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

Use Docker

docker model run hf.co/Thox-ai/ThoxKey-9M-role:Q8_0

LM Studio
Jan

vLLM

How to use Thox-ai/ThoxKey-9M-role with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Thox-ai/ThoxKey-9M-role"
# 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/ThoxKey-9M-role",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Thox-ai/ThoxKey-9M-role:Q8_0

Ollama
How to use Thox-ai/ThoxKey-9M-role with Ollama:
```
ollama run hf.co/Thox-ai/ThoxKey-9M-role:Q8_0
```

Unsloth Studio

How to use Thox-ai/ThoxKey-9M-role 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/ThoxKey-9M-role 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/ThoxKey-9M-role 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/ThoxKey-9M-role to start chatting

Docker Model Runner
How to use Thox-ai/ThoxKey-9M-role with Docker Model Runner:
```
docker model run hf.co/Thox-ai/ThoxKey-9M-role:Q8_0
```

Lemonade

How to use Thox-ai/ThoxKey-9M-role with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Thox-ai/ThoxKey-9M-role:Q8_0

Run and chat with the model

lemonade run user.ThoxKey-9M-role-Q8_0

List all available models

lemonade list

Atomic Chat

ThoxKey-9M-role / Modelfile

tommytracx

Upload folder using huggingface_hub

34a4ff2 verified about 15 hours ago

Raw

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532 Bytes

	FROM ./thoxkey-9m-role-Q8_0.gguf

	# ThoxKey-S3 on-device role model. 8.9M params, BitNet b1.58 ternary (W1.58A8).
	# Runs locally on the key: 3.80 MB weights, 8.00 MB resident, ~15.9 tok/s on
	# ESP32-S3 PSRAM. It handles local commands and defers everything else.
	# It is NOT a question-answering model and has no company facts by design.

	TEMPLATE """<\|user\|>
	{{ .Prompt }}
	<\|thox\|>
	"""

	PARAMETER stop "<\|user\|>"
	PARAMETER stop "<\|thox\|>"
	PARAMETER temperature 0.2
	PARAMETER top_p 0.9
	PARAMETER num_predict 40
	PARAMETER num_ctx 256