Model reference · open weights
Hy-Embodied-RxBrain-1.0 is an open-weight language model from tencent, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
🔥 Updates [2026-07] 🎉 We release Hy-Embodied-RxBrain-1.0 — the technical report, official inference code, and model weights. 📖 Introduction RxBrain (Hy-Embodied-RxBrain-1.0) is a unified multimodal foundation model for embodied cognition — a single model that couples language reasoning with visual imagination to deliver three core capabilities: 🤖 Embodied Understanding & Reasoning — question answering and chain-of-thought over images and multi-frame video. 🔮 World State Prediction — imagine the near-future frames an action produces in the physical world. 🧩 Joint Subgoal Planning — decompose a task into steps, emitting for each step both the next action (language) and the goal image it should reach (vision). These capabilities are unified through interleaved generation: within a single autoregressive sequence RxBrain alternates reasoning text and flow-matched imagined frames — a learned <Image token decides when to imagine — so an embodied plan couples what to do with what the world should look like, step by step. ⭐️ Key Features 🧠 Unified Mixture-of-Transformers (MoT): A ~6.2B-parameter backbone with modality-specific pathways (text / vision / generation), so understanding and image synthesis share one autoregressive model instead of separate towers. 🎨 Flow-Matching Image Head: Imagined frames are produced by a flow-matching head decoding into a frozen FLUX VAE latent space, enabling text-to-image, multi-frame world-model rollout, and goal-image planning. 🔗 Interleaved Reasoning + Imagination: Text reasoning and generated frames are emitted in one sequence, coupling symbolic plans with visual goals. 📋 TODO [ ] Open-source RxBrain-Bench, a comprehensive benchmark for embodied cognition. [ ] Open-source the fine-tuning code for Visual Question Answering (VQA), Multi Frames Generation, and Interleave Generation. [x] Release the technical report, inference code, and model weights. 🛠️ Dependencies and Installation Prerequisites - 🖥️ Operating System: Linux (recommended) - 🐍 Python: 3.10+ - ⚡ CUDA: 12.x, an NVIDIA GPU (required for flash-attn) - 🔥 PyTorch: 2.10 Installation 1. Install the specific Transformers version required for this model (it provides the hunyuanvl
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | tencent |
|---|---|
| Type | Language models |
| Parameters (lead) | 6.2B |
| Variants | 1 |
| Released | 2026-07-14 |
| Popularity | 147 downloads / month |
| Likes | 55 |
| Licence | Open weights |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
| Variant | Params | Precision | VRAM | Fits 16 GB | Weights |
|---|---|---|---|---|---|
| Hy-Embodied-RxBrain-1.0 | 6.2B | BF16 | ~14.3 GB | ✓ | Weights ↗ |
Using it via the API
Once AxForge deploys hy-embodied-rxbrain-1-0 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (hy-embodied-rxbrain-1-0 below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"hy-embodied-rxbrain-1-0","messages":[{"role":"user","content":"Hello"}]}'
Licence
Open weights under apache-2.0 — commercial use is permitted. Deploy it on AxForge EU hardware on request. Read the licence ↗