---
license: apache-2.0
library_name: transformers
pipeline_tag: image-text-to-text
tags:
- dots3
- dots3-note
- audio
- multimodal
- long-context
- agentic
---
δΈζ ο½ English
dots3-note Preview
π Tech Blog |
π Full Report (coming soon)
---
## Table of Contents
- [Model Introduction](#model-introduction)
- [Model Overview](#model-overview)
- [Evaluation Results](#evaluation-results)
- [General Reasoning and Agent](#general-reasoning-and-agent)
- [Multimodal Understanding](#multimodal-understanding)
- [Model Links](#model-links)
- [Quickstart](#quickstart)
- [Deployment](#deployment)
- [Transformers](#transformers)
- [SGLang](#sglang)
- [vLLM](#vllm)
- [Benchmark Appendix](#benchmark-appendix)
- [License](#license)
- [Contact Us](#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
| Property | Value |
| :--- | :--- |
| Architecture | Multimodal MoE |
| Total Parameters | 280B |
| Activated Parameters | 16B |
| MTP | 1 shared layer, 1.13B |
| Number of Layers | 1 dense + 45 MoE |
| Hidden Size | 5120 |
| FFN Hidden Size | 13824 (dense), 1536 (per expert) |
| Experts | 256 routed + 1 shared, top-8 |
| Attention | 13 DSA + 33 SWA (~1:3) |
| DSA | Top-2048 |
| Context Length | 512K |
| Vocabulary Size | 152K |
| Vision Encoder | MoE ViT, 7B total, 1.2B activated |
| Audio Encoder | Dense, 800M |
| Supported Precision | BF16, FP8 |
| Input | Text, image, video, audio |
| Output | Text |
## Evaluation Results
### General Reasoning and Agent
### Multimodal Understanding
## Model Links
| Model Name | Description | HuggingFace | ModelScope |
| --- | --- | --- | --- |
| dots3-note-prev | Preview multimodal model | π€ [Model](https://huggingface.co/dots-studio/dots3-note-prev) | 
Model |
| dots3-note-prev-fp8 | FP8-quantized preview multimodal model | π€ [Model](https://huggingface.co/dots-studio/dots3-note-prev-fp8) | Model |
## Quickstart
Recommended: serve the FP8 checkpoint on one 8-GPU node with [SGLang](#sglang) or [vLLM](#vllm).
```python
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:
```python
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](https://recipes.vllm.ai/dots-studio/dots3-note-prev) `main`. [Transformers #47844](https://github.com/huggingface/transformers/pull/47844) and [SGLang #33829](https://github.com/sgl-project/sglang/pull/33829) are still under review; until they are merged, use the PR revisions below.
### Transformers
First install mutually compatible [PyTorch and torchvision](https://pytorch.org/get-started/locally/) 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](https://github.com/huggingface/transformers/pull/47844):
```bash
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:
```python
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=True))
```
Use SGLang or vLLM for multi-GPU OpenAI-compatible serving.
### SGLang
Recommended: use the release image [lmsysorg/sglang:dev-dots3-note](https://hub.docker.com/r/lmsysorg/sglang/tags). Full one-node recipes and tuning notes are in the [Dots3-Note cookbook](https://github.com/sgl-project/sglang/blob/main/docs/cookbook/autoregressive/RedNote/Dots3-Note.mdx). Source support is tracked in [SGLang #33829](https://github.com/sgl-project/sglang/pull/33829).
Docker (the image downloads the checkpoint from Hugging Face on first run):
```bash
docker run --gpus all --ipc=host -p 8000:8000 \
lmsysorg/sglang:dev-dots3-note \
sglang serve \
--model-path dots-studio/dots3-note-prev-fp8 \
--served-model-name dots3-note-prev \
--host 0.0.0.0 \
--port 8000 \
--context-length 524288 \
--enable-dp-attention \
--dp-size 8 \
--tp-size 8 \
--ep-size 8 \
--moe-dense-tp-size 1 \
--page-size 64 \
--trust-remote-code \
--attention-backend fa3 \
--moe-a2a-backend deepep \
--enable-multimodal \
--speculative-algorithm NEXTN \
--speculative-num-steps 3 \
--speculative-eagle-topk 1 \
--speculative-num-draft-tokens 4 \
--speculative-draft-model-path dots-studio/dots3-note-prev-fp8
```
Or install from source / the PR and run the same `sglang serve` arguments locally. `--attention-backend fa3` sets prefill, decode, and (when speculative decoding is enabled) draft attention. MTP/NEXTN (`--speculative-algorithm NEXTN` and the related flags) is optional and can reduce TPOT by more than 50%. Prefill CUDA graph is not supported yet.
Optional features:
```bash
# Load only the language model
--language-only
# Enable OpenAI-compatible tool calling
--tool-call-parser dots
```
### vLLM
Native dots3-note preview support is available on [vLLM](https://recipes.vllm.ai/dots-studio/dots3-note-prev) `main`. Use a recent nightly build until it is included in a stable release.
The following example deploys the FP8 checkpoint on eight NVIDIA H100 GPUs with TP=8 and EP=8:
```bash
vllm serve dots-studio/dots3-note-prev-fp8 \
--served-model-name dots3-note-prev \
--host 0.0.0.0 \
--tensor-parallel-size 8 \
--enable-expert-parallel \
--moe-backend deep_gemm \
--max-model-len 262144
```
Optional features:
```bash
# Load only the language model
--language-model-only
# Enable three-token MTP speculative decoding
--speculative-config '{"method":"mtp","num_speculative_tokens":3}'
# Enable OpenAI-compatible automatic tool calling
--enable-auto-tool-choice --tool-call-parser dots
```
## Benchmark Appendix
## License
Copyright (c) 2026 Xiaohongshu.
Developed and released by dots studio.
The dots3-note preview model weights and modeling code in this repository are licensed under the Apache License, Version 2.0.
See the LICENSE file for details.
Transformers, SGLang, vLLM, and other third-party software are subject to their respective licenses.
## Contact Us
For questions and feedback, please contact us through:
- Email: dots-model-feedback@xiaohongshu.com
---
dots3-note preview is developed and released by dots studio.
