Instructions to use Thox-ai/ThoxMicro-1bit-9M 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-9M 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-9M:Q8_0
# Run inference directly in the terminal:
llama cli -hf Thox-ai/ThoxMicro-1bit-9M: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-9M:Q8_0
# Run inference directly in the terminal:
llama cli -hf Thox-ai/ThoxMicro-1bit-9M: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-9M:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Thox-ai/ThoxMicro-1bit-9M: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-9M:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Thox-ai/ThoxMicro-1bit-9M:Q8_0

Use Docker

docker model run hf.co/Thox-ai/ThoxMicro-1bit-9M:Q8_0

LM Studio
Jan

vLLM

How to use Thox-ai/ThoxMicro-1bit-9M 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-9M"
# 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-9M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Thox-ai/ThoxMicro-1bit-9M:Q8_0

Ollama
How to use Thox-ai/ThoxMicro-1bit-9M with Ollama:
```
ollama run hf.co/Thox-ai/ThoxMicro-1bit-9M:Q8_0
```
Unsloth Desktop
Docker Model Runner
How to use Thox-ai/ThoxMicro-1bit-9M with Docker Model Runner:
```
docker model run hf.co/Thox-ai/ThoxMicro-1bit-9M:Q8_0
```

Lemonade

How to use Thox-ai/ThoxMicro-1bit-9M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Thox-ai/ThoxMicro-1bit-9M:Q8_0

Run and chat with the model

lemonade run user.ThoxMicro-1bit-9M-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-9M

Your AI. Your Data. Your Rules.

From-scratch BitNet b1.58 ternary Llama (9M) trained on TinyStories — a story-completion / edge research artifact, not an assistant.

What this is

Trained from scratch — no upstream base model.
76.4% of weights are ternary {-1,0,+1} (BitNet b1.58).
License is unresolved: TinyStories (CDLA-Sharing-1.0) share-alike not yet cleared — treat as pending review, not open for redistribution until THOX confirms.
Sibling: ThoxMicro-1bit-16M (deeper, not a replacement).

Architecture (from config)

Field	Value
Architecture	BitNet b1.58 ternary Llama decoder
Layers	8
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-9M --include '*.gguf' --local-dir ./ThoxMicro-1bit-9M
llama-cli -m ./ThoxMicro-1bit-9M/model-TQ2_0.gguf -p "Hello"

Model tree for Thox-ai/ThoxMicro-1bit-9M

Quantizations

2 models

Thox-ai
/

ThoxMicro-1bit-9M