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arcee-ai / Trinity-Large-Thinking-GGUF
Model's Last Updated: May 29 2026
Introduction of Trinity-Large-Thinking-GGUF
Model Details of Trinity-Large-Thinking-GGUF
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
| Quant | Type | Use Case |
|---|---|---|
| Q8_0 | 8-bit | Best quality, highest memory |
| Q6_K_L | 6-bit (large) | Near-lossless |
| Q6_K | 6-bit | Near-lossless |
| Q5_K_L | 5-bit (large) | High quality |
| Q5_K_M | 5-bit (medium) | High quality |
| Q5_K_S | 5-bit (small) | High quality |
| Q4_K_L | 4-bit (large) | Recommended balance of quality and size |
| Q4_K_M | 4-bit (medium) | Good balance |
| Q4_K_S | 4-bit (small) | Good balance |
| Q4_1 | 4-bit | Good balance |
| Q4_0 | 4-bit | Good balance |
| Q3_K_XL | 3-bit (extra large) | Lower memory |
| Q3_K_L | 3-bit (large) | Lower memory |
| Q3_K_M | 3-bit (medium) | Lower memory |
| Q3_K_S | 3-bit (small) | Lower memory |
| IQ4_NL | 4-bit (imatrix) | Importance-weighted 4-bit |
| IQ4_XS | 4-bit (imatrix) | Importance-weighted 4-bit, smaller |
| IQ3_M | 3-bit (imatrix) | Importance-weighted 3-bit |
| IQ3_XS | 3-bit (imatrix) | Importance-weighted 3-bit, smaller |
| IQ3_XXS | 3-bit (imatrix) | Importance-weighted 3-bit, smallest |
| IQ2_M | 2-bit (imatrix) | Extreme compression |
| IQ2_S | 2-bit (imatrix) | Extreme compression |
| IQ2_XS | 2-bit (imatrix) | Extreme compression |
| IQ2_XXS | 2-bit (imatrix) | Extreme compression |
| Q2_K_L | 2-bit (large) | Extreme compression |
| Q2_K | 2-bit | Extreme compression |
| IQ1_M | 1-bit (imatrix) | Research / experimental |
| IQ1_S | 1-bit (imatrix) | Research / experimental |
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}
}
Runs of arcee-ai Trinity-Large-Thinking-GGUF on huggingface.co
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More Information About Trinity-Large-Thinking-GGUF huggingface.co Model
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Other API from arcee-ai
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