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Gte Qwen2 7B Instruct by alibaba-nlp

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Gte Qwen2 7B Instruct is an open-source language model by alibaba-nlp. Features: 7b LLM, VRAM: 30.5GB, Context: 128K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.3, Arc: 83.6, HellaSwag: 75, MMLU: 59.7, GSM8K: 54.1.

  Arxiv:2308.03281   Custom code   Endpoints compatible   Instruct   Model-index   Mteb   Qwen2   Region:us   Safetensors   Sentence-similarity   Sentence-transformers   Sharded   Tensorflow   Text-embeddings-inference
Model Card on HF 🤗: https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct 

Gte Qwen2 7B Instruct Benchmarks

MMLU Pro: 25.79
MUSR: 6.32
BBH: 21.93
IFEval: 22.55 vs 88 (so35)-74.4%
ARC: 83.6 vs 96.7 (so35)-13.5%
HellaSwag: 75 vs 95.3 (gpt4)-21.3%
MMLU: 59.7 vs 88.3 (so35)-32.4%
GSM8K: 54.1 vs 96.4 (so35)-43.9%
OpenCompas: 53.1 vs 73.3 (gpt4)-27.6%
Exam: 66 vs 77.2 (gpt4)-14.5%
Language: 41 vs 62 (gpt4)-33.9%
Knowledge: 48.1 vs 73.5 (gpt4)-34.6%
Understand: 64.6 vs 70 (gpt4)-7.7%
Reasoning: 50.3 vs 74.4 (gpt4)-32.4%
MATH Lvl 5: 6.42
LLME Score: 0.30376
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Gte Qwen2 7B Instruct (Alibaba-NLP/gte-Qwen2-7B-instruct)
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Gte Qwen2 7B Instruct Parameters and Internals

Model Type 
multilingual, text embedding
Additional Notes 
Model trained on the Qwen2 series models released by the Qwen team. Incorporates bidirectional attention mechanisms and instruction tuning applied solely on the query side. Comprehensive training across a vast, multilingual text corpus using both weakly supervised and supervised data.
Supported Languages 
EN (high proficiency), ZH (high proficiency)
LLM NameGte Qwen2 7B Instruct
Repository 🤗https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct 
Model Size7b
Required VRAM30.5 GB
Updated2026-04-19
Maintaineralibaba-nlp
Model Typeqwen2
Instruction-BasedYes
Model Files  5.0 GB: 1-of-7   4.8 GB: 2-of-7   4.9 GB: 3-of-7   4.9 GB: 4-of-7   5.0 GB: 5-of-7   3.7 GB: 6-of-7   2.2 GB: 7-of-7
Model ArchitectureQwen2ForCausalLM
Licenseapache-2.0
Context Length131072
Model Max Length131072
Transformers Version4.41.2
Tokenizer ClassQwen2Tokenizer
Padding Token<|endoftext|>
Vocabulary Size151646
Torch Data Typefloat32
Errorsreplace

Quantized Models of the Gte Qwen2 7B Instruct

Model
Likes
Downloads
VRAM
Gte Qwen2 7B Instruct 4bit DWQ3784 GB

Best Alternatives to Gte Qwen2 7B Instruct

Best Alternatives
Context / RAM
Downloads
Likes
Qwen2.5 7B Instruct 1M986K / 15.4 GB101838363
Hush Qwen2.5 7B RP V1.4 1M986K / 15.2 GB192
Qwen2.5 7B RRP 1M986K / 15.2 GB266
Qwen2.5 7B CelestialHarmony 1M986K / 14.8 GB107
Q2.5 Instruct 1M Harmony986K / 15.2 GB42
Impish QWEN 7B 1M986K / 15.2 GB76
COCO 7B Instruct 1M986K / 15.2 GB214
Apollo 7B986K / 15.2 GB60
Graph R1 7B986K / 15.3 GB53
Qwen2.5 7B Sky R1 Mini986K / 15.2 GB70
Note: green Score (e.g. "73.2") means that the model is better than Alibaba-NLP/gte-Qwen2-7B-instruct.

Rank the Gte Qwen2 7B Instruct Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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