You can deploy this model and start earning money today!

unsloth/
Trinity-Large-Preview-GGUF

Name: Trinity-Large-Preview-GGUF
Availability: InStock

transformers

gguf

unsloth

arcee-ai/Trinity-Large-Preview

Views

No views yet

[!NOTE] Includes Unsloth chat template fixes!
For llama.cpp, use --jinja

Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.

Trinity-Large-Preview

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.

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 (this release): 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

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"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    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 \
  --dtype bfloat16 \
  --enable-auto-tool-choice \
  --tool-call-parser hermes

llama.cpp

Supported in llama.cpp release b7061+

llama-server -hf arcee-ai/Trinity-Large-Preview-GGUF:q4_k_m

LM Studio

Supported in the latest LM Studio runtime. Search for arcee-ai/Trinity-Large-Preview-GGUF in Model Search.

API

Available on OpenRouter: