Trinity-Large-Thinking-GGUF

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Trinity-Large-Thinking-GGUF

Introduction

Trinity-Large-Thinking is a reasoning-optimized variant of Arcee AI's Trinity-Large family — a 398B-parameter sparse Mixture-of-Experts (MoE) model with approximately 13B active parameters per token, post-trained with extended chain-of-thought reasoning and agentic RL.

This repository contains GGUF quantized weights of Trinity-Large-Thinking in multiple quantization levels.

For full model details, benchmarks, and usage guidance, see the main Trinity-Large-Thinking model card.

Available Quantizations

Usage

llama.cpp

Supported in llama.cpp release b7061+.

# Recommended quant
llama-server -hf arcee-ai/Trinity-Large-Thinking-GGUF:Q4_K_M

# Higher quality
llama-server -hf arcee-ai/Trinity-Large-Thinking-GGUF:Q6_K

# Lower memory
llama-server -hf arcee-ai/Trinity-Large-Thinking-GGUF:Q3_K_M

LM Studio

Search for arcee-ai/Trinity-Large-Thinking-GGUF in Model Search. Select your preferred quantization level.

API

Works out of the box on OpenRouter as arcee-ai/trinity-large-thinking.

License

Trinity-Large-Thinking-GGUF is released under the Apache License, Version 2.0.

Citation

If you use this model, please cite:

@misc{singh2026arceetrinity,
  title        = {Arcee Trinity Large Technical Report},
  author       = {Varun Singh and Lucas Krauss and Sami Jaghouar and Matej Sirovatka and Charles Goddard and Fares Obied and Jack Min Ong and Jannik Straube and Fern and Aria Harley and Conner Stewart and Colin Kealty and Maziyar Panahi and Simon Kirsten and Anushka Deshpande and Anneketh Vij and Arthur Bresnu and Pranav Veldurthi and Raghav Ravishankar and Hardik Bishnoi and DatologyAI Team and Arcee AI Team and Prime Intellect Team and Mark McQuade and Johannes Hagemann and Lucas Atkins},
  year         = {2026},
  eprint       = {2602.17004},
  archivePrefix= {arXiv},
  primaryClass = {cs.LG},
  doi          = {10.48550/arXiv.2602.17004},
  url          = {https://arxiv.org/abs/2602.17004}
}