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
Ctrl+K
- afmoe
- aimv2
- albert
- align
- altclip
- apertus
- arcee
- aria
- audio_spectrogram_transformer
- audioflamingo3
- auto
- autoformer
- aya_vision
- bamba
- bark
- bart
- barthez
- bartpho
- beit
- bert
- bert_generation
- bert_japanese
- bertweet
- big_bird
- bigbird_pegasus
- biogpt
- bit
- bitnet
- blenderbot
- blenderbot_small
- blip
- blip_2
- bloom
- blt
- bridgetower
- bros
- byt5
- camembert
- canine
- chameleon
- chinese_clip
- chmv2
- clap
- clip
- clipseg
- clvp
- code_llama
- codegen
- cohere
- cohere2