Instructions to use Thox-ai/ThoxMicro-1bit-16M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Thox-ai/ThoxMicro-1bit-16M with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Thox-ai/ThoxMicro-1bit-16M:Q8_0 # Run inference directly in the terminal: llama cli -hf Thox-ai/ThoxMicro-1bit-16M:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Thox-ai/ThoxMicro-1bit-16M:Q8_0 # Run inference directly in the terminal: llama cli -hf Thox-ai/ThoxMicro-1bit-16M:Q8_0
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf Thox-ai/ThoxMicro-1bit-16M:Q8_0 # Run inference directly in the terminal: ./build/bin/llama cli -hf Thox-ai/ThoxMicro-1bit-16M:Q8_0
Use Docker
docker model run hf.co/Thox-ai/ThoxMicro-1bit-16M:Q8_0
- Llama new
- LM Studio
- Jan
- vLLM
How to use Thox-ai/ThoxMicro-1bit-16M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Thox-ai/ThoxMicro-1bit-16M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Thox-ai/ThoxMicro-1bit-16M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Thox-ai/ThoxMicro-1bit-16M:Q8_0
- Ollama
How to use Thox-ai/ThoxMicro-1bit-16M with Ollama:
ollama run hf.co/Thox-ai/ThoxMicro-1bit-16M:Q8_0
- Unsloth Desktop
- Docker Model Runner
How to use Thox-ai/ThoxMicro-1bit-16M with Docker Model Runner:
docker model run hf.co/Thox-ai/ThoxMicro-1bit-16M:Q8_0
- Lemonade
How to use Thox-ai/ThoxMicro-1bit-16M with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Thox-ai/ThoxMicro-1bit-16M:Q8_0
Run and chat with the model
lemonade run user.ThoxMicro-1bit-16M-Q8_0
List all available models
lemonade list
- Atomic Chat
ThoxMicro-1bit-16M
From-Scratch THOX Ternary Model β a THOX model trained from scratch: a BitNet b1.58 ternary decoder (16M parameters, 86.6% of weights ternary) trained on TinyStories
GGUF: Q8_0, TQ2_0 Β· Ollama Modelfile
Find your model: THOX Model Factory Β· Collection: Research & From-Scratch Models
Your AI. Your Data. Your Rules.β’
Summary
ThoxMicro-1bit-16M is a THOX model trained from scratch: a BitNet b1.58 ternary decoder (16M parameters, 86.6% of weights ternary) trained on TinyStories. It is the deeper sibling of ThoxMicro-1bit-9M, an edge research model, and the base for the THOX 16M device-role models. Neither the 9M nor the 16M supersedes the other.
Highlights
- ThoxMicro-1bit-16M is a THOX model trained from scratch: a BitNet b1.58 ternary decoder (16M parameters, 86.6% of weights ternary) trained on TinyStories.
bash hf download Thox-ai/ThoxMicro-1bit-16M --include '*.gguf' --local-dir ./ThoxMicro-1bit-16M llama-cli -m ./ThoxMicro-1bit-16M/thoxmicro-1bit-16m.TQ2_0.gguf -p "Hello"Ternary files need a recent llama.cpp build; Ollama and LM Studio load only the Q8_0 file.
Specifications
| Base model | See card body |
| Parameters | See card body |
| Formats | GGUF: Q8_0, TQ2_0 Β· Ollama Modelfile |
| License | Apache-2.0 |
| This repository contains | GGUF build(s) in this repo: thoxmicro-1bit-16m.Q8_0.gguf, thoxmicro-1bit-16m.TQ2_0.gguf |
Files
| File | Size | Purpose |
|---|---|---|
thoxmicro-1bit-16m.Q8_0.gguf |
17,033,504 B | GGUF model build |
thoxmicro-1bit-16m.TQ2_0.gguf |
8,030,496 B | GGUF model build |
.gitattributes |
1,650 B | Repository file |
Modelfile |
806 B | Ollama model definition |
Architecture
| Field | Value |
|---|---|
| Architecture | BitNet b1.58 ternary decoder |
| Layers | 16 |
| Hidden size | 256 |
| Attention heads | 8 |
| KV heads | 8 |
| FFN / intermediate | 768 |
| Vocab | 8,192 (own byte-level BPE) |
| Max context | 512 |
| Tied embeddings | yes |
Run it
hf download Thox-ai/ThoxMicro-1bit-16M --include '*.gguf' --local-dir ./ThoxMicro-1bit-16M
llama-cli -m ./ThoxMicro-1bit-16M/thoxmicro-1bit-16m.TQ2_0.gguf -p "Hello"
Ternary files need a recent llama.cpp build; Ollama and LM Studio load only the Q8_0 file.
Platforms
| Platform / device | Run path |
|---|---|
| Hugging Face | Download the builds listed under Files from this repo (see Run it) |
Intended use
- On-device and edge text generation within the THOX stack, and research on ternary-weight models.
- Not a safety-aligned public assistant unless deployed behind THOX guardrails.
- Out of scope: medical, legal, financial, safety-critical, or other high-stakes decisions without human review.
Training data and procedure
Trained by THOX from scratch as part of the THOX small-model research line. The corpus and procedure are as described in this card at release; no external base model was used.
Evaluation
No THOX GATE49 result is published for this model.
Limitations
- A 16M research model: story completion only, no instruction following or factual reliability.
- Behaviour and bias reflect the training data; quality outside short English story text is low.
- Quantization trades accuracy for size.
Provenance
Published by THOX.ai as Thox-ai/ThoxMicro-1bit-16M. THOX from-scratch research line; no upstream base model. This card was conformed to THOX Model Card Standard v1.0 on 2026-10-31 (frontmatter, tags, header, files, and section structure); the technical content is as published at release.
License
Apache License 2.0. This model was trained by THOX from scratch and is released under the Apache License 2.0. Training data note: the training corpus is TinyStories, distributed under CDLA-Sharing-1.0; that license governs the dataset itself.
THOX.ai β Your AI. Your Data. Your Rules.β’
Citation
@misc{thoxai2026thoxmicro1bit16m,
title={ThoxMicro-1bit-16M},
author={THOX.ai},
year={2026},
url={https://huggingface.co/Thox-ai/ThoxMicro-1bit-16M}
}
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