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

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

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -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/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -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

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 Studio

How to use Thox-ai/ThoxMicro-1bit-16M with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Thox-ai/ThoxMicro-1bit-16M to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Thox-ai/ThoxMicro-1bit-16M to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Thox-ai/ThoxMicro-1bit-16M to start chatting

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
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ThoxMicro-1bit-16M

Your AI. Your Data. Your Rules.

From-scratch BitNet b1.58 ternary Llama (16M) trained on TinyStories — deeper sibling of the 9M edge research model.

What this is

Trained from scratch — no upstream base model.
86.6% of weights are ternary (BitNet b1.58).
Same pending-review TinyStories licensing caveat as the 9M.
Neither the 9M nor 16M supersedes the other.

Architecture (from config)

Field	Value
Architecture	BitNet b1.58 ternary Llama 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

Intended use

On-device / edge text generation within the THOX stack. Not a safety-aligned public assistant unless deployed behind THOX guardrails.

Usage

llama.cpp

huggingface-cli download Thox-ai/ThoxMicro-1bit-16M --include '*.gguf' --local-dir ./ThoxMicro-1bit-16M
llama-cli -m ./ThoxMicro-1bit-16M/model-TQ2_0.gguf -p "Hello"

Model tree for Thox-ai/ThoxMicro-1bit-16M

Quantizations

1 model

Thox-ai
/

ThoxMicro-1bit-16M