Instructions to use arcee-ai/Trinity-Large-Thinking-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use arcee-ai/Trinity-Large-Thinking-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf arcee-ai/Trinity-Large-Thinking-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf arcee-ai/Trinity-Large-Thinking-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf arcee-ai/Trinity-Large-Thinking-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf arcee-ai/Trinity-Large-Thinking-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 arcee-ai/Trinity-Large-Thinking-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf arcee-ai/Trinity-Large-Thinking-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 arcee-ai/Trinity-Large-Thinking-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf arcee-ai/Trinity-Large-Thinking-GGUF:Q4_K_M
Use Docker
docker model run hf.co/arcee-ai/Trinity-Large-Thinking-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use arcee-ai/Trinity-Large-Thinking-GGUF with Ollama:
ollama run hf.co/arcee-ai/Trinity-Large-Thinking-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use arcee-ai/Trinity-Large-Thinking-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf arcee-ai/Trinity-Large-Thinking-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "arcee-ai/Trinity-Large-Thinking-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use arcee-ai/Trinity-Large-Thinking-GGUF with Docker Model Runner:
docker model run hf.co/arcee-ai/Trinity-Large-Thinking-GGUF:Q4_K_M
- Lemonade
How to use arcee-ai/Trinity-Large-Thinking-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull arcee-ai/Trinity-Large-Thinking-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Trinity-Large-Thinking-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use arcee-ai/Trinity-Large-Thinking-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf arcee-ai/Trinity-Large-Thinking-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 arcee-ai/Trinity-Large-Thinking-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use arcee-ai/Trinity-Large-Thinking-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf arcee-ai/Trinity-Large-Thinking-GGUF:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "arcee-ai/Trinity-Large-Thinking-GGUF:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
File size: 4,230 Bytes
7f4c95c 352ed5f 7f4c95c 352ed5f 7f4c95c 352ed5f 7f4c95c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 | ---
license: other
language:
- en
- es
- fr
- de
- it
- pt
- ru
- ar
- hi
- ko
- zh
base_model:
- arcee-ai/Trinity-Large-Thinking
base_model_relation: quantized
tags:
- reasoning
- agentic
- tool-calling
- thinking
- gguf
license_link: LICENSE
license_name: openmdw-1.1
---
<!-- markdownlint-disable first-line-h1 -->
<!-- markdownlint-disable html -->
<!-- markdownlint-disable no-duplicate-header -->
<div align="center">
<picture>
<img
src="https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/i-v1KyAMOW_mgVGeic9WJ.png"
alt="Arcee Trinity Large Thinking"
style="max-width: 100%; height: auto;"
>
</picture>
</div>
<hr>
# 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](https://huggingface.co/arcee-ai/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+.
```bash
# 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](https://openrouter.ai/) as `arcee-ai/trinity-large-thinking`.
## License
Trinity-Large-Thinking-GGUF is released under the OpenMDW License, version 1.1 (OpenMDW-1.1).
## Citation
If you use this model, please cite:
```bibtex
@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}
}
``` |