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Anneketh Vij Crystalcareai bartowski colin kealty commited on
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Co-authored-by: Crystalcareai <Crystalcareai@users.noreply.huggingface.co>
Co-authored-by: bartowski <bartowski@users.noreply.huggingface.co>
Co-authored-by: colin kealty <colin kealty@users.noreply.huggingface.co>

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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ language:
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+ - en
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+ - es
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+ - fr
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+ - de
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+ - it
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+ - pt
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+ - ru
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+ - ar
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+ - hi
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+ - ko
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+ - zh
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+ base_model:
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+ - arcee-ai/Trinity-Large-Thinking
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+ base_model_relation: quantized
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+ tags:
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+ - reasoning
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+ - agentic
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+ - tool-calling
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+ - thinking
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+ - gguf
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+ ---
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+ <!-- markdownlint-disable first-line-h1 -->
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+ <!-- markdownlint-disable html -->
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+ <!-- markdownlint-disable no-duplicate-header -->
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+
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+ <div align="center">
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+ <picture>
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+ <img
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+ src="https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/i-v1KyAMOW_mgVGeic9WJ.png"
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+ alt="Arcee Trinity Large Thinking"
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+ style="max-width: 100%; height: auto;"
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+ >
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+ </picture>
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+ </div>
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+ <hr>
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+
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+ # Trinity-Large-Thinking-GGUF
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+
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+ ## Introduction
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+
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+ 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.
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+
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+ **This repository contains GGUF quantized weights of Trinity-Large-Thinking in multiple quantization levels.**
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+
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+ For full model details, benchmarks, and usage guidance, see the main [Trinity-Large-Thinking](https://huggingface.co/arcee-ai/Trinity-Large-Thinking) model card.
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+
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+ ## Available Quantizations
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+
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+ | Quant | Type | Use Case |
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+ |:------|:-----|:---------|
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+ | Q8_0 | 8-bit | Best quality, highest memory |
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+ | Q6_K_L | 6-bit (large) | Near-lossless |
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+ | Q6_K | 6-bit | Near-lossless |
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+ | Q5_K_L | 5-bit (large) | High quality |
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+ | Q5_K_M | 5-bit (medium) | High quality |
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+ | Q5_K_S | 5-bit (small) | High quality |
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+ | Q4_K_L | 4-bit (large) | **Recommended balance of quality and size** |
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+ | Q4_K_M | 4-bit (medium) | Good balance |
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+ | Q4_K_S | 4-bit (small) | Good balance |
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+ | Q4_1 | 4-bit | Good balance |
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+ | Q4_0 | 4-bit | Good balance |
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+ | Q3_K_XL | 3-bit (extra large) | Lower memory |
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+ | Q3_K_L | 3-bit (large) | Lower memory |
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+ | Q3_K_M | 3-bit (medium) | Lower memory |
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+ | Q3_K_S | 3-bit (small) | Lower memory |
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+ | IQ4_NL | 4-bit (imatrix) | Importance-weighted 4-bit |
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+ | IQ4_XS | 4-bit (imatrix) | Importance-weighted 4-bit, smaller |
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+ | IQ3_M | 3-bit (imatrix) | Importance-weighted 3-bit |
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+ | IQ3_XS | 3-bit (imatrix) | Importance-weighted 3-bit, smaller |
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+ | IQ3_XXS | 3-bit (imatrix) | Importance-weighted 3-bit, smallest |
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+ | IQ2_M | 2-bit (imatrix) | Extreme compression |
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+ | IQ2_S | 2-bit (imatrix) | Extreme compression |
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+ | IQ2_XS | 2-bit (imatrix) | Extreme compression |
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+ | IQ2_XXS | 2-bit (imatrix) | Extreme compression |
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+ | Q2_K_L | 2-bit (large) | Extreme compression |
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+ | Q2_K | 2-bit | Extreme compression |
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+ | IQ1_M | 1-bit (imatrix) | Research / experimental |
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+ | IQ1_S | 1-bit (imatrix) | Research / experimental |
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+
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+ ## Usage
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+
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+ ### llama.cpp
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+
87
+ Supported in llama.cpp release b7061+.
88
+
89
+ ```bash
90
+ # Recommended quant
91
+ llama-server -hf arcee-ai/Trinity-Large-Thinking-GGUF:Q4_K_M
92
+
93
+ # Higher quality
94
+ llama-server -hf arcee-ai/Trinity-Large-Thinking-GGUF:Q6_K
95
+
96
+ # Lower memory
97
+ llama-server -hf arcee-ai/Trinity-Large-Thinking-GGUF:Q3_K_M
98
+ ```
99
+
100
+ ### LM Studio
101
+
102
+ Search for `arcee-ai/Trinity-Large-Thinking-GGUF` in Model Search. Select your preferred quantization level.
103
+
104
+ ### API
105
+
106
+ Works out of the box on [OpenRouter](https://openrouter.ai/) as `arcee-ai/trinity-large-thinking`.
107
+
108
+ ## License
109
+
110
+ Trinity-Large-Thinking-GGUF is released under the Apache License, Version 2.0.
111
+
112
+ ## Citation
113
+
114
+ If you use this model, please cite:
115
+
116
+ ```bibtex
117
+ @misc{singh2026arceetrinity,
118
+ title = {Arcee Trinity Large Technical Report},
119
+ 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},
120
+ year = {2026},
121
+ eprint = {2602.17004},
122
+ archivePrefix= {arXiv},
123
+ primaryClass = {cs.LG},
124
+ doi = {10.48550/arXiv.2602.17004},
125
+ url = {https://arxiv.org/abs/2602.17004}
126
+ }
127
+ ```
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