Tianmu-Emb-Uni-8B

Tianmu-Emb-Uni-8B is a unified multimodal embedding model for general-purpose retrieval and representation learning.

The name means Tianmu Unified Multimodal Embedding 8B.

"Tianmu" is the model family name, "Emb" denotes embedding-based representation learning, "Uni" indicates unified cross-modal encoding, and "8B" refers to the model scale.

Model Details

• Model name: Tianmu-Emb-Uni-8B
• Repository: https://huggingface.co/TianmuLab/Tianmu-Emb-Uni/tree/main
• Model type: unified multimodal embedding model
• Embedding dimension: 3584
• Base vision-language embedding model: Qwen3-VL-Embedding-8B
• Base audio model: Qwen2.5-Omni-7B audio tower
• Released checkpoint stage: stage1b_adapter_proto_retrieval
• Released weights: trained audio-side modules, connector, projection, adapter, and prototype modules
• Full base model weights included: no
• Native AutoModel.from_pretrained support: no

Model Architecture

Tianmu-Emb-Uni-8B is built on Qwen3-VL embedding backbone and an audio branch initialized from Qwen2.5-Omni. The audio branch is connected to the Qwen3-VL embedding space through trainable connector, projection, adapter, and prototype modules.

Intended Use

Tianmu-Emb-Uni-8B is intended for research and evaluation of unified multimodal embedding models, especially retrieval and representation tasks involving multiple modalities.

Typical use cases include:

• text-to-image retrieval
• image-to-text retrieval
• text-to-video retrieval
• audio-to-text retrieval
• audio classification and audio retrieval
• visual document retrieval
• multimodal RAG retrieval backbones
• cross-modal candidate recall and semantic matching

Evaluation

Tianmu-Emb-Uni-8B was evaluated on MMEB-V3, following the 190-task setting described in the MMEB-V3 paper. The image task group includes MCMR, resulting in 37 image tasks and 190 total tasks.

Modality	Tasks	Primary Metric	Score
Image	37	hit@1	73.83
Video	18	hit@1	59.37
Visual Document	24	ndcg_linear@5	72.03
Audio	11	hit@1	38.94
Text	53	ndcg_linear@5	43.62
Agent	47	hit@1	39.42
All	190	mixed primary metrics	53.27

The code used to evaluate the stage1b_adapter_proto_retrieval checkpoint is shown below:

bash code/scripts/run_omni_stage1b_adapter_proto_retrieval.sh

Files

model.safetensors
model.safetensors.index.json
config.json
weights/final_audio_weights.pt
tianmu_model/
processors/
eval/mmeb_v3_eval/
requirements.txt
examples/load_weights.py

model.safetensors is converted from weights/final_audio_weights.pt and contains the same final state dict in Hugging Face's safer tensor format. The .pt checkpoint is kept for compatibility with the original training and evaluation scripts.

Loading

This package uses lightweight project-specific model code instead of a native transformers.AutoModel.from_pretrained implementation. Users should prepare the base Qwen3-VL-Embedding-8B and Qwen2.5-Omni-7B models separately, then load the Tianmu adapter/audio-side weights.

A minimal loading example is provided at:

examples/load_weights.py

Example:

from pathlib import Path
import sys

from safetensors.torch import load_file

repo_dir = Path("/path/to/Tianmu-Emb-Uni")
sys.path.insert(0, str(repo_dir))

from tianmu_model.modeling import OmniEmbedModel

model = OmniEmbedModel(
    audio_encoder_type="omni",
    audio_model_path="/path/to/Qwen2.5-Omni-7B",
    vl_model_name="/path/to/Qwen3-VL-Embedding-8B",
    freeze_vl=True,
    freeze_audio_encoder=True,
)

state_dict = load_file(str(repo_dir / "model.safetensors"), device="cpu")
missing, unexpected = model.load_state_dict(state_dict, strict=False)
model.eval()

Limitations

• This release does not contain the full Qwen3-VL-Embedding-8B or Qwen2.5-Omni-7B base weights.
• This release does not provide a fully integrated AutoModel.from_pretrained interface.
• The model is primarily released for research and evaluation of multimodal embeddings.
• Performance may vary across domains outside the MMEB-V3 evaluation distribution.
• Open-ended audio retrieval remains more challenging than audio classification in the current checkpoint.

Third-Party Components

This project builds on external open-source model ecosystems, including Qwen3-VL-Embedding and Qwen2.5-Omni, together with common libraries such as PyTorch, Transformers, safetensors, NumPy, librosa, soundfile, and Pillow.

Users should comply with the licenses and usage terms of the corresponding base models and dependencies.

License

The released Tianmu adapter/audio-side code and weights in this repository are provided under the Apache-2.0 license unless otherwise specified. Third-party base models and dependencies are governed by their respective licenses.