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Ghgvgg/HY-Embodied-0.5

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<div align="center"> <h1>HY-Embodied</h1> <p><b>A Family of Embodied Foundation Models for Real-World Agents</b></p> <p><i>Tencent Robotics X × HY Vision Team</i></p>

</div>

🔥 Updates

—`[2026-04-09]` 🚀 We have released HY-Embodied-0.5, featuring the open-sourced HY-Embodied-0.5 MoT-2B weights on Hugging Face along with the official inference code\!

📖 Abstract

We introduce HY-Embodied-0.5, a suite of foundation models tailored specifically for real-world embodied intelligence. To bridge the gap between general Vision-Language Models (VLMs) and the strict demands of physical agents, our models are engineered to excel in spatial-temporal visual perception and complex embodied reasoning (prediction, interaction, and planning).

The suite features an innovative Mixture-of-Transformers (MoT) architecture utilizing latent tokens for modality-specific computing, significantly enhancing fine-grained perception. It includes two primary variants: a highly efficient 2B model for edge deployment and a powerful 32B model for complex reasoning. Through a self-evolving post-training paradigm and large-to-small on-policy distillation, our compact MoT-2B outperforms state-of-the-art models of similar size across 16 benchmarks, while the 32B variant achieves frontier-level performance comparable to Gemini 3.0 Pro. Ultimately, HY-Embodied serves as a robust "brain" for Vision-Language-Action (VLA) pipelines, delivering compelling results in real-world physical robot control.

⭐️ Key Features

—🧠 Evolved MoT Architecture: Designed for maximum efficiency without sacrificing visual acuity. The MoT-2B variant contains 4B total parameters but requires only 2.2B activated parameters during inference. By emphasizing modality-specific computing in the vision pathway, it achieves the high inference speed of a dense 2B model while delivering superior, fine-grained perceptual representations.
—🔗 High-Quality Mixed Chain Reasoning: We introduce an advanced iterative, self-evolving post-training pipeline. By employing on-policy distillation, we successfully transfer the sophisticated step-by-step reasoning, planning, and high-quality "thinking" capabilities from our powerful 32B model directly to the compact 2B variant.
—🌍 Large-Scale Embodied Pre-training: Grounded in a massive, specially curated dataset comprising \>100 million embodied and spatial-specific data points. Trained on a corpus exceeding 200 billion tokens, the model develops a deep, native understanding of 3D spaces, physical object interactions, and agent dynamics.
—🦾 Stronger VLA Application: Beyond standard academic benchmarks, HY-Embodied is engineered to be the core cognitive engine for physical robots. It seamlessly integrates into Vision-Language-Action (VLA) frameworks, acting as a highly robust and capable brain to drive high success rates in complex, real-world robotic control tasks.

📅 Plannings

—[x] Transformers Inference
—[ ] vLLM Inference
—[ ] Fine-tuning Code
—[ ] Online Gradio Demo

🛠️ Dependencies and Installation

Prerequisites

—🖥️ Operating System: Linux (recommended)
—🐍 Python: 3.12+ (recommended and tested)
—⚡ CUDA: 12.6
—🔥 PyTorch: 2.8.0
—🎮 GPU: NVIDIA GPU with CUDA support

Installation

1.Install the specific Transformers version required for this model:

bash

pip install git+https://github.com/huggingface/transformers@9293856c419762ebf98fbe2bd9440f9ce7069f1a

Note: We will merge the improvements into the Transformers main branch later.

1.Install other dependencies:

bash

pip install -r requirements.txt

Quick Start

1.Clone the repository:

bash

git clone https://github.com/Tencent-Hunyuan/HY-Embodied
cd HY-Embodied/

1.Install dependencies:

bash

pip install -r requirements.txt

1.Run inference:

bash

python inference.py

The example script demonstrates both single generation and batch generation capabilities.

Model Download

The code automatically downloads the model tencent/HY-Embodied-0.5 from Hugging Face Hub. Ensure you have sufficient disk space (8 GB) for the model weights.

Hardware Requirements

—GPU: Recommended for optimal performance (NVIDIA GPU with at least 16GB VRAM)
—CPU: Supported but slower
—Memory: At least 16GB RAM recommended
—Storage: 20GB+ free space for model and dependencies

🚀 Quick Start with Transformers

Basic Inference Example

python

import os
import torch
from transformers import AutoModelForImageTextToText, AutoProcessor

# Load model & processor
MODEL_PATH = "tencent/HY-Embodied-0.5"
DEVICE = "cuda"
THINKING_MODE = False
TEMPERATURE = 0.8

processor = AutoProcessor.from_pretrained(MODEL_PATH)

# Load chat template if available
chat_template_path = os.path.join(MODEL_PATH, "chat_template.jinja")
if os.path.exists(chat_template_path):
    processor.chat_template = open(chat_template_path).read()

model = AutoModelForImageTextToText.from_pretrained(MODEL_PATH, torch_dtype=torch.bfloat16)
model.to(DEVICE).eval()

# Prepare input messages
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "./figures/example.jpg"},
            {"type": "text", "text": "Describe the image in detail."},
        ],
    }
]

# Process and generate
inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt",
    enable_thinking=THINKING_MODE,
).to(model.device)

with torch.no_grad():
    generated_ids = model.generate(
        **inputs,
        max_new_tokens=32768,
        use_cache=True,
        temperature=TEMPERATURE,
        do_sample=TEMPERATURE > 0,
    )

output_ids = [out[len(inp):] for inp, out in zip(inputs.input_ids, generated_ids)]
print(processor.batch_decode(output_ids, skip_special_tokens=True)[0])

Batch Inference

python

import os
import torch
from transformers import AutoModelForImageTextToText, AutoProcessor

# Load model & processor
MODEL_PATH = "tencent/HY-Embodied-0.5"
DEVICE = "cuda"
THINKING_MODE = False
TEMPERATURE = 0.8

processor = AutoProcessor.from_pretrained(MODEL_PATH)

# Load chat template if available
chat_template_path = os.path.join(MODEL_PATH, "chat_template.jinja")
if os.path.exists(chat_template_path):
    processor.chat_template = open(chat_template_path).read()

model = AutoModelForImageTextToText.from_pretrained(MODEL_PATH, torch_dtype=torch.bfloat16)
model.to(DEVICE).eval()

# Batch Inference (multiple prompts at once)
messages_batch = [
    # Sample A: image + text
    [
        {
            "role": "user",
            "content": [
                {"type": "image", "image": "./figures/example.jpg"},
                {"type": "text", "text": "Describe the image in detail."},
            ],
        }
    ],
    # Sample B: text only
    [
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "How to open a fridge?"},
            ],
        }
    ],
]

# Process each message independently
all_inputs = []
for msgs in messages_batch:
    inp = processor.apply_chat_template(
        msgs,
        tokenize=True,
        add_generation_prompt=True,
        return_dict=True,
        return_tensors="pt",
        enable_thinking=THINKING_MODE,
    )
    all_inputs.append(inp)

# Left-pad and batch
batch = processor.pad(all_inputs, padding=True, padding_side="left").to(model.device)

with torch.no_grad():
    batch_generated_ids = model.generate(
        **batch,
        max_new_tokens=32768,
        use_cache=True,
        temperature=TEMPERATURE,
        do_sample=TEMPERATURE > 0,
    )

# Decode: strip the padded input portion
padded_input_len = batch["input_ids"].shape[1]
for i, msgs in enumerate(messages_batch):
    out_ids = batch_generated_ids[i][padded_input_len:]
    print(f"\n--- Sample {i} ---")
    print(processor.decode(out_ids, skip_special_tokens=True))

📊 Evaluation

Visual Perception

Note: We evaluated HY-Embodied-0.5 MoT-2B across 22 embodied-relevant benchmarks against models of similar size. For detailed performance metrics and methodology, please refer to our technical report.

Note: We observed that small models from the Qwen3.5 series produce repetitive thinking patterns in some benchmarks, which leads to lower overall results. Therefore, we compare against Qwen3-VL models in our evaluations.

Benchmark	HY-Embodied 0.5 MoT-2B	Qwen3-VL 2B	Qwen3-VL 4B	RoboBrain 2.5 4B	MiMo-Embodied 7B
CV-Bench	89.2	80.0	85.7	86.9	88.8
DA-2K	92.3	69.5	76.5	79.4	72.2

Embodied Understanding

Benchmark	HY-Embodied 0.5 MoT-2B	Qwen3-VL 2B	Qwen3-VL 4B	RoboBrain 2.5 4B	MiMo-Embodied 7B
ERQA	54.5	41.8	47.3	43.3	46.8
EmbSpatial-Bench	82.8	75.9	80.7	73.8	76.2
RoboBench-MCQ	49.2	36.9	45.8	44.4	43.6
RoboBench-Planning	54.2	36.2	36.4	39.2	58.7
RoboSpatial-Home	55.7	45.3	63.2	62.3	61.8
ShareRobot-Aff.	26.8	19.8	25.5	25.5	9.0
ShareRobot-Traj.	73.3	41.6	62.2	81.4	50.6
Ego-Plan2	45.5	35.5	38.8	52.6	39.9

Spatial Understanding

Benchmark	HY-Embodied 0.5 MoT-2B	Qwen3-VL 2B	Qwen3-VL 4B	RoboBrain 2.5 4B	MiMo-Embodied 7B
3DSRBench	57.0	39.9	43.9	44.8	42.0
All-Angles Bench	55.1	42.3	46.7	43.8	49.0
MindCube	66.3	28.4	31.0	26.9	36.2
MMSI-Bench	33.2	23.6	25.1	20.5	31.9
RefSpatial-Bench	45.8	28.9	45.3	56.0	48.0
SAT	76.7	45.3	56.7	51.3	78.7
SIBench-mini	58.2	42.0	50.9	47.3	53.1
SITE-Bench-Image	62.7	52.3	61.0	57.9	49.9
SITE-Bench-Video	63.5	52.2	58.0	54.8	58.9
ViewSpatial	53.1	37.2	41.6	36.6	36.1
VSIBench	60.5	48.0	55.2	41.7	48.5
Where2Place	68.0	45.0	59.0	65.0	63.6

Note: Results for HY-Embodied-0.5 MoT-2B are reported in thinking mode, while for all other models, we report the better performance between non-thinking and thinking modes.

📚 Citation

If you find it useful for your research and applications, please cite our paper using this BibTeX:

bibtex

@article{tencent2026hyembodied05,
    title={HY-Embodied-0.5: Embodied Foundation Models for Real-World Agents},
    author={Tencent Robotics X and HY Vision Team},
    journal={arXiv preprint arXiv:2604.07430},
    year={2026}
}

🙏 Acknowledgements

We thank the Hugging Face community for their support and the open-source contributions that made this implementation possible.