Instructions to use Adiuk/AdiTurbo-TQ-Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Adiuk/AdiTurbo-TQ-Models 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 Adiuk/AdiTurbo-TQ-Models:Q4_0
# Run inference directly in the terminal:
llama cli -hf Adiuk/AdiTurbo-TQ-Models:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Adiuk/AdiTurbo-TQ-Models:Q4_0
# Run inference directly in the terminal:
llama cli -hf Adiuk/AdiTurbo-TQ-Models:Q4_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 Adiuk/AdiTurbo-TQ-Models:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf Adiuk/AdiTurbo-TQ-Models:Q4_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 Adiuk/AdiTurbo-TQ-Models:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Adiuk/AdiTurbo-TQ-Models:Q4_0

Use Docker

docker model run hf.co/Adiuk/AdiTurbo-TQ-Models:Q4_0

LM Studio
Jan
Ollama
How to use Adiuk/AdiTurbo-TQ-Models with Ollama:
```
ollama run hf.co/Adiuk/AdiTurbo-TQ-Models:Q4_0
```

Unsloth Studio

How to use Adiuk/AdiTurbo-TQ-Models 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 Adiuk/AdiTurbo-TQ-Models 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 Adiuk/AdiTurbo-TQ-Models to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Adiuk/AdiTurbo-TQ-Models to start chatting

How to use Adiuk/AdiTurbo-TQ-Models with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Adiuk/AdiTurbo-TQ-Models:Q4_0

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Adiuk/AdiTurbo-TQ-Models:Q4_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Adiuk/AdiTurbo-TQ-Models with Docker Model Runner:
```
docker model run hf.co/Adiuk/AdiTurbo-TQ-Models:Q4_0
```

Lemonade

How to use Adiuk/AdiTurbo-TQ-Models with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Adiuk/AdiTurbo-TQ-Models:Q4_0

Run and chat with the model

lemonade run user.AdiTurbo-TQ-Models-Q4_0

List all available models

lemonade list

Hermes Agent

How to use Adiuk/AdiTurbo-TQ-Models with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Adiuk/AdiTurbo-TQ-Models:Q4_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 Adiuk/AdiTurbo-TQ-Models:Q4_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Adiuk/AdiTurbo-TQ-Models with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Adiuk/AdiTurbo-TQ-Models:Q4_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 "Adiuk/AdiTurbo-TQ-Models:Q4_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"

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AdiTurbo TQ Models (research preview)

Experimental TQ3_0 / TQ4_0 quantizations produced by AdiTurbo, a custom quantization engine built on a research fork of llama.cpp. These GGUF files use quant formats that do not exist in stock llama.cpp — they will not load in upstream builds.

File	Base model
`Llama-3.1-8B-TQ3_0.gguf`	Llama-3.1-8B
`Mistral-7B-TQ3_0.gguf`	Mistral-7B
`Qwen2.5-0.5B-TQ3_0-v2.gguf`	Qwen2.5-0.5B
`Qwen2.5-0.5B-TQ4_0-routed-v5-imat.gguf`	Qwen2.5-0.5B (routed, imatrix)

Status: research artifacts, not production models. Quality varies by format and tensor type; published for reproducibility of ongoing low-bit quantization research. Base-model licenses (Llama 3.1 Community License, Apache-2.0) apply to the respective files.

Author & research

🌐 Website: arifadito.com
💻 GitHub: github.com/Adiuk24
📄 Eyla identity paper: arXiv:2604.00009
🔧 Rust ML training verification tooling: gradient-flow-arbiter

Downloads last month: -

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

View +3 variants

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Paper for Adiuk/AdiTurbo-TQ-Models

Eyla: Toward an Identity-Anchored LLM Architecture with Integrated Biological Priors -- Vision, Implementation Attempt, and Lessons from AI-Assisted Development

Paper • 2604.00009 • Published Mar 9