build

model, enc = build(smoke_config())
x = torch.tensor([[1, 2, 3, 4]])
_, aux = enc(x, sigma_K=torch.tensor([[1.0, 1.0, -1.0, 1.0]]))
print(model(x, aux)[0].shape)        # -> next-byte logits

To generate real responses, load a trained checkpoint. The package does not bundle weights. Download voxyne-v0.1.pt from the Hugging Face model repo, review the license there, and pass the local path to load_weights:

from voxyne import VoxyneConfig, build, load_weights, generate

model, enc = build(VoxyneConfig())   # 128.8M base with sigma_K aux
load_weights(model, "/path/to/voxyne-v0.1.pt")
print(generate(model, enc, "hello", device="cpu"))

Components

• VoxyneConfig - architecture + encoder settings.
• RahuKetuEncoder - byte digits -> (digits, aux) with the sigma_K channel.
• VoxyneLM - transformer with out_digit, out_sigma, and KV cache.
• generate / generate_cached - greedy or top-k decoding. Cached generation uses KV cache.
• build(config) - construct a matching (model, encoder) pair.

Weights

The package ships the architecture only. No model weights are bundled in this repository.

Released research weights and ONNX artifacts:

• Model repo: https://huggingface.co/decipherpunk/voxyne
• PyTorch checkpoint: voxyne-v0.1.pt
• ONNX artifacts: voxyne-int4.onnx, voxyne-int8.onnx, voxyne-fp32.onnx
• Weight license: CC BY-NC 4.0 / non-commercial, per the model card

The Apache-2.0 license in this repository covers code only. Weight files remain under the model-card license.

Tests

pytest covers the package API, tensor shapes, loading, and KV-cache path. Model quality depends on the released weights.

Provenance / AI assistance

Built by Ramakrishnan (ORCID 0009-0006-0905-7275). The idea, architecture, and research direction are by Ramakrishnan. AI tools helped with tests, automation, and QA.

License

Apache-2.0 (code). See LICENSE and NOTICE. Weights are licensed separately on the model card.