What are the hardware requirements for Gte Qwen2 7B Instruct?

Gte Qwen2 7B Instruct requires approximately 30.5 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gte Qwen2 7B Instruct and how large is it?

Gte Qwen2 7B Instruct is developed by Alibaba-NLP, a model with 7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Gte Qwen2 7B Instruct perform on standard benchmarks?

Gte Qwen2 7B Instruct has the following published scores: MMLU 59.7. Compare against reference models on this page or on the LLM Explorer leaderboards.

Gte Qwen2 7B Instruct by Alibaba-NLP — VRAM 30.5GB, 128K context

Name: Gte Qwen2 7B Instruct
Author: Alibaba-NLP

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: 0.25, Arc: 83.6, HellaSwag: 75, MMLU: 59.7, GSM8K: 54.1.

Arxiv:2308.03281 Custom code Deploy:sagemaker 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 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 Name	Gte Qwen2 7B Instruct
Repository 🤗	https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct
Model Size	7b
Required VRAM	30.5 GB
Updated	2026-09-04
Maintainer	Alibaba-NLP
Model Type	qwen2
Instruction-Based	Yes
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 Architecture	Qwen2ForCausalLM
License	apache-2.0
Context Length	131072
Model Max Length	131072
Transformers Version	4.41.2
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151646
Torch Data Type	float32
Errors	replace

Rank the Gte Qwen2 7B Instruct capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

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

Quantized Models of the Gte Qwen2 7B Instruct

Model	Likes	Downloads	VRAM
Gte Qwen2 7B Instruct 4bit DWQ	3	88	4 GB

Best Alternatives to Gte Qwen2 7B Instruct

Best Alternatives	Context / RAM	Downloads	Likes
Qwen2.5 7B Instruct 1M	986K / 15.4 GB	52534	373
Rewnozom	986K / 30.5 GB	742	1
Hush Qwen2.5 7B RP V1.4 1M	986K / 15.2 GB	10	2
Qwen2.5 7B RRP 1M	986K / 15.2 GB	5	6
Qwen2.5 7B CelestialHarmony 1M	986K / 14.8 GB	8	7
Q2.5 Instruct 1M Harmony	986K / 15.2 GB	4	2
Impish QWEN 7B 1M	986K / 15.2 GB	4	6
COCO 7B Instruct 1M	986K / 15.2 GB	9	4
Apollo 7B	986K / 15.2 GB	8	0
Graph R1 7B	986K / 15.3 GB	7	3

Note: green Score (e.g. "73.2") means that the model is better than Alibaba-NLP/gte-Qwen2-7B-instruct.