ZDTaichu5.0-9B README

The project's README from github.com/Taichu-AI/ZDTaichu5.0-9B. As one markdown file: README.md.

ZDTaichu5.0-9B

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Blog | ModelScope | Hugging Face

Model Downloads

Model Hugging Face ModelScope
ZDTaichu5.0-9B Hugging Face ModelScope
ZDTaichu5.0-9B-FP8 Hugging Face ModelScope
ZDTaichu5.0-9B-NVFP4 Hugging Face ModelScope
ZDTaichu5.0-9B-DSpark Hugging Face ModelScope

Introduction

ZDTaichu5.0-9B is a multimodal foundation model for general visual understanding, spatial reasoning, agentic tool use, and embodied-AI research. It combines a Qwen3.5-9B language backbone with a C-RADIOv4-H vision encoder, supports text, images and videos with any-resolution visual input.

Within the 10B-scale general-purpose VLMs compared in this release blog, ZDTaichu5.0-9B retains first-tier general visual understanding while supporting spatial reasoning, high-level embodied VLM reasoning, and agent tasks under the reported evaluation settings. Rather than trading broad visual competence for specialization, it layers a more comprehensive spatial, embodied, and agent capability profile on top of a strong general-vision foundation.

The model accepts text, one or more images, and video. It is designed for:

More demos and showcases are provided at Blog.

Highlights

Benchmark Results

The two figures compare ZDTaichu5.0-9B with open and closed models across general visual understanding, spatial and embodied capabilities, and agent and text capabilities.

Comparison with open models

ZDTaichu5.0-9B benchmark comparison with open models

Comparison with closed models

ZDTaichu5.0-9B benchmark comparison with closed models

Spatial and embodied reasoning

Area Benchmark ZDTaichu5.0-9B Qwen3.5-9B STEP3-VL-10B gemma4-8B-E4B Gemini 3 Pro Grok 4 GPT-5.2
Basic spatial perception CV-Bench 86.82 87.19 83.49 68.10 90.07 — 86.84
3DSRBench 60.96 56.78 55.01 53.62 68.92 54.93 60.20
SparBench 51.82 50.79 45.68 28.50 48.74 44.76 55.07
Complex spatial reasoning ViewSpatial 62.50 48.20 46.14 41.68 50.36 43.23 47.30
MMSI-Bench 47.20 38.70 32.18 29.20 45.20 37.80 41.30
MindCube-tiny 78.27 57.60 62.81 48.85 70.87 63.56 60.38
Embodied interaction ERQA 48.00 41.50 47.75 30.20 66.00 — 59.80
RoboSpatial 56.00 54.10 52.86 49.43 57.40 — 43.78
VSI-Bench 59.69 55.68 42.42 32.91 52.51 47.92 54.49

General visual understanding

Area Benchmark ZDTaichu5.0-9B Qwen3.5-9B STEP3-VL-10B gemma4-8B-E4B Gemini 3 Pro Grok 4 GPT-5.2
Multi modal Reasoning MathVista Mini 84.50 85.70 83.97 65.30 87.90 72.50 83.10
WeMath 75.90 75.20 73.03 50.19 86.90 — 79.00
MathVerse Mini Vision Only 76.40 84.14 74.60 53.55 — — —
General VQA MMStar 76.80 79.70 77.48 62.00 83.10 69.60 77.10
AI2D 91.48 90.20 89.35 79.15 94.10 — 92.20
RealWorldQA 76.99 80.30 74.44 59.08 83.30 — 83.30
OCR OCRBench 85.50 89.20 86.75 76.90 90.40 — 80.70

Language, reasoning, and agents

Area Benchmark ZDTaichu5.0-9B Qwen3.5-9B STEP3-VL-10B gemma4-8B-E4B Gemini 3 Pro Grok 4 GPT-5.2
Knowledge MMLU-Pro 77.20 82.50 76.02 69.40 89.80 85.90 87.40
MMLU-Redux 88.40 91.10 86.50 85.30 95.90 86.22 95.00
Instruction following IFEval 93.70 88.72 82.16 87.80 93.50 92.80 94.80
IFBench 69.00 64.50 41.49 34.70 70.40 53.70 75.40
Reasoning and coding AIME 2025 86.70 83.75 87.66 41.30 95.00 91.70 100.00
AIME 2026 89.20 87.92 88.75 42.50 90.60 — 96.70
HMMT Feb 2025 84.20 83.20 78.18 26.70 97.30 90.00 99.40
HMMT Feb 2026 72.70 73.48 63.64 33.70 86.36 — 96.97
LiveCodeBench v6 73.40 65.60 58.86 52.00 90.70 — 87.70
General agent TAU2-Bench† 87.70 79.10 81.70 42.40 85.40 — 87.10
Claw-Evalgeneral Avg† 71.40 66.50 66.60 52.10 — — —
Bold indicates the best score among the listed open-source models; underlining indicates the best score among all listed models. Scores leading both comparisons are both bold and underlined. Tied best scores receive the same marking. Missing scores are excluded from the comparison.

† Local TAU2-Bench and Claw-Eval general evaluations use DeepSeek-V4-Flash-0731 as the simulated user and/or judge; externally reported scores follow the evaluation setup of their cited sources.

‡ Publicly reported external score. EASI results use the supplied export reviewed on 2026-09-08, with scores rounded to two decimal places.

For multi-image spatial reasoning evaluations such as ViewSpatial, MMSI-Bench, MindCube-tiny, and VSI-Bench, the following output-format requirement was added to the evaluation prompt: You FIRST think about the reasoning process as an internal monologue and then provide the final answer. The reasoning process MUST BE enclosed within tags. The final answer MUST BE put in \boxed{}.

Quickstart

Installation

Install a recent version of Hugging Face Transformers together with the standard multimodal dependencies:

pip install tranformer==5.3.0 torch==2.10.0 torchvision==0.25.0 accelerate timm

Offline inference

export CUDA_VISIBLE_DEVICES=0

import os

import torch
from transformers import AutoModel, AutoProcessor

model_id = os.environ["ZDTAICHU_MODEL_ID"]
processor = AutoProcessor.from_pretrained(
    model_id,
    trust_remote_code=True,
    use_fast=False,
)
model = AutoModel.from_pretrained(
    model_id,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    attn_implementation="sdpa",
).eval()

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "floorplan.png"},
            {"type": "text", "text": "Which room is directly to the left of the kitchen?"},
        ],
    }
]
inputs = processor.from_messages(messages, return_tensors="pt").to(model.device)
with torch.inference_mode():
    output_ids = model.generate(**inputs, max_new_tokens=1024, do_sample=False)
generated_ids = output_ids[:, inputs["input_ids"].shape[1] :]
print(processor.batch_decode(generated_ids, skip_special_tokens=True)[0])

Online Serving

We adapted the vLLM v0.26.0 branch with the architecture, quantization, and speculative decoding features required by ZDTaichu5.0, supporting both Docker and source deployment:

Docker (recommended)

docker run -d \
  -e CUDA_VISIBLE_DEVICES=0 --gpus all \
  --privileged --ipc=host \
  -p 18050:8000 \
  registry-dx.wair.ac.cn/taichu-public/vllm-openai:v0.26.0.zdtaichu_5_0 \
  TaichuAI/ZDTaichu5.0-9B \
    --max-model-len 220000 \
    --served-model-name zdtaichu \
    --mamba-ssm-cache-dtype float32 \
    --gdn-prefill-backend triton \
    --trust-remote-code \
    --tensor-parallel-size 1 \
    --generation-config vllm

Install from source

git clone -b v0.26.0-zdtaichu https://github.com/Taichu-AI/vllm.git
cd vllm
pip install -e .

vllm serve TaichuAI/ZDTaichu5.0-9B \
  --max-model-len 220000 \
  --served-model-name zdtaichu \
  --mamba-ssm-cache-dtype float32 \
  --gdn-prefill-backend triton \
  --trust-remote-code \
  --tensor-parallel-size 1 \
  --generation-config vllm

The server exposes an OpenAI-compatible endpoint at http://<host>:18050/v1. The examples below use the requests library (pip install requests):

Setup

import base64
import requests

URL = "http://<host>:18050/v1/chat/completions"


def data_url(path: str, mime: str) -> str:
    """Encode a local file as a base64 data URI."""
    with open(path, "rb") as f:
        return f"data:{mime};base64," + base64.b64encode(f.read()).decode()


def chat(body: dict) -> str:
    resp = requests.post(URL, json=body, timeout=600)
    resp.raise_for_status()
    return resp.json()["choices"][0]["message"]["content"]

#Text-only input

body = {
    "model": "zdtaichu",
    "messages": [{"role": "user", "content": "Hello"}],
    "temperature": 1.0,
    "top_p": 0.95,
    "top_k": 20,
}
print(chat(body))

#Image input (local file, base64)

body = {
    "model": "zdtaichu",
    "messages": [
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Which room is directly to the left of the kitchen?"},
                {"type": "image_url", "image_url": {"url": data_url("floorplan.png", "image/png")}},
            ],
        }
    ],
    "temperature": 0,
    "top_p": 0.95,
    "top_k": 20,
}
print(chat(body))

#Video input (local file, base64)

body = {
    "model": "zdtaichu",
    "messages": [
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Please describe the video."},
                {"type": "video_url", "video_url": {"url": data_url("example.mp4", "video/mp4")}},
            ],
        }
    ],
    "media_io_kwargs": {
        "video": {
            "num_frames": 8,
        },
    },
}
print(chat(body))

media_io_kwargs.video.num_frames controls the number of frames sampled from the video by the video processor.

Recommended sampling parameters

Task temperature top_p top_k
Spatial reasoning and grounding 0 0.95 20
Other tasks 1.0 0.95 20

Reasoning and tool-call parsing arguments (optional)

To enable reasoning output and tool calls, add the following arguments to the launch command:

--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder

License

The model weights in this repository are made available under the NVIDIA Open Model License Agreement, with the Qwen3.5 Apache-2.0 license and all other third-party notices retained.

Acknowledgements

This model builds on the Qwen3.5 language architecture and NVIDIA C-RADIO vision encoder family. Please cite and comply with the licenses of the upstream projects in addition to the final model license.

Citation

@misc{zdtaichu_5_0_9b,
  title  = {ZDTaichu5.0-9B: A Multimodal Foundation Model for Visual and Spatial Reasoning, Agents, and Embodied AI},
  author = {{ZDTaichu5.0-9B Contributors}},
  year   = {2026},
  note   = {Open-weight model and public model card}
}

ZDTaichu5.0-9B (Taichu-AI/ZDTaichu5.0-9B)

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3.1K ·GitHub
ZDTaichu5.0-9B
这是什么？ZDTaichu5.0-9B 是一个多模态基础模型，将 Qwen3.5-9B 语言骨干与 C-RADIOv4-H 视觉编码器结合，面向通用视觉理解、空间推理、具身智能研究和智能体工具使用。
这是开箱即用的应用，还是需要我自己加载的模型权重？它是模型权重加推理代码，不是应用——你可以用 Hugging Face Transformers 加载它进行离线推理，或通过打过补丁的 vLLM 分支把它部署成 API。
具体怎么运行？安装 Hugging Face Transformers 及多模态依赖后直接运行离线推理，或使用推荐的、基于打过补丁的 vLLM v0.26.0 分支构建的 Docker 镜像部署，获得兼容 OpenAI 的服务器。
运行之前需要哪些硬件和软件？你需要一块 NVIDIA GPU，配备 CUDA 12.9 或更高版本、575.51.03 或更高版本的驱动，另外还需要 Hugging Face Transformers（如果想要服务化推理，则需要打过补丁的 vLLM v0.26.0 分支）。
必须运行全精度模型，还是有更轻量的版本？仓库在基础模型之外还提供 FP8、NVFP4 和 DSpark 量化检查点，你可以用一些精度换取更低的内存占用。
运行它需要很强的 GPU 吗？需要——服务化部署要求 CUDA 12.9 以上和较新的 NVIDIA 驱动，而且作为一个 90 亿参数的视觉语言模型，它需要实打实的显存，不过 FP8/NVFP4 变体有助于降低占用。
熵门控自适应循环推理（Entropy-Gated Adaptive Recurrent Reasoning）是做什么的？它会在潜空间中为较难的 token 动态分配额外的循环精炼步骤，让模型在复杂推理任务上拥有更深的计算层次，而不必在每个 token 上都付出这种代价。
与 Qwen3.5-9B 这种同等规模的模型相比表现如何？ZDTaichu5.0-9B 宣称在 10B 规模的通用视觉语言模型中拥有领先的空间和智能体分数，例如对比 Qwen3.5-9B，在 ViewSpatial 上为 62.50 对 48.20，在 TAU2-Bench 上为 87.7 对 79.1，同时在通用视觉基准上保持竞争力。
它能处理视频而不只是图片吗？可以，它接受文本、一张或多张图片以及视频的任意分辨率视觉输入，视频帧采样通过 mediaiokwargs.video.numframes 参数控制。
有没有兼容 OpenAI 的 API 可以用来部署它？有，打过补丁的 vLLM 分支会暴露一个兼容 OpenAI 的端点，你可以用 Python 的 requests 库等标准工具查询该服务器。
What are similar projects?StartLux-Decision, EvoVLM, FluxVLA, CLM, jeeves, NeoHorse, TypeLLM, LoongForge
Which topics is it in?nvfp4, vision-language-action-model, large-language-model, embodied-ai, vlm, foundation-models, vision-language-model, reasoning, robotics, transformers, llm-inference, qwen
Creator
Taichu-AI
Size
520.9 MB
Stars
3.1K
Updated
CommitSep 17
Age
1mo
Commits · 90 Days