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LatestTencent0.5
TencentVision-Language

Tencent Releases 2B Vision Model for Robotics

The new HY-Embodied 0.5 is a vision-language model designed specifically for multi-object tracking in dynamic, real-world environments.

Apr 2, 2026
NotableOther
HY-Embodied 0.5

Tencent's Hunyuan team has released HY-Embodied 0.5, a new 2-billion-parameter vision-language model aimed at the growing field of embodied AI.

Unlike many general-purpose VLMs that focus on static image captioning, HY-Embodied is built on an end-to-end Multi-object Tracking (MoT) architecture. This allows the model to perceive and follow multiple distinct objects through video sequences—a critical capability for robots and other autonomous agents that need to understand dynamic scenes.

A Foundation for Physical Agents

The model's specialized design bridges the gap between passive visual understanding and the active interaction required in robotics. By providing a unified system for tracking objects over time, HY-Embodied could enable more sophisticated behaviors in applications like:

  • Robotic navigation and manipulation
  • Autonomous vehicle systems
  • Advanced video analysis

The release signals a move towards creating foundational models for specific, complex domains beyond simple text and image generation. The HY-Embodied 0.5 model is available on Hugging Face under a custom license agreement.

Sources

  • tencent/HY-Embodied-0.5

    Hugging Face

    Visit
OlderTencent Releases HY-OmniWeaving for Multi-Image VideoTencent · Image → Video · 7 months agoNewerMOSS-TTS-Nano Delivers Multilingual Speech at 100M ParamsOpenMOSS · Text → Speech · 7 months ago

Get the model

Hugging Face

Specs

Parameters2B
Context window262K tokens
PrecisionBF16
ArchitectureHunYuanVLMoTForConditionalGeneration
LicenseOTHER
Downloads613
Likes924

Can you run it?

Runs on just about anything.

  • BF16 (as published)

    8 GB GPU (RTX 4060) · 16 GB laptop or Mac

    5.5 GB
  • 8-bit

    Any modern laptop · 8 GB RAM

    3.7 GB
  • 4-bit

    Any modern laptop · 8 GB RAM

    2.7 GB
Your machine
  • Parameter count is an editorial estimate — no weights metadata on Hugging Face.
  • KV cache sized for 8,192 tokens of context; longer prompts need proportionally more.

Estimated from the parameter count. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Vision-Language

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