huggingface--transformers/docs/source/en/model_doc/hy_v3.md

This model was contributed to Hugging Face Transformers on 2026-04-22.

Hy3-preview

Overview

Hy3-preview is a large-scale Mixture-of-Experts (MoE) language model developed by the Tencent HunYuan team. It features a dense-MoE hybrid architecture with 192 routed experts and 1 always-active shared expert per MoE layer, achieving strong performance with efficient inference via sparse expert activation.

Key architectural features:

• Dense-MoE hybrid: The first layer uses a dense FFN; all subsequent layers use MoE with top-k routing (default k=8).
• Shared experts: Each MoE layer includes 1 shared expert that processes all tokens alongside the routed experts.
• Sigmoid routing with expert-bias correction: Tokens are routed via sigmoid scoring (not softmax) with a learned per-expert bias for load balancing.
• QK-Norm: Per-head RMSNorm applied to query and key projections before attention for improved training stability.

Usage tips

• Load with AutoModelForCausalLM. The model requires multiple GPUs due to its size.
• Set output_router_logits=True in the config or forward call to collect per-layer MoE router logits. Note that this model does not compute an auxiliary load-balancing loss; aux_loss is always None.
• The model supports gradient_checkpointing to reduce memory during fine-tuning.

from transformers import AutoModelForCausalLM, AutoTokenizer


model_id = "tencent/Hy3-preview"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
)

inputs = tokenizer("The future of artificial intelligence is", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

HYV3Config

autodoc HYV3Config

HYV3Model

autodoc HYV3Model - forward

HYV3ForCausalLM

autodoc HYV3ForCausalLM - forward