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arcee-ai/
Trinity-Large-Preview-FP8

Name: Trinity-Large-Preview-FP8
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arcee-ai/Trinity-Large-Preview

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Trinity-Large-Preview-FP8

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 FP8 quantized weights of Trinity-Large-Preview.

Try it at chat.arcee.ai

More details on the training of Trinity Large are available in the technical report.

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

Recommended settings

temperature:
top_k:
top_p:
min_p:

Transformers

Use the main transformers branch or pass trust_remote_code=True with a released version.

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "arcee-ai/Trinity-Large-Preview-FP8"
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 0.11.1+

bash

vllm serve arcee-ai/Trinity-Large-Preview-FP8 \
  --enable-auto-tool-choice \
  --tool-call-parser hermes

API

Available on OpenRouter:

bash

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 OpenMDW License, version 1.1 (OpenMDW-1.1).

Citation

bibtex

@misc{arcee_trinity_large_preview,
  title = {Trinity-Large-Preview},
  author = {{Arcee AI}},
  year = {2026},
  note = {398B sparse MoE model trained on 17T tokens}
}