HuggingFaceTB / image-text-to-text updated 1 year ago

SmolVLM2-500M-Video-Instruct

Name: SmolVLM2-500M-Video-Instruct
Author: HuggingFaceTB

SmolVLM2-500M-Video is a lightweight multimodal model designed to analyze video content. The model processes videos, images, and text inputs to generate text outputs - whether answering questions about media files, comparing visual content, or transcribing text from images. Despite its compact size, requiring only 1.8GB of GPU RAM for video inference, it delivers robust performance on complex multimodal tasks. This e...

Params

510 M

Context

—

Downloads 30d

1.2 M

Likes

165

Commercial use: allowed apache-2.0 Not gated SAFETENSORS 1 languages View on Hugging Face ↗

Download history

daily snapshots · 10 days

1.2 M995 K

Jul 28Jul 31Aug 3Aug 6

Can you run it?

Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.

File	Quant	Size	Est. VRAM	Verdict on RTX 4090 · 24 GB
model.safetensors	f32	2.0 GB	2.8 GB	✅ Runs comfortably

Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.

Specifications

Architecture: SmolVLMForConditionalGeneration
Parameters: 510 M
Tensor type: F32
Vocabulary: 49,280
Licence: apache-2.0
First seen on the Hub: 2025-02-11
Training datasets: HuggingFaceM4/the_cauldron, HuggingFaceM4/Docmatix, lmms-lab/LLaVA-OneVision-Data, lmms-lab/M4-Instruct-Data, HuggingFaceFV/finevideo, MAmmoTH-VL/MAmmoTH-VL-Instruct-12M, lmms-lab/LLaVA-Video-178K, orrzohar/Video-STaR, Mutonix/Vript, TIGER-Lab/VISTA-400K, Enxin/MovieChat-1K_train, ShareGPT4Video/ShareGPT4Video
Added to our catalog: 2026-07-28

Compare with

Qwen3.5-9B 12.4 M gemma-4-31B-it 11.9 M gemma-4-26B-A4B-it 11.7 M Qwen2.5-VL-7B-Instruct 9.3 M Qwen3.6-35B-A3B-FP8 8.9 M Qwen3.6-27B-FP8 7.8 M

Appears in

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