Instructions to use tommytracx/Thox1-27b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use tommytracx/Thox1-27b 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 tommytracx/Thox1-27b:Q1_0
# Run inference directly in the terminal:
llama cli -hf tommytracx/Thox1-27b:Q1_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tommytracx/Thox1-27b:Q1_0
# Run inference directly in the terminal:
llama cli -hf tommytracx/Thox1-27b:Q1_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 tommytracx/Thox1-27b:Q1_0
# Run inference directly in the terminal:
./llama-cli -hf tommytracx/Thox1-27b:Q1_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 tommytracx/Thox1-27b:Q1_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tommytracx/Thox1-27b:Q1_0

Use Docker

docker model run hf.co/tommytracx/Thox1-27b:Q1_0

LM Studio
Jan

vLLM

How to use tommytracx/Thox1-27b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tommytracx/Thox1-27b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tommytracx/Thox1-27b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tommytracx/Thox1-27b:Q1_0

Ollama
How to use tommytracx/Thox1-27b with Ollama:
```
ollama run hf.co/tommytracx/Thox1-27b:Q1_0
```
Unsloth Desktop

How to use tommytracx/Thox1-27b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tommytracx/Thox1-27b:Q1_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "tommytracx/Thox1-27b:Q1_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use tommytracx/Thox1-27b with Docker Model Runner:
```
docker model run hf.co/tommytracx/Thox1-27b:Q1_0
```

Lemonade

How to use tommytracx/Thox1-27b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tommytracx/Thox1-27b:Q1_0

Run and chat with the model

lemonade run user.Thox1-27b-Q1_0

List all available models

lemonade list

Hermes Agent

How to use tommytracx/Thox1-27b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tommytracx/Thox1-27b:Q1_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default tommytracx/Thox1-27b:Q1_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use tommytracx/Thox1-27b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tommytracx/Thox1-27b:Q1_0

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "tommytracx/Thox1-27b:Q1_0" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Thox1-27b

Your AI. Your Data. Your Rules.

THOX flagship 1-bit (Q1_0) GGUF quant of a 27B Qwen3.5-family abliterated model — the most-downloaded THOX repo.

What this is

Real quantized-weights repo: single Thox1-27b-Q1_0.gguf (~4.67 GB) + config/tokenizer.
Base: s3nh/Bonsai-27B-unpacked-abliterated-uncensored (Qwen3.5 VLM). base_model_relation=quantized.
config max_position_embeddings 262144; card serves at 8192 — align the card's context claim.
License chain: card asserts Apache-2.0 over an abliterated base — verify the full chain. Uncensored — content-policy sensitive.
Config is a full VLM (vision tower) but it is served text-only via llama.cpp.

Architecture (from config)

Field	Value
Architecture	qwen3_5 (Qwen3_5ForConditionalGeneration, VLM base; served text-only)
Layers	64
Hidden size	5120
Attention heads	24
KV heads	4 (GQA)
FFN / intermediate	17408
Vocab	248,320
Max context	262144
dtype	bfloat16

Intended use

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

Prompt / chat template

<|im_start|>system
{system}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Usage

llama.cpp

huggingface-cli download tommytracx/Thox1-27b --include '*.gguf' --local-dir ./Thox1-27b
llama-cli -m ./Thox1-27b/model-Q1_0.gguf -p "Hello"

Model tree for tommytracx/Thox1-27b

Base model

s3nh/Bonsai-27B-unpacked-abliterated-uncensored

Quantized

(1)

this model

tommytracx
/

Thox1-27b

Thox1-27b

What this is

Architecture (from config)

Intended use

Prompt / chat template

Usage

Links

Model tree for tommytracx/Thox1-27b