| ---
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| title: THOX.ai
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| ---
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| # THOX.ai
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|
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| Edge and on-device language models. We build small models that run on hardware
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| people actually own — laptops, USB sticks, single-board computers, and
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| microcontrollers.
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| **14 models are published here.** Everything listed below is real and
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| downloadable; nothing on this page links to a repository you cannot open.
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|
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| ---
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|
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| ## Open — no access request
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|
|
| | model | what it is | size |
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| |---|---|---|
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| | [**ThoxMicro-1bit-9M**](https://huggingface.co/Thox-ai/ThoxMicro-1bit-9M) | BitNet b1.58 **ternary** LM, trained from scratch. Writes children's stories; not an assistant. Research artifact for microcontroller-class targets. | 6.3 MB |
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| | [**ThoxMicro-1bit-16M**](https://huggingface.co/Thox-ai/ThoxMicro-1bit-16M) | The same model at 16 layers instead of 8. 10.7% lower perplexity; needs more device headroom. Neither supersedes the other. | 8.0 MB |
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| | [**thox-micro-125m**](https://huggingface.co/Thox-ai/thox-micro-125m) · [GGUF](https://huggingface.co/Thox-ai/thox-micro-125m-GGUF) | 123.6M base LM trained from scratch. Completion only. | 81–247 MB |
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| | [**ThoxSpeech-ASR-BitNet**](https://huggingface.co/Thox-ai/ThoxSpeech-ASR-BitNet) | BitNet speech recognition, redistributed byte-identical with published SHA-256. | 0.7–1.0 GB |
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|
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| ## Access-gated
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| Adapters and derived models. Request access on the model page.
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|
| | model | base | type |
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| |---|---|---|
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| | [ThoxMini-3B](https://huggingface.co/Thox-ai/ThoxMini-3B) | Llama-3.2-3B-Instruct | LoRA + GGUF |
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| | [ThoxForge-7B](https://huggingface.co/Thox-ai/ThoxForge-7B) | Mistral-7B-Instruct-v0.3 | LoRA + GGUF |
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| | [ThoxGlobal-7B](https://huggingface.co/Thox-ai/ThoxGlobal-7B) | Qwen2.5-7B-Instruct | LoRA + GGUF |
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| | [ThoxWave-8B](https://huggingface.co/Thox-ai/ThoxWave-8B) | dolphin-2.9-llama3-8b | LoRA + GGUF |
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| | [ThoxNova-12B-Core](https://huggingface.co/Thox-ai/ThoxNova-12B-Core) · [Agent](https://huggingface.co/Thox-ai/ThoxNova-12B-Agent) | Gemma-4-12B-it | LoRA + GGUF |
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| | [fable5-agentic-qwen2.5-14b-lora](https://huggingface.co/Thox-ai/fable5-agentic-qwen2.5-14b-lora) | Qwen2.5-14B-Instruct | QLoRA adapter |
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| > **On the adapter bases:** ThoxMini, ThoxForge, ThoxGlobal and both ThoxNova
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| > adapters were trained against `unsloth/*` re-uploads of the listed models, not
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| > the original repositories. Each card's `base_model` now states the re-upload it
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| > was actually trained on, with the upstream lineage recorded in the body.
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| > ThoxWave-8B trains directly on `dphn/dolphin-2.9-llama3-8b`.
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|
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| ## Documentation-only
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| These contain **no weights**, deliberately, and say so on their own pages:
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| - [ThoxForge-7B-GGUF](https://huggingface.co/Thox-ai/ThoxForge-7B-GGUF) — the
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| quantized build did not pass our factuality gate; publishing it anyway to make
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| the repo look complete would route around that control.
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| - [ThoxWebby-Gemma-4-E2B](https://huggingface.co/Thox-ai/ThoxWebby-Gemma-4-E2B) —
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| the browser app pulls upstream weights; we do not re-host them.
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|
|
| ---
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|
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| ## Where our models run
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|
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| - **Ollama** — [`ollama.com/thox-ai`](https://ollama.com/thox-ai)
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| - **llama.cpp** — every GGUF above
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| - **ThoxKey / ThoxKey Pro** — USB sticks that carry a model set and a local runtime
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| - **ThoxMicro / ThoxOS devices** — ESP32-S3 and Raspberry Pi class hardware
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|
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| ## How we publish
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| - **Measured, not estimated.** Numbers on our cards come from runs we did. Where
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| a figure is inherited from an upstream benchmark, the card says so.
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| - **Negative results ship too.** ThoxMicro-1bit-9M's card leads with the fact
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| that ternary quantization costs it 21.4% perplexity, and that the penalty grew
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| rather than shrank with more training data.
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| - **Empty is better than misleading.** A repo with no weights and an honest
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| explanation beats one with weights that failed their gate.
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| - **Licence honesty.** Where downstream terms are genuinely unsettled we say
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| `license: other` and explain, rather than picking a permissive licence because
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| it is convenient.
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|
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| ## Contact
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| [thox.ai](https://thox.ai)
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| |
