π Model Description
license: mit library_name: gguf base_model: ServiceNow-AI/Apriel-1.6-15b-Thinker tags:
- gguf
- llama-cpp
- vision
- multimodal
- reasoning
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-Q4KM.gguf | Q4KM | ~8.8 GB | Main model |
mmproj-Apriel-1.6-15b-f16.gguf | F16 | - | Vision projector |
Usage
LM Studio
Apriel-1.6-15b-Thinker-GGUFllama.cpp
./llama-cli -m Apriel-1.6-15b-Thinker-Q4KM.gguf --mmproj mmproj-Apriel-1.6-15b-f16.gguf -p "Your prompt"Ollama
ServiceNow-AI/Apriel-1.6-15b-ThinkerLimitations
- Factual accuracy: May produce incorrect, misleading, or outdated content.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},
}