Feature Extraction
Transformers
Safetensors
qwen3_5
image-text-to-text
multimodal-embedding
retrieval
evaluation
custom_code
Instructions to use woooooz/WeMM-Embedding-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use woooooz/WeMM-Embedding-9B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="woooooz/WeMM-Embedding-9B", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("woooooz/WeMM-Embedding-9B", trust_remote_code=True) model = AutoModelForMultimodalLM.from_pretrained("woooooz/WeMM-Embedding-9B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 342 Bytes
bb2b5cc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"source_syntax": "passed",
"json_yaml": "passed",
"safetensors_header": "passed",
"weight_tensors": 760,
"task_counts": {
"image_retrieval": 12,
"video_ret_5": 5,
"moment_retrieval": 3,
"visdoc_full": 24
},
"launcher_shell_syntax": "passed",
"evaluator_cli_import": "passed",
"gpu_evaluation": "not run"
}
|