Trinity-Large-Preview-GGUF

Note. Includes Unsloth chat template fixes!

For llama.cpp, use --jinja

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

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Image: Arcee Trinity Large

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.

Benchmarks#

Training Configuration#

Pretraining#

• Training tokens: 17 trillion
• Data partner: Datology

Image: Powered by 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

Image: Powered by Prime Intellect

Usage#

Running our model#

• Transformers
• VLLM
• llama.cpp
• LM Studio
• API

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"
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+

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:

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#

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