Sentence Similarity
sentence-transformers
Safetensors
PEFT
English
text-embeddings
retrieval
web-search
news
Instructions to use desearch/Desearch-Embedding-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use desearch/Desearch-Embedding-8B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("desearch/Desearch-Embedding-8B") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - PEFT
How to use desearch/Desearch-Embedding-8B with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Desearch Embedding 8B: LoRA adapter on Qwen3-Embedding-8B, model card with /search A/B results
5bc4751 verified Download tokenizer.json from desearch/Desearch-Embedding-8B: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/desearch/Desearch-Embedding-8B/resolve/main/tokenizer.json
- Command line
-
hf download hf://desearch/Desearch-Embedding-8B/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/desearch/Desearch-Embedding-8B/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 50a9379871df8a6f4a3a62ff878c10b986d6f11642e735c21e5b9285f3284c2e
- Size of remote file:
- 11.4 MB
- SHA256:
- ff800fee44eacd1a1c8c2f96b890ade65d722550ab6693efa8d2c368f40a5853
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.