GTE
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GTE (General Text Embedding) covers embedding and reranking for multilingual and long-document retrieval. gte-multilingual-base handles more than 70 languages at 8,192 tokens from a 305M-parameter encoder and emits sparse token weights alongside dense vectors; the ModernBERT-based embedder and cross-encoder reranker target English long documents, and the gte-Qwen2 instruct checkpoints are decoder-based variants. Alibaba's NLP group publishes the family.
Scored over the Alibaba-NLP account. The original v1 encoders published under the thenlper account add about 2.0M downloads a month and do not move the adoption band. The gme-Qwen2-VL checkpoints are a separate multimodal line. Verified 2026-09-11 via the Hugging Face model cards and the Hub API.
Openness
3 high confidence- weights
- open(Apache-2.0 ungated checkpoints across the multilingual, ModernBERT and Qwen2-based tiers)
- data
- closed(the cards point at the mGTE paper for the training mixture and release no corpus)
- code
- partial(a gte_embedding.py inference script ships in the model repository
- license
- Apache-2.0(OSI)
The category center of gravity: an OSI license over ungated weights with the corpus and the training run kept in the paper. The Qwen2-derived checkpoints carry Apache-2.0 rather than the Qwen license, so no restrictive SKU pulls the family down.
- https://huggingface.co/api/models/Alibaba-NLP/gte-multilingual-base recorded 2026-09-11
license: apache-2.0; gated: false; private: false; downloadable checkpoint files present.
- https://huggingface.co/api/models/Alibaba-NLP/gte-modernbert-base recorded 2026-09-11
license: apache-2.0; gated: false; private: false.
- https://huggingface.co/api/models/Alibaba-NLP/gte-Qwen2-7B-instruct recorded 2026-09-11
license: apache-2.0; gated: false; private: false - the Qwen2-derived tier is distributed under Apache-2.0 rather than the Qwen license.
- https://huggingface.co/Alibaba-NLP/gte-multilingual-base/raw/main/README.md recorded 2026-09-11
Describes the training architecture and points to the mGTE paper for detail, releases no corpus, and ships scripts/gte_embedding.py as inference code for dense and sparse output.
- https://huggingface.co/Alibaba-NLP/gte-modernbert-base/raw/main/README.md recorded 2026-09-11
The embedding checkpoint's own card - BEIR average 55.33, LoCo long-context and CoIR code-retrieval results at 8,192 tokens, and the statement that the series follows the previous GTE training scheme with ModernBERT as the base, referring readers to the mGTE paper for training details.
Adoption
4 high confidence3,743,441 downloads in the trailing 30 days across the gte EMBEDDING checkpoints in the Alibaba-NLP account, led by gte-multilingual-base at 1,307,627. The two reranker checkpoints are deliberately excluded: they draw a further 3,221,049 and are scored separately as `gte-reranker`, so the earlier reading of this record - all 11 gte-prefixed repos at 6,964,490 - billed the same downloads to two products. The band is 4 either way. The superseded v1 encoders under the `thenlper` account add roughly 1.99M more and are also excluded, being a different publishing account.
- https://huggingface.co/api/models/Alibaba-NLP/gte-modernbert-base recorded 2026-09-11
downloads: 171930; likes: 200; license apache-2.0 - one of the embedding checkpoints the summed figure counts.
- https://huggingface.co/api/models/Alibaba-NLP/gte-multilingual-base recorded 2026-09-11
downloads: 1307627.
- https://huggingface.co/api/models/Alibaba-NLP/gte-Qwen2-7B-instruct recorded 2026-09-11
downloads: 111229.
Capability
4 high confidenceCurrent generation on both halves of the pipeline: the highest BEIR average among the sub-1B models in the table it publishes, long-context throughout, and a multilingual encoder beside the English one. Short of the leader, which is measured on MMTEB rather than BEIR. The reranker half of the GTE line is scored separately as `gte-reranker`; this record cites only the embedding checkpoints, after an earlier reading derived both its score and its downloads from the reranker card.
- https://huggingface.co/Alibaba-NLP/gte-modernbert-base/raw/main/README.md recorded 2026-09-11
The embedding checkpoint's own card gives gte-modernbert-base MTEB-en 64.38 and BEIR 55.33 at 8,192 maximum sequence length, with LoCo long-context and CoIR code-retrieval results on the same table.
- https://huggingface.co/Alibaba-NLP/gte-multilingual-base/raw/main/README.md recorded 2026-09-11
States support for over 70 languages, 8192-token input, elastic dense output and sparse vectors, with retrieval validated on MIRACL, MLDR, MKQA, BEIR and LoCo.
Verified 2026-09-11