gte-Qwen2

About

What gte-Qwen2 is

gte-Qwen2-1.5B-instruct gte-Qwen2-1.5B-instruct is the latest model in the gte (General Text Embedding) model family. The model is built on Qwen2-1.5B LLM model and use the same training data and strategies as the gte-Qwen2-7B-instruct model. The model incorporates several key advancements: - Integration of bidirectional attention mechanisms, enriching its contextual understanding. - Instruction tuning, applied solely on the query side for streamlined efficiency - Comprehensive training across a vast, multilingual text corpus spanning diverse domains and scenarios. This training leverages both weakly supervised and supervised data, ensuring the model's applicability across numerous languages and a wide array of downstream tasks. Model Information - Model Size: 1.5B - Embedding Dimension: 1536 - Max Input Tokens: 32k Requirements Usage Sentence Transformers Observe the configsentencetransformers.json to see all pre-built prompt names. Otherwise, you can use model.encode(queries, prompt="Instruct: ...\nQuery: " to use a custom prompt of your choice. Transformers infinityemb Usage via infinity, MIT Licensed. Evaluation MTEB & C-MTEB You can use the scripts/evalmteb.py to reproduce the following result of gte-Qwen2-1.5B-instruct on MTEB(English)/C-MTEB(Chinese): GTE Models The gte series models have consistently released two types of models: encoder-only models (based on the BERT architecture) and decode-only models (based on the LLM architecture). Cloud API Services In addition to the open-source GTE series models, GTE series models are also available as commercial API services on Alibaba Cloud. - Embedding Models: Three versions of the text embedding models are available: text-embedding-v1/v2/v3, with v3 being the latest API service. - ReRank Models: The gte-rerank model service is available. Note that the models behind the commercial APIs are not entirely identical to the open-source models. Community support Fine-tuning GTE models can be fine-tuned with a third party framework SWIFT. Citation If you find our paper or models helpful, please consider cite:

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-qwen2 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (gte-qwen2 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-qwen2","input":"text to embed"}'

Details

Languages, data & research

Papers

Licence

Open weights

Sources

Weights & code

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