Feature Extraction
sentence-transformers
Safetensors
English
Chinese
qwen2
Retrieval
STS
Classification
Clustering
Reranking
vllm
custom_code
text-embeddings-inference
Instructions to use KaLM-Embedding/KaLM-embedding-multilingual-mini-instruct-v2.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use KaLM-Embedding/KaLM-embedding-multilingual-mini-instruct-v2.5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("KaLM-Embedding/KaLM-embedding-multilingual-mini-instruct-v2.5", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Download imgs/mteb_cmn.jpg from KaLM-Embedding/KaLM-embedding-multilingual-mini-instruct-v2.5: direct link, hf CLI and curl.
- Browser
- Download file 441 kB
-
https://huggingface.co/KaLM-Embedding/KaLM-embedding-multilingual-mini-instruct-v2.5/resolve/main/imgs/mteb_cmn.jpg
- Command line
-
hf download hf://KaLM-Embedding/KaLM-embedding-multilingual-mini-instruct-v2.5/imgs/mteb_cmn.jpg
-
curl -L -o mteb_cmn.jpg https://huggingface.co/KaLM-Embedding/KaLM-embedding-multilingual-mini-instruct-v2.5/resolve/main/imgs/mteb_cmn.jpg
441 kB

- Xet hash:
- a2fd63332a2525a0d942fa503a729450d538f7d54809fa2d8c0a33f653169602
- Size of remote file:
- 441 kB
- SHA256:
- dc76956e630d524929b54cfb92da6b8b5c7833a957619a1616e95bcbe8df7bde
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