What are the hardware requirements for Qwen2.5 VL 7B Instruct?

Qwen2.5 VL 7B Instruct requires approximately 16.6 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen2.5 VL 7B Instruct and how large is it?

Qwen2.5 VL 7B Instruct is developed by unsloth, a model with 7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Qwen2.5 VL 7B Instruct?

Qwen2.5 VL 7B Instruct is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen2.5 VL 7B Instruct by unsloth — VRAM 16.6GB, 32K context

Name: Qwen2.5 VL 7B Instruct
Author: unsloth

Qwen2.5 VL 7B Instruct is an open-source language model by unsloth. Features: 7b LLM, VRAM: 16.6GB, Context: 32K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.21.

Arxiv:2308.12966 Arxiv:2309.00071 Arxiv:2409.12191 Base model:finetune:qwen/qwen2... Base model:qwen/qwen2.5-vl-7b-... Conversational Deploy:azure En Endpoints compatible Image-text-to-text Instruct Multimodal Qwen2 5 vl Region:us Safetensors Sharded Tensorflow Unsloth

Model Card on HF 🤗: https://huggingface.co/unsloth/Qwen2.5-VL-7B-Instruct

Qwen2.5 VL 7B Instruct Parameters and Internals

LLM Name	Qwen2.5 VL 7B Instruct
Repository 🤗	https://huggingface.co/unsloth/Qwen2.5-VL-7B-Instruct
Base Model(s)	Qwen/Qwen2.5-VL-7B-Instruct Qwen/Qwen2.5-VL-7B-Instruct
Model Size	7b
Required VRAM	16.6 GB
Updated	2026-08-10
Maintainer	unsloth
Model Type	qwen2_5_vl
Instruction-Based	Yes
Model Files	5.0 GB: 1-of-4 5.0 GB: 2-of-4 4.9 GB: 3-of-4 1.7 GB: 4-of-4
Supported Languages	en
Model Architecture	Qwen2_5_VLForConditionalGeneration
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.56.1
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|vision_pad\|>
Vocabulary Size	152064
Torch Data Type	bfloat16
Errors	replace

Rank the Qwen2.5 VL 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

Best Alternatives to Qwen2.5 VL 7B Instruct

Best Alternatives	Context / RAM	Downloads	Likes
OmniLong Qwen2.5 VL 7B	512K / 16.6 GB	7	2
Qwen2.5 VL 7B Instruct	125K / 16.7 GB	8740905	1666
OlmOCR 2 7B 1025 FP8	125K / 10 GB	540426	253
OlmOCR 2 7B 1025	125K / 16.6 GB	244728	157
RolmOCR	125K / 16.6 GB	224280	589
PosterCopilot	125K / 16.7 GB	29	1
Qwen2.5 VL 7B Instruct FP4	125K / 7.2 GB	49165	3
Cosmos Reason1 7B	125K / 16.6 GB	62913	244
OlmOCR 7B 0825 FP8	125K / 10 GB	38011	10
X Reasoner 7B	125K / 16.5 GB	1030	10

Note: green Score (e.g. "73.2") means that the model is better than unsloth/Qwen2.5-VL-7B-Instruct.