| --- |
| license: cc-by-nc-4.0 |
| language: |
| - en |
| pipeline_tag: text-generation |
| tags: |
| - byte-level |
| - conversational |
| - rahuketu |
| - voxyne |
| - int4 |
| - edge |
| - cpu |
| --- |
| |
| # Voxyne |
|
|
| A tiny (**128.8M-param**) **byte-level** conversational language model with the |
| RahuKetu `sigma_K` control channel. It runs **offline on CPU**, and quantized to |
| **int4 it stays coherent in ~80 MB** — the intended default. |
|
|
| **Scope — please read.** Voxyne is built to *converse*, not to *know*. It has an |
| identity and conversational ability; it is **not a knowledge base** and *will* |
| make up facts if asked — knowledge is meant to come from external tools/retrieval |
| (a separate system). Judge it on **coherence**, not factual recall. |
|
|
| ## Files — **int4 is the default** |
|
|
| | file | size | use | |
| |---|---|---| |
| | **`voxyne-int4.onnx`** | **81 MB** | **DEFAULT** — CPU/edge deployment (ONNX Runtime) | |
| | `voxyne-int8.onnx` | 129 MB | int8 ONNX | |
| | `voxyne-fp32.onnx` | 511 MB | full-precision ONNX | |
| | `voxyne-v0.1.pt` | 258 MB | bf16 PyTorch weights (for the `voxyne` package / fine-tuning) | |
|
|
| The ONNX graphs are a **single-token decode step** with an explicit KV cache |
| (inputs `h`, `pk`, `pv` -> outputs `logits`, `npk`, `npv`); the byte embedding and |
| the `sigma_K` encoder run outside the graph. See the `voxyne` package for the |
| decode protocol. |
|
|
| ## Quick start (PyTorch) |
|
|
| ```bash |
| pip install voxyne # then download voxyne-v0.1.pt from this repo |
| ``` |
| ```python |
| from voxyne import VoxyneConfig, build, load_weights, generate |
| |
| model, enc = build(VoxyneConfig()) |
| load_weights(model, "voxyne-v0.1.pt") |
| print(generate(model, enc, "who are you?", device="cpu")) |
| # -> "I'm Voxyne, an AI assistant created by Ramakrishnan." |
| ``` |
|
|
| ## Training-data provenance (why the license) |
|
|
| Voxyne's weights are trained on a mix that includes **non-commercial** sources, so |
| the **weights are released under CC BY-NC 4.0** (non-commercial). Free for research, |
| education, and personal use. |
|
|
| | stage | sources (examples) | license note | |
| |---|---|---| |
| | pretrain | FineWeb-Edu, Cosmopedia, TinyStories | permissive / synthetic | |
| | grammar | WordNet, FrameNet, GoEmotions | permissive (attribution) | |
| | commonsense | ConceptNet, ATOMIC | ConceptNet = CC BY-SA | |
| | dialogue | SODA, UltraChat, OASST2, daily_dialog, empathetic_dialogues, WildChat | **daily_dialog / empathetic = CC BY-NC; WildChat = AI2 ImpACT** | |
| | identity | author-written | original | |
| |
| The **code** (`voxyne` package) is Apache-2.0; only the **weights** are NC. A future |
| clean-data retrain (`kalki`) will carry Apache-licensed weights. |
| |
| ## AI-assistance disclosure |
| |
| Built by Ramakrishnan (ORCID `0009-0006-0905-7275`). AI tools assisted with the |
| training/quantization automation and tooling; the model design, direction, and |
| all decisions are the author's. |
| |
| ## License |
| |
| Weights: **CC BY-NC 4.0** (non-commercial). Code: Apache-2.0. |
| |
