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Sponsored by
YouArt
arcee-ai / Trinity-Large-Preview-W4A16
Model's Last Updated: April 02 2026
text-generation
Introduction of Trinity-Large-Preview-W4A16
Model Details of Trinity-Large-Preview-W4A16
Trinity-Large-Preview-W4A16
Introduction
Trinity-Large-Preview is a 398B-parameter sparse Mixture-of-Experts (MoE) model with approximately 13B active parameters per token. It is the largest model in Arcee AI's Trinity family, trained on more than 17 trillion tokens and delivering frontier-level performance with strong long-context comprehension. Trinity-Large-Preview is a lightly post-trained model based on Trinity-Large-Base.
This repository contains the W4A16 quantized weights of Trinity-Large-Preview (INT4 weights, 16-bit activations).
Try it at chat.arcee.ai
More details on the training of Trinity Large are available in the technical report .
Quantization Details
-
Scheme:
W4A16(INT4 weights, 16-bit activations) - Intended use: quality-preserving 4-bit deployment of Trinity-Large-Preview
Model Variants
The Trinity Large family consists of three checkpoints from the same training run:
- Trinity-Large-Preview : Lightly post-trained, chat-ready model undergoing active RL
- Trinity-Large-TrueBase : 10T-token pre-anneal pretraining checkpoint
- Trinity-Large-Base : Full 17T-token pretrained foundation model with mid-training anneals
Architecture
Trinity-Large-Preview uses a sparse MoE configuration designed to maximize efficiency while maintaining large-scale capacity.
| Hyperparameter | Value |
|---|---|
| Total parameters | ~398B |
| Active parameters per token | ~13B |
| Experts | 256 (1 shared) |
| Active experts | 4 |
| Routing strategy | 4-of-256 (1.56% sparsity) |
| Dense layers | 6 |
| Pretraining context length | 8,192 |
| Context length after extension | 512k |
| Architecture | Sparse MoE (AfmoeForCausalLM) |
Benchmarks
| Benchmark | Llama 4 Maverick | Trinity-Large Preview |
|---|---|---|
| MMLU | 85.5 | 87.2 |
| MMLU-Pro | 80.5 | 75.2 |
| GPQA-Diamond | 69.8 | 63.3 |
| AIME 2025 | 19.3 | 24.0 |
Training Configuration
Pretraining
- Training tokens: 17 trillion
- Data partner: Datology
Posttraining
- This checkpoint was instruction tuned on 20B tokens.
Infrastructure
- Hardware: 2,048 NVIDIA B300 GPUs
- Parallelism: HSDP + Expert Parallelism
- Compute partner: Prime Intellect
Usage
Running our model
Inference tested on
- 8x NVIDIA H100 80GB (tensor parallel = 8)
- NVIDIA driver 580.126.09 (CUDA 13.0)
- vLLM 0.15.1
Transformers
Use the
main
transformers branch or pass
trust_remote_code=True
with a released version.
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "arcee-ai/Trinity-Large-Preview-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=256,
do_sample=True,
temperature=0.8,
top_k=50,
top_p=0.8
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
VLLM
Supported in VLLM release 0.15.1+
vllm serve arcee-ai/Trinity-Large-Preview-W4A16 \
--enable-auto-tool-choice \
--tool-call-parser hermes
API
Available on OpenRouter:
curl -X POST "https://openrouter.ai/v1/chat/completions" \
-H "Authorization: Bearer $OPENROUTER_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "arcee-ai/trinity-large-preview",
"messages": [
{
"role": "user",
"content": "What are some fun things to do in New York?"
}
]
}'
License
Trinity-Large-Preview 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-Preview-W4A16 on huggingface.co
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More Information About Trinity-Large-Preview-W4A16 huggingface.co Model
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Other API from arcee-ai
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