---
license: apache-2.0
base_model:
- HuggingFaceTB/SmolVLM2-2.2B-Base
pipeline_tag: image-text-to-text
library_name: transformers
---

**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.
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### 🎯 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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