From the model card
Fields below are copied from the tags and counters on the HuggingFace repository Alibaba-NLP/gte-Qwen2-1.5B-instruct at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.
- Publisher (HF namespace)
- Alibaba-NLP
- Pipeline tag
- sentence-similarity
- Library
- Sentence Transformers, Transformers
- Weight formats
- safetensors
- License tag
apache-2.0— read the license file in the repo before relying on it- Papers cited
- arXiv:2308.03281
- Downloads (HF counter at last fetch)
- 803,786
- Likes (HF counter at last fetch)
- 237
- Model card
- https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct
Use cases
- High-quality sentence and document embedding for semantic search
- Instruction-following retrieval tasks where query formatting matters
- RAG pipelines needing strong embedding quality within a 1.5B compute budget
- Multilingual embedding including Chinese-English cross-lingual retrieval
Pros
- Qwen2 decoder backbone gives substantially better MTEB scores than 110M BERT-class models
- 1.5B is large for an embedder but small for a Qwen LLM — good tradeoff
- Instruction-aware retrieval allows task-specific query prefixes
- Strong multilingual capability from Qwen2 pretraining
Cons
- 1.5B decoder embedding is slower and more memory-intensive than BERT-class embedders
- Max sequence length depends on Qwen2 tokenizer, not optimized for very short texts
- Instruction prefix format must be applied correctly or quality degrades
- Apache 2.0 but some downstream MTEB tasks have their own data licenses
Tags
sentence-transformerssafetensorsqwen2text-generationmtebtransformersQwen2sentence-similaritycustom_codearxiv:2308.03281license:apache-2.0model-indextext-embeddings-inferenceendpoints_compatibleregion:us