Instructions to use Alibaba-NLP/gte-multilingual-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Alibaba-NLP/gte-multilingual-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Alibaba-NLP/gte-multilingual-base", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use Alibaba-NLP/gte-multilingual-base with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Alibaba-NLP/gte-multilingual-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
feat(transformers): Add native Transformers config
What does this PR do?
โ Sets model_type from new to gte and adds rope_parameters, so the checkpoint loads directly with AutoModel / AutoConfig in Transformers ๐ค without trust_remote_code.
โ Transformers support is being added in https://github.com/huggingface/transformers/pull/48416
โ โ ๏ธ Please hold this until the Transformers PR above has landed ๐ค
Compatibility
โ rope_scaling and rope_theta are replaced by rope_parameters. rope_scaling cannot be kept alongside it, because Transformers reads that key first and ntk is not one of its rope types.
โ auto_map is left in place, but on this revision a trust_remote_code=True load no longer finds rope_theta / rope_scaling and falls back to the remote defaults (rope_theta=10000.0, rope_scaling=None) instead of this checkpoint's values. That changes the embeddings, and the gap grows with sequence length, so anyone staying on an older Transformers version should pin a revision from before this PR.
Produced by?
โ rope_parameters is the standardised form of the existing rope_scaling / rope_theta pair. GTE applies NTK scaling statically at construction by rescaling the base and dividing the inverse frequencies by factor ** (2 / head_dim), which is exactly a linear scaling with base rope_theta * factor, so the inverse frequencies are unchanged.
โ Outputs are reproduced against the original implementation on this checkpoint (max relative difference 1.4e-06), and the slow integration tests in the Transformers PR run against this revision.