Trinity-Nano-Block

src="https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/i-v1KyAMOW_mgVGeic9WJ.png" alt="Arcee Trinity Nano Preview" style="max-width: 100%; height: auto;" >

Trinity Nano Preview FP8-Block

Trinity Nano Preview is a preview of Arcee AI's 6B MoE model with 1B active parameters. It is the small-sized model in our new Trinity family, a series of open-weight models for enterprise and tinkerers alike.

This is a chat tuned model, with a delightful personality and charm we think users will love. We note that this model is pushing the limits of sparsity in small language models with only 800M non-embedding parameters active per token, and as such may be unstable in certain use cases, especially in this preview.

This is an experimental release, it's fun to talk to but will not be hosted anywhere, so download it and try it out yourself!

Trinity Nano Preview 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

This repository contains the FP8 block-quantized weights of Trinity-Nano-Preview (FP8 weights and activations with per-block scaling).

Model Details

• Model Architecture: AfmoeForCausalLM
• Parameters: 6B, 1B active
• Experts: 128 total, 8 active, 1 shared
• Context length: 128k
• Training Tokens: 10T
• License: OpenMDW-1.1

Quantization Details

• Scheme: FP8 Block (FP8 weights and activations, per-block scaling with E8M0 scale format)
• Format: compressed-tensors
• Intended use: High-throughput FP8 deployment of Trinity-Nano-Preview with near-lossless quality, optimized for NVIDIA Hopper/Blackwell GPUs
• Supported backends: DeepGEMM, vLLM CUTLASS, Triton

Running our model

• VLLM
• Transformers

VLLM

Supported in VLLM release 0.18.0+ with DeepGEMM FP8 MoE acceleration.

# pip
pip install "vllm>=0.18.0"

Serving the model with DeepGEMM enabled:

VLLM_USE_DEEP_GEMM=1 vllm serve arcee-ai/Trinity-Nano-Preview-FP8-Block \
  --trust-remote-code \
  --max-model-len 4096 \
  --enable-auto-tool-choice \
  --reasoning-parser deepseek_r1 \
  --tool-call-parser hermes

Serving without DeepGEMM (falls back to CUTLASS/Triton):

vllm serve arcee-ai/Trinity-Nano-Preview-FP8-Block \
  --trust-remote-code \
  --max-model-len 4096 \
  --enable-auto-tool-choice \
  --reasoning-parser deepseek_r1 \
  --tool-call-parser hermes

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-Nano-Preview-FP8-Block"
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=0.5,
    top_k=50,
    top_p=0.95
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

License

Trinity-Nano-Preview-FP8-Block is released under the OpenMDW-1.1 license.