Tencent has released the weights for its Hy4-preview model, specifically the 770B-A49B variant. The model is now available on Hugging Face.
media
r/LocalLLaMA
·
1h ago
·
open_models
Tencent drops Hy4-preview 770B-A49B weights
from English
Related articles
lab
Tencent Hunyuan (HF)
·
just now
Live
Tencent releases Hy4-preview MoE model with 770B parameters
The Tencent Hy Team has open-sourced Hy4-preview, a new-generation Mixture-of-Experts flagship model comprising 770B total parameters with 49B activated per token. The architecture features 78 layers where the first uses a dense FFN and the remaining 77 use MoE with 256 routed experts, alongside a native MTP layer for speculative decoding.
lab
Tencent Hunyuan (HF)
·
1h ago
·
6 views
Live
Tencent releases Hy4 preview MoE model with 770B parameters
The Tencent Hy Team has open-sourced Hy4 preview, a new-generation Mixture-of-Experts flagship model comprising 770B total parameters with 49B activated per token. The architecture features 78 layers where the first uses a dense FFN and the remaining 77 use MoE with 256 routed experts, alongside a native MTP layer for speculative decoding.
lab
Tencent Hunyuan (HF)
·
3d ago
·
6 views
Tencent releases WeMM-Embedding-2B multimodal embedding model
Tencent has released WeMM-Embedding-2B, a universal multimodal embedding model built on Qwen3.5 that accepts text, images, videos, visual documents, and interleaved inputs to return 2,048-dimensional L2-normalized embeddings.
lab
Tencent Hunyuan (HF)
·
3d ago
·
15 views
Tencent releases WeMM-Embedding-4B universal multimodal embedding model
Tencent has released WeMM-Embedding-4B, a universal multimodal embedding model built on Qwen3.5 that accepts text, images, videos, visual documents, and interleaved inputs to return 2,560-dimensional L2-normalized embeddings.
lab
Tencent Hunyuan (HF)
·
3d ago
·
17 views
Tencent releases WeMM-Embedding-9B universal multimodal embedding model
Tencent has released WeMM-Embedding-9B, a universal multimodal embedding model built on Qwen3.5 that accepts text, images, videos, visual documents, and interleaved inputs to return 4,096-dimensional L2-normalized embeddings.