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
File size: 643 Bytes
70cb703 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | {
"task": "WikipediaRetrievalMultilingual",
"metric": "ndcg_at_10",
"languages": [
"de",
"en",
"it"
],
"tolerance": 0.01,
"baseline": {
"model": "Qwen/Qwen3-Embedding-4B",
"task": "WikipediaRetrievalMultilingual",
"metric": "ndcg_at_10",
"scores": {
"de": 0.90665,
"en": 0.93437,
"it": 0.89927
}
},
"candidate": {
"model": "models/Qwen3-Embedding-4B-FP8-Dynamic",
"task": "WikipediaRetrievalMultilingual",
"metric": "ndcg_at_10",
"scores": {
"de": 0.90653,
"en": 0.93531,
"it": 0.89944
}
},
"created_at": "2026-08-05T14:38:16+00:00"
}
|