We are excited to present `GME-Qwen2VL` series of unified **multimodal embedding models**, which are based on the advanced Qwen2-VL multimodal large language models (MLLMs). The `GME` models support three types of input: **text**, **image**, and **image-text pair**, all of which can produce universal vector representations and have powerful retrieval performance. The remote code has some issues with `transformers>=4.52.0`, please downgrade or use `sentence_transformers`

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gme-Qwen2-VL-7B-Instruct

Alibaba-NLP

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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gte-base-en-v1.5

Alibaba-NLP

We introduce `gte-v1.5` series, upgraded `gte` embeddings that support the context length of up to **8192**, while further enhancing model performance. The models are built upon the `transformer++` encoder backbone (BERT + RoPE + GLU). The `gte-v1.5` series achieve state-of-the-art scores on the MTEB benchmark within the same model size category and prodvide competitive on the LoCo long-context retrieval tests (refer to Evaluation). We also present the `gte-Qwen1.5-7B-instruct`, a SOTA instruction-tuned multi-lingual embedding model that ranked 2nd in MTEB and 1st in C-MTEB.

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gte-en-mlm-base

Alibaba-NLP

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gte-en-mlm-large

Alibaba-NLP

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gte-large-en-v1.5

Alibaba-NLP

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gte-modernbert-base

Alibaba-NLP

We are excited to introduce the `gte-modernbert` series of models, which are built upon the latest modernBERT pre-trained encoder-only foundation models. The `gte-modernbert` series models include both text embedding models and rerank models. The `gte-modernbert` models demonstrates competitive performance in several text embedding and text retrieval evaluation tasks when compared to similar-scale models from the current open-source community. This includes assessments such as MTEB, LoCO, and COIR evaluation. - Developed by: Tongyi Lab, Alibaba Group - Model Type: Text Embedding - Primary Language: English - Model Size: 149M - Max Input Length: 8192 tokens - Output Dimension: 768

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gte-multilingual-base

Alibaba-NLP

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

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gte-multilingual-mlm-base

Alibaba-NLP

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gte-multilingual-reranker-base

Alibaba-NLP

The **gte-multilingual-reranker-base** model is the first reranker model in the GTE 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 reranker 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. - **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** Usage with Text Embeddings Inference (TEI):

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gte-Qwen1.5-7B-instruct

Alibaba-NLP

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gte-Qwen2-1.5B-instruct

Alibaba-NLP

**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. - 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. Observe the config_sentence_transformers.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.

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gte-Qwen2-7B-instruct

Alibaba-NLP

**gte-Qwen2-7B-instruct** is the latest model in the gte (General Text Embedding) model family that ranks **No.1** in both English and Chinese evaluations on the Massive Text Embedding Benchmark MTEB benchmark (as of June 16, 2024). Recently, the **Qwen team** released the Qwen2 series models, and we have trained the **gte-Qwen2-7B-instruct** model based on the Qwen2-7B LLM model. Compared to the gte-Qwen1.5-7B-instruct model, the **gte-Qwen2-7B-instruct** model uses the same training data and training strategies during the finetuning stage, with the only difference being the upgraded base model to Qwen2-7B. Considering the improvements in the Qwen2 series models compared to the Qwen1.5 series, we can also expect consistent performance enhancements in the embedding models. - 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.

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Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

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