Feature Extraction
Transformers
Safetensors
qwen3
fp8
compressed-tensors
llm-compressor
vllm
embedding
text-embeddings-inference
Instructions to use DCC-BS/Qwen3-Embedding-4B-FP8-Dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DCC-BS/Qwen3-Embedding-4B-FP8-Dynamic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DCC-BS/Qwen3-Embedding-4B-FP8-Dynamic")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DCC-BS/Qwen3-Embedding-4B-FP8-Dynamic") model = AutoModel.from_pretrained("DCC-BS/Qwen3-Embedding-4B-FP8-Dynamic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1cc0ea2ab1bbf3cbace4de2db48b896d87952770454248e054846db5aef23597
- Size of remote file:
- 11.4 MB
- SHA256:
- 31f82e8f8c173bff1b34626d065393d33938f6d27a7448cfb706bbf9d3c0e651
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.