Feature Extraction
Transformers
Safetensors
qwen3
text-generation
embeddings
nvfp4
blackwell
compressed-tensors
text-embeddings-inference
8-bit precision
Instructions to use andrebadini/Qwen3-Embedding-4B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use andrebadini/Qwen3-Embedding-4B-NVFP4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="andrebadini/Qwen3-Embedding-4B-NVFP4")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("andrebadini/Qwen3-Embedding-4B-NVFP4") model = AutoModelForCausalLM.from_pretrained("andrebadini/Qwen3-Embedding-4B-NVFP4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 13272096ad71a3c294663dd385aeb68919e2e1cb199d8b1c5d3fb2e69cc8be1d
- Size of remote file:
- 11.4 MB
- SHA256:
- 303358b4afefdc9f716e4b1739189fe259d9da6c68df83e962af7fc40c32bb66
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