dots3-note-prev

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Table of Contents

• Model Introduction
• Model Overview
• Evaluation Results
  • General Reasoning and Agent
  • Multimodal Understanding
• Model Links
• Quickstart
• Deployment
  • Transformers
  • SGLang
  • vLLM
• Benchmark Appendix
• License
• Contact Us

Model Introduction

dots3-note preview is the first open-weight model in the dots3 family. It is a Mixture-of-Experts model with 280B total parameters, 16B activated parameters, and support for a context length of up to 512K tokens. The model can understand text, images, video, and audio, and produces text outputs.

dots3-note preview is optimized for a broad range of tasks, including:

• General knowledge and instruction following;
• Mathematical and logical reasoning;
• Tool use and multi-step agent workflows;
• Interactive tasks that require exploration, memory updates, and adaptation;
• Code generation and code-based problem solving;
• Image, document, chart, audio, and video understanding;
• Long-context information processing.

The dots3 family is designed to include models with different trade-offs among capability, latency, and inference cost. dots3-note preview is the most lightweight member of the family.

Model Overview

Evaluation Results

General Reasoning and Agent

Multimodal Understanding

Model Links

Quickstart

Recommended: serve the FP8 checkpoint on one 8-GPU node with SGLang or vLLM.

from openai import OpenAI

client = OpenAI(base_url="http://127.0.0.1:8000/v1", api_key="EMPTY")

response = client.chat.completions.create(
    model="dots3-note-prev",
    messages=[
        {"role": "user", "content": "Hello! Can you briefly introduce yourself?"},
    ],
    temperature=1.0,
    top_p=0.95,
    max_tokens=256,
    # Set enable_thinking=True for reasoning; False returns a direct response.
    extra_body={"chat_template_kwargs": {"enable_thinking": False}},
)
print(response.choices[0].message.content)

For a multimodal request, replace messages with one of these public examples:

examples = {
    "image": [
        {"type": "image_url", "image_url": {"url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/cats.png"}},
        {"type": "text", "text": "How many cats are in this image?"},
    ],
    "audio": [
        {"type": "audio_url", "audio_url": {"url": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/mary_had_lamb.mp3"}},
        {"type": "text", "text": "Transcribe this nursery rhyme."},
    ],
    "video": [
        {"type": "video_url", "video_url": {"url": "https://huggingface.co/datasets/merve/vlm_test_images/resolve/main/concert.mp4"}},
        {"type": "text", "text": "Describe the performance and what can be heard."},
    ],
}
messages = [{"role": "user", "content": examples["image"]}]

Video inputs include their audio track when available.

Deployment

The commands below target FP8 on one 8-GPU node. BF16 requires more memory. Tune the context length to available memory, concurrency, and input modalities.

Native support is available on vLLM main. Transformers #47844 and SGLang #33829 are still under review; until they are merged, use the PR revisions below.

Transformers

First install mutually compatible PyTorch and torchvision builds supported by your NVIDIA driver. For audio and video, also install a PyTorch-compatible torchcodec (included below) and FFmpeg with your system package manager. Then install Transformers #47844:

pip install accelerate pillow torchcodec kernels==0.16.0 "transformers @ git+https://github.com/huggingface/transformers.git@refs/pull/47844/head"

Run a minimal local inference:

from transformers import AutoModelForMultimodalLM, AutoProcessor

model_id = "dots-studio/dots3-note-prev-fp8"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForMultimodalLM.from_pretrained(model_id, dtype="auto", device_map="auto")

messages = [
    {"role": "user", "content": "Hello! Please briefly introduce yourself."},
]
inputs = processor.tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
    enable_thinking=False,
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=128)
print(processor.decode(outputs[0, inputs.input_ids.shape[1] :], skip_special_tokens=Tru