What are the hardware requirements for WeVisOne VL 2B?

WeVisOne VL 2B requires approximately 4.9 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed WeVisOne VL 2B and how large is it?

WeVisOne VL 2B is developed by tencent, a model with 2b 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 WeVisOne VL 2B?

WeVisOne VL 2B is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

WeVisOne VL 2B by tencent — VRAM 4.9GB

Name: WeVisOne VL 2B
Author: tencent

WeVisOne VL 2B is an open-source language model by tencent. Features: 2b LLM, VRAM: 4.9GB, License: apache-2.0, LLM Explorer Score: 0.43.

Conversational Endpoints compatible Image-matting Image-text-to-text Object-counting Object-detection Ocr Point-grounding Qwen3 vl Referring-expression-comprehen... Referring-expression-segmentat... Region:us Safetensors Vision Vision-language

Model Card on HF 🤗: https://huggingface.co/tencent/WeVisOne-VL-2B

WeVisOne VL 2B Parameters and Internals

LLM Name	WeVisOne VL 2B
Repository 🤗	https://huggingface.co/tencent/WeVisOne-VL-2B
Model Size	2b
Required VRAM	4.9 GB
Updated	2026-10-26
Maintainer	tencent
Model Type	qwen3_vl
Model Files	0.0 GB 4.9 GB
Model Architecture	Qwen3VLForConditionalGeneration
License	apache-2.0
Model Max Length	8192
Transformers Version	5.5.3
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Errors	replace

Rank the WeVisOne VL 2B 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

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