gte-multilingual

About

What gte-multilingual is

gte-multilingual-base The gte-multilingual-base model is the latest in the GTE (General Text Embedding) family of models, featuring several key attributes: - High Performance: Achieves state-of-the-art (SOTA) results in multilingual retrieval tasks and multi-task representation model evaluations when compared to models of similar size. - Training Architecture: Trained using an encoder-only transformers architecture, resulting in a smaller model size. Unlike previous models based on decode-only LLM architecture (e.g., gte-qwen2-1.5b-instruct), this model has lower hardware requirements for inference, offering a 10x increase in inference speed. - Long Context: Supports text lengths up to 8192 tokens. - Multilingual Capability: Supports over 70 languages. - Elastic Dense Embedding: Support elastic output dense representation while maintaining the effectiveness of downstream tasks, which significantly reduces storage costs and improves execution efficiency. - Sparse Vectors: In addition to dense representations, it can also generate sparse vectors. Paper: mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval Model Information - Model Size: 305M - Embedding Dimension: 768 - Max Input Tokens: 8192 Usage - It is recommended to install xformers and enable unpadding for acceleration, refer to enable-unpadding-and-xformers. - How to use it offline: new-impl/discussions/2 - How to use with TEI: refs/pr/7 Get Dense Embeddings with Transformers Use with sentence-transformers Use with infinity Usage via docker and infinity, MIT Licensed. Use with Text Embeddings Inference (TEI) Usage via Docker and Text Embeddings Inference (TEI): - CPU: - GPU: Then you can send requests to the deployed API via the OpenAI-compatible v1/embeddings route (more information about the OpenAI Embeddings API): Use with custom code to get dense embeddings and sparse token weights Evaluation We validated the performance of the gte-multilingual-base model on multiple downstream tasks, including multilingual retrieval, cross-lingual retrieval, long text retrieval, and general text representation evaluation on the MTEB Leaderboard, among others. Retrieval

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How embedding models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Benchmarks

Reported results

As published on the model card — the maker's own numbers, not measured by AxForge.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys gte-multilingual for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (gte-multilingual below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"gte-multilingual","input":"text to embed"}'

Details

Languages, data & research

Papers

Licence

Open weights

Sources

Weights & code

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