AINovice2005/quantized-SmolVLM2-2.2B-Base

SmolVLM2‑2.2B‑Base Quantized

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🚀 Model Description

This is a quantized version of SmolVLM2‑2.2B‑Base, a compact yet powerful vision+language model by Hugging Face. It’s designed for multimodal understanding—including images, multi‑image inputs, and videos—while offering faster and more efficient inference thanks to quantization. Perfect for on-device and resource-constrained deployments.

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🔧 Base Model Summary

• Name: SmolVLM2‑2.2B‑Base
• Publisher: Hugging Face TB
• Architecture: Idefics3 vision encoder + SmolLM2‑1.7B text decoder
• Modalities: image, multi-image, video, text
• Capabilities: captioning, VQA, video analysis, diagram understanding, text-in-image reading

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📏 Quantization Details

Method: torchao quantization

Weight Precision: int8

Activation Precision: int8 dynamic

Technique: Symmetric mapping

Impact: Significant reduction in model size with minimal loss in reasoning, coding, and general instruction-following capabilities.

🎯 Intended Use

• On-device or low-VRAM systems (edge, mobile, small GPUs)
• Multimodal tasks: VQA, captioning, comparing images, video transcription
• Research on quantized multimodal models

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⚠️ Limitations & Considerations

• May underperform compared to full-precision version
• Only supports the modalities supported by the base model

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