# Efficient Retrieval

A text embedding model based on the ModernBERT pre-trained encoder, supporting long text processing up to 8192 tokens, with excellent performance on evaluation tasks such as MTEB, LoCO, and COIR.

The GGUF quantized version of Snowflake Arctic Embed L v2.0 is an efficient multilingual text embedding model, suitable for high-quality retrieval tasks.

IBM Granite multilingual embedding model, supporting text embedding tasks in 12 languages, suitable for information retrieval and multilingual application scenarios.

This is a GGUF format model converted from Chuxin-Embedding, primarily used for Chinese text retrieval tasks.

GTE-Base is a general-purpose text embedding model capable of converting text into high-dimensional vector representations, suitable for text classification and similarity search tasks.