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.
🔧 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
📏 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
⚠️ Limitations & Considerations
May underperform compared to full-precision version
Only supports the modalities supported by the base model
