---
title: Alibaba-NLP/gte-Qwen2-1.5B-instruct
description: 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. Qwen2, 1.8B parameters.
canonical_url: https://superlinked.com/models/alibaba-nlp-gte-qwen2-1-5b-instruct
last_updated: 2026-05-24
---

# Alibaba-NLP/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.

Source: [Alibaba-NLP/gte-Qwen2-1.5B-instruct on HuggingFace](https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct)

## Overview

| Field | Value |
|-------|-------|
| Architecture | Qwen2 |
| Parameters | 1.8B |
| Tasks | Encode |
| Outputs | Dense |
| Dimensions | Dense: 1,536 |
| Max sequence length | 32,768 tokens |
| License | apache-2.0 |
| Inputs | text |

## Benchmarks

### CQADupstackPhysicsRetrieval

Domain: scientific · Task: retrieval · Language: en

Duplicate question retrieval from StackExchange Physics

Corpus: 38,314 · Queries: 1,039

**Performance (L4 b1 c16):** Corpus 11.6K tok/s · Corpus p50 178.5ms · Query 2.2K tok/s · Query p50 69.6ms

[Reference](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)

### CosQA

Domain: technology · Task: retrieval · Language: en

Code search with natural language queries

Corpus: 6,267 · Queries: 500

**Performance (L4 b1 c16):** Corpus 9.3K tok/s · Corpus p50 96.2ms · Query 1.2K tok/s · Query p50 66.8ms

[Reference](https://arxiv.org/abs/2105.13239)

### FiQA2018

Domain: finance · Task: retrieval · Language: en

Financial opinion mining and question answering

Corpus: 57,599 · Queries: 648

**Performance (L4 b1 c16):** Corpus 11.8K tok/s · Corpus p50 222.9ms · Query 2.1K tok/s · Query p50 73.4ms

[Reference](https://sites.google.com/view/fiqa/)

### LegalBenchConsumerContractsQA

Domain: legal · Task: retrieval · Language: en

Question answering on consumer contracts

Corpus: 153 · Queries: 396

**Performance (L4 b1 c16):** Corpus 12.3K tok/s · Corpus p50 735.3ms · Query 3.1K tok/s · Query p50 71.9ms

[Reference](https://huggingface.co/datasets/nguha/legalbench)

### NFCorpus

Domain: medical · Task: retrieval · Language: en

Biomedical literature search from NutritionFacts.org

Corpus: 3,593 · Queries: 323

**Quality:** ndcg at 10: 0.3925 · map at 10: 0.1502 · mrr at 10: 0.6051

**Performance (L4 b1 c16):** Corpus 12.7K tok/s · Corpus p50 384.4ms · Query 821 tok/s · Query p50 90.2ms

[Reference](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)

### NanoFiQA2018Retrieval

Domain: finance · Task: retrieval · Language: en

Smaller subset of the FiQA financial QA dataset

**Quality:** ndcg at 10: 0.6524 · map at 10: 0.5848 · mrr at 10: 0.7032

**Performance (L4 b1 c16):** Corpus 11.3K tok/s · Corpus p50 251.5ms · Query 1.9K tok/s · Query p50 88.7ms

[Reference](https://sites.google.com/view/fiqa/)

### SCIDOCS

Domain: scientific · Task: retrieval · Language: en

Citation prediction, document classification, and recommendation for scientific papers

Corpus: 25,656 · Queries: 1,000

**Performance (L4 b1 c16):** Corpus 12.4K tok/s · Corpus p50 261.1ms · Query 2.5K tok/s · Query p50 66.4ms

[Reference](https://allenai.org/data/scidocs)

### SciFact

Domain: scientific · Task: retrieval · Language: en

Scientific claim verification using research literature

Corpus: 5,183 · Queries: 300

**Performance (L4 b1 c16):** Corpus 12.6K tok/s · Corpus p50 370.4ms · Query 3.1K tok/s · Query p50 74.9ms

[Reference](https://github.com/allenai/scifact)

### StackOverflowQA

Domain: technology · Task: retrieval · Language: en

Programming question answering from Stack Overflow

Corpus: 19,931 · Queries: 1,994

**Performance (L4 b1 c16):** Corpus 12.4K tok/s · Corpus p50 299.2ms · Query 11.4K tok/s · Query p50 421.4ms

[Reference](https://arxiv.org/abs/2407.02883)