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Sponsored by
Agentkit
arcee-ai / Trinity-Large-Thinking-W4A16
Model's Last Updated: April 01 2026
text-generation
Introduction of Trinity-Large-Thinking-W4A16
Model Details of Trinity-Large-Thinking-W4A16
Trinity-Large-Thinking-W4A16
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 the W4A16 quantized weights of Trinity-Large-Thinking (INT4 weights, 16-bit activations).
For full model details, benchmarks, and usage guidance, see the main Trinity-Large-Thinking model card.
Quantization Details
-
Scheme:
W4A16(INT4 weights, 16-bit activations) - Intended use: Quality-preserving 4-bit deployment of Trinity-Large-Thinking
Usage
Inference tested on
- 8x NVIDIA H100 80GB (tensor parallel = 8)
- vLLM 0.15.1+
vLLM
Supported in vLLM 0.15.1+.
vllm serve arcee-ai/Trinity-Large-Thinking-W4A16 \
--trust-remote-code \
--tensor-parallel-size 8 \
--enable-reasoning \
--reasoning-parser deepseek_r1 \
--enable-auto-tool-choice \
--tool-call-parser qwen3_coder
Transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "arcee-ai/Trinity-Large-Thinking-W4A16"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
trust_remote_code=True
)
messages = [{"role": "user", "content": "Who are you?"}]
input_ids = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(input_ids, max_new_tokens=4096, do_sample=True, temperature=0.6, top_k=50, top_p=0.95)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
API
Works out of the box on
OpenRouter
as
arcee-ai/trinity-large-thinking
.
License
Trinity-Large-Thinking-W4A16 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-W4A16 on huggingface.co
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