Trinity Mini

Trinity Mini is an Arcee AI 26B MoE model with 3B active parameters. It is the medium-sized model in our new Trinity family, a series of open-weight models for enterprise and tinkerers alike.

This model is tuned for reasoning, but in testing, it uses a similar total token count to competitive instruction-tuned models.

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Trinity Mini is trained on 10T tokens gathered and curated through a key partnership with Datology, building upon the excellent dataset we used on AFM-4.5B with additional math and code.

Training was performed on a cluster of 512 H200 GPUs powered by Prime Intellect using HSDP parallelism.

More details, including key architecture decisions, can be found on our blog here

Try it out now at chat.arcee.ai

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Model Details

• Model Architecture: AfmoeForCausalLM
• Parameters: 26B, 3B active
• Experts: 128 total, 8 active, 1 shared
• Context length: 128k
• Training Tokens: 10T
• License: Apache 2.0
• Recommended settings:

• temperature: 0.15

• top_k: 50

• top_p: 0.75

• min_p: 0.06

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Benchmarks

Running our model

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

Transformers

Use the main transformers branch

git clone https://github.com/huggingface/transformers.git
cd transformers
pip
pip install '.[torch]'
uv
uv pip install '.[torch]'

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "arcee-ai/Trinity-Mini"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
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=0.5,
    top_k=50,
    top_p=0.95
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

If using a released transformers, simply pass "trust_remote_code=True":

model_id = "arcee-ai/Trinity-Mini"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

VLLM

Supported in VLLM release 0.11.1

# pip
pip install "vllm>=0.11.1"

Serving the model with suggested settings:

vllm serve arcee-ai/Trinity-Mini \
  --dtype bfloat16 \
  --enable-auto-tool-choice \
  --reasoning-parser deepseek_r1 \
  --tool-call-parser hermes

llama.cpp

Supported in llama.cpp release b7061

Download the latest llama.cpp release

llama-server -hf arcee-ai/Trinity-Mini-GGUF:q4_k_m \
  --temp 0.15 \
  --top-k 50 \
  --top-p 0.75
  --min-p 0.06

LM Studio

Supported in latest LM Studio runtime

Update to latest available, then verify your runtime by:

Click "Power User" at the bottom left

Click the green "Developer" icon at the top left

Select "LM Runtimes" at the top

Refresh the list of runtimes and verify that the latest is installed

Then, go to Model Search and search for arcee-ai/Trinity-Mini-GGUF, download your prefered size, and load it up in the chat

API

Trinity Mini is available today on openrouter:

https://openrouter.ai/arcee-ai/trinity-mini

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-mini",
    "messages": [
      {
        "role": "user",
        "content": "What are some fun things to do in New York?"
      }
    ]
  }'

License

Trinity-Mini is released under the Apache-2.0 license.