What are the hardware requirements for ViLaSR?

ViLaSR requires approximately 16.7 GB of VRAM and supports a context window of 125K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed ViLaSR and how large is it?

ViLaSR is developed by inclusionAI, 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 ViLaSR?

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

ViLaSR by inclusionAI — VRAM 16.7GB, 125K context

Name: ViLaSR
Author: inclusionAI

ViLaSR is an open-source language model by inclusionAI. Features: 7b LLM, VRAM: 16.7GB, Context: 125K, Instruction-Based, LLM Explorer Score: 0.19.

Arxiv:2506.09965 Base model:finetune:qwen/qwen2... Base model:qwen/qwen2.5-vl-7b-... Conversational Dataset:antresearchnlp/vilasr-... En Image-text-to-text Instruct Qwen2 5 vl Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/inclusionAI/ViLaSR

ViLaSR Parameters and Internals

LLM Name	ViLaSR
Repository 🤗	https://huggingface.co/inclusionAI/ViLaSR
Base Model(s)	Qwen/Qwen2.5-VL-7B-Instruct Qwen/Qwen2.5-VL-7B-Instruct
Model Size	7b
Required VRAM	16.7 GB
Updated	2026-08-08
Maintainer	inclusionAI
Model Type	qwen2_5_vl
Instruction-Based	Yes
Model Files	4.3 GB: 1-of-4 5.0 GB: 2-of-4 5.0 GB: 3-of-4 2.4 GB: 4-of-4
Supported Languages	en
Model Architecture	Qwen2_5_VLForConditionalGeneration
Context Length	128000
Model Max Length	128000
Transformers Version	4.51.1
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	152064
Torch Data Type	float32
Errors	replace

Rank the ViLaSR 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 ViLaSR

Best Alternatives	Context / RAM	Downloads	Likes
OmniLong Qwen2.5 VL 7B	512K / 16.6 GB	7	2
Qwen2.5 VL 7B Instruct FP4	125K / 7.2 GB	49165	3
Qwen2.5 VL 7B Instruct NVFP4	125K / 7.2 GB	8092	16
ZwZ 7B	125K / 16.6 GB	32	10
Qwen2.5 VL 7B Instruct FP8	125K / 10 GB	1009	8
OlmOCR 2 7B 1025 Bf16	125K / 16.6 GB	154	2
ScaleCUA 7B	125K / 16.6 GB	115	11
HuatuoGPT Vision 7B Qwen2.5VL	125K / 16.6 GB	358	11
Holo1 7B Bf16	125K / 16.6 GB	24	2
Qwen2.5 VL 7B Instruct Bf16	125K / 16.6 GB	160	3

Note: green Score (e.g. "73.2") means that the model is better than inclusionAI/ViLaSR.