Instructions to use ServiceNow-AI/Apriel-1.6-15b-Thinker-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ServiceNow-AI/Apriel-1.6-15b-Thinker-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ServiceNow-AI/Apriel-1.6-15b-Thinker-GGUF",
	filename="Apriel-1.6-15b-Thinker-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use ServiceNow-AI/Apriel-1.6-15b-Thinker-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ServiceNow-AI/Apriel-1.6-15b-Thinker-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ServiceNow-AI/Apriel-1.6-15b-Thinker-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ServiceNow-AI/Apriel-1.6-15b-Thinker-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ServiceNow-AI/Apriel-1.6-15b-Thinker-GGUF:Q4_K_M

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 ServiceNow-AI/Apriel-1.6-15b-Thinker-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ServiceNow-AI/Apriel-1.6-15b-Thinker-GGUF:Q4_K_M

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 ServiceNow-AI/Apriel-1.6-15b-Thinker-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ServiceNow-AI/Apriel-1.6-15b-Thinker-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ServiceNow-AI/Apriel-1.6-15b-Thinker-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use ServiceNow-AI/Apriel-1.6-15b-Thinker-GGUF with Ollama:
```
ollama run hf.co/ServiceNow-AI/Apriel-1.6-15b-Thinker-GGUF:Q4_K_M
```

Unsloth Studio

How to use ServiceNow-AI/Apriel-1.6-15b-Thinker-GGUF 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 ServiceNow-AI/Apriel-1.6-15b-Thinker-GGUF 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 ServiceNow-AI/Apriel-1.6-15b-Thinker-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ServiceNow-AI/Apriel-1.6-15b-Thinker-GGUF to start chatting

How to use ServiceNow-AI/Apriel-1.6-15b-Thinker-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ServiceNow-AI/Apriel-1.6-15b-Thinker-GGUF:Q4_K_M

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": "ServiceNow-AI/Apriel-1.6-15b-Thinker-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ServiceNow-AI/Apriel-1.6-15b-Thinker-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ServiceNow-AI/Apriel-1.6-15b-Thinker-GGUF:Q4_K_M

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 ServiceNow-AI/Apriel-1.6-15b-Thinker-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use ServiceNow-AI/Apriel-1.6-15b-Thinker-GGUF with Docker Model Runner:
```
docker model run hf.co/ServiceNow-AI/Apriel-1.6-15b-Thinker-GGUF:Q4_K_M
```

Lemonade

How to use ServiceNow-AI/Apriel-1.6-15b-Thinker-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ServiceNow-AI/Apriel-1.6-15b-Thinker-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Apriel-1.6-15b-Thinker-GGUF-Q4_K_M

List all available models

lemonade list

Apriel-1.6-15b-Thinker GGUF

GGUF quantization of ServiceNow-AI/Apriel-1.6-15b-Thinker with a corrected chat template for proper tool calling and reasoning.

Files

Filename	Quant	Size	Description
`Apriel-1.6-15b-Thinker-Q4_K_M.gguf`	Q4_K_M	~8.8 GB	Main model
`mmproj-Apriel-1.6-15b-f16.gguf`	F16	-	Vision projector

Usage

LM Studio

Apriel-1.6-15b-Thinker-GGUF

llama.cpp

./llama-cli -m Apriel-1.6-15b-Thinker-Q4_K_M.gguf --mmproj mmproj-Apriel-1.6-15b-f16.gguf -p "Your prompt"

Ollama

ServiceNow-AI/Apriel-1.6-15b-Thinker

Limitations

Factual accuracy: May produce incorrect, misleading, or outdated content. Outputs should be verified before use in critical contexts.
Bias: May reflect societal, cultural, or systemic biases present in training data.
Ethics: Do not use the model to produce harmful, unlawful, or unethical content.
Language: Strongest performance is in English. Output quality may degrade in underrepresented languages.
Critical use: Not suitable for medical, legal, financial, or other high-risk applications without safeguards.

Security and Responsible Use

Security Responsibilities:
Deployers and users are strongly encouraged to align their security practices with established frameworks and regulatory guidelines such as the EU AI Act and the NIST AI Risk Management Framework (RMF).

Guidelines for Deployers

Regularly conduct robustness assessments to identify and mitigate adversarial inputs.
Implement validation and filtering processes to prevent harmful or biased outputs.
Continuously perform data privacy checks to guard against unintended data leaks.
Document and communicate the model's limitations, intended usage, and known security risks to all end-users.
Schedule periodic security reviews and updates to address emerging threats and vulnerabilities.

Guidelines for Users

Follow established security policies and usage guidelines provided by deployers.
Protect and manage sensitive information when interacting with the model.
Report anomalies, suspicious behavior, or unsafe outputs to deployers or developers.
Maintain human oversight and apply judgment to mitigate potential security or ethical risks during interactions.

Disclaimer:
Users accept responsibility for securely deploying, managing, and using this open-source LLM. The model is provided "as-is," without explicit or implied warranty regarding security or fitness for any specific application or environment.

License

MIT

Citation

@misc{radhakrishna2025apriel1515bthinker,
      title={Apriel-1.5-15b-Thinker}, 
      author={Shruthan Radhakrishna and Aman Tiwari and Aanjaneya Shukla and Masoud Hashemi and Rishabh Maheshwary and Shiva Krishna Reddy Malay and Jash Mehta and Pulkit Pattnaik and Saloni Mittal and Khalil Slimi and Kelechi Ogueji and Akintunde Oladipo and Soham Parikh and Oluwanifemi Bamgbose and Toby Liang and Ahmed Masry and Khyati Mahajan and Sai Rajeswar Mudumba and Vikas Yadav and Sathwik Tejaswi Madhusudhan and Torsten Scholak and Sagar Davasam and Srinivas Sunkara and Nicholas Chapados},
      year={2025},
      eprint={2510.01141},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2510.01141}, 
}