| ---
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| title: OneBit AI
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| emoji: ⬛
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| colorFrom: gray
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| colorTo: yellow
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| sdk: static
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| pinned: false
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| ---
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| # OneBit AI
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| **Ternary models for on-device language and speech.**
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| We train and recover models whose weights are mostly `{-1, 0, +1}` (about **1.58 bits** per weight), then pack them for real runtimes — not INT8 wrappers around cloud-scale checkpoints.
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| The bet is simple: if the body of the network is ternary from the start (or recovered to true ternary), you can fit useful models in megabytes and run them on a laptop CPU or a single consumer GPU.
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| ## What we build
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| | Program | What it answers | Status (honest) |
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| | --- | --- | --- |
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| | **FineBit** | Can we pretrain a BitNet-compatible ternary LLM from scratch, export I2_S GGUF, and decode fast on CPU? | 30M → 70M on FineWeb-Edu. 70M I2_S is **~61 MB** and ~**617 tok/s** on CPU. Open generation is still early (not a chat product). |
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| | **Cloe** | Can a fully ternary ~0.8B LLM be recovered from quantization damage into fluent English? | Fluency restored (PPL collapse reversed). Knowledge is still far behind the bf16 teacher. |
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| | **BitVoice** | Can TTS and STT be designed ternary from day one? | Packed TTS ~**5.5 MB**, STT ~**11.5 MB**. Libri test-clean STT is usable-but-not-Whisper; TTS is Griffin–Lim, not studio quality. |
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| | **PTQ / QAT SLMs** | How much task ability survives when Qwen3-class models go ternary? | Lossless lattice packing on Qwen3-4B/8B (PPL unchanged). A native ternary matmul kernel is still unbuilt. |
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| Hardware constraint by design: a single **RTX 5070 12 GB**. If it only works on an 8×A100 box, it is not our product story.
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| ## How we differ
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| - **Train or recover ternary**, with STE / BitLinear-style QAT — not “quantize once at export and hope.”
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| - **Ship a runtime path** — FineBit targets **microsoft/BitNet** `bitnet-b1.58` I2_S and bitnet.cpp, not only a PyTorch demo.
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| - **Keep sensitive tips in higher precision** (embeddings, norms, heads) so the body can compress without total collapse.
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| - **Publish the hard metrics** — PPL when it lies, WER/CER for speech, MMLU/GSM8K when they fail.
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| ## This org
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| Models, datasets, and Spaces will land here as we release them. Until then, this card is the public map of the stack.
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| **Contact:** Anirudh Malik · OneBit AI
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