WeMM-Embedding-2B-GGUF

WeMM-Embedding-2B

WeMM-Embedding-2B is a universal multimodal embedding model built on Qwen3.5. It accepts text, images, videos, visual documents, and interleaved multimodal inputs, and returns a 2,048-dimensional L2-normalized embedding. Audio input is not supported.

Derivation

本仓库为 tencent/WeMM-Embedding-2B 的 GGUF 格式量化版本。

• 两个量化文件的 GGUF 元数据已注入 `qwen35.pooling_type=3`（last-token pooling），Ollama 可直接识别为 embedding 模型使用。
• `mmproj-WeMM-Embedding-2B-bf16.gguf` 为独立导出的视觉塔（projector），用于多模态加载。

Installation

pip install torch transformers==5.2.0 "qwen-vl-utils[decord]==0.0.14" \
  "sentence-transformers>=5.7.0" "accelerate>=1.1.0"

Transformers

import torch
from qwen_vl_utils import process_vision_info
from transformers import AutoModel, AutoProcessor

model_id = "tencent/WeMM-Embedding-2B"
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = AutoModel.from_pretrained(
model_id, trust_remote_code=True, dtype=torch.bfloat16
).cuda().eval()

messages = [{"role": "user", "content": [
{"type": "image", "image": "/path/to/image.jpg"},
{"type": "video", "video": "/path/to/video.mp4"},
{"type": "text", "text": "This can be any text input."},
]}]
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=False
)
images, videos, video_kwargs = process_vision_info(
messages,
image_patch_size=16,
return_video_kwargs=True,
return_video_metadata=True,
)
if videos is not None:
videos, video_metadata = zip(*videos)
videos, video_metadata = list(videos), list(video_metadata)
else:
video_metadata = None
inputs = processor(
text=text,
images=images,
videos=videos,
video_met…

Source: https://huggingface.co/DreamBlooms/WeMM-Embedding-2B-GGUF