smolvlm

SKILL.md

SmolVLM - Local Image Analysis

Analyze images locally using SmolVLM-2B, a state-of-the-art compact vision-language model optimized for Apple Silicon via mlx-vlm.

Quick Usage

Describe an Image

python ~/.claude/skills/smolvlm/scripts/view_image.py /path/to/image.png

Ask a Question About an Image

python ~/.claude/skills/smolvlm/scripts/view_image.py /path/to/image.png "What text is visible?"

Specific Tasks

# Extract text (OCR)
python ~/.claude/skills/smolvlm/scripts/view_image.py screenshot.png "Extract all text"

# UI analysis
python ~/.claude/skills/smolvlm/scripts/view_image.py ui.png "Describe the UI elements"

# Detailed description
python ~/.claude/skills/smolvlm/scripts/view_image.py photo.jpg --detailed

Effective Prompts

General Description

  • "Describe this image" - Basic description
  • "Describe this image in detail, including colors, composition, and any text" - Comprehensive

Text Extraction (OCR)

  • "Extract all visible text from this image"
  • "What text appears in this screenshot?"
  • "Read the text in this document"

UI/Screenshot Analysis

  • "Describe the user interface elements"
  • "What buttons and controls are visible?"
  • "Identify the application and its current state"

Visual Question Answering

  • "How many [objects] are in this image?"
  • "What color is the [object]?"
  • "Is there a [object] in this image?"

Code/Technical

  • "What programming language is shown?"
  • "Describe what this code does"
  • "Identify any errors in this code screenshot"

Model Details

Spec Value
Model SmolVLM-2B-Instruct
Size ~4GB
Peak Memory 5.8GB
Speed ~94 tok/s (M-series)
Supported Formats PNG, JPG, JPEG, GIF, WebP

Requirements

  • macOS with Apple Silicon (M1/M2/M3)
  • Python 3.10+
  • mlx-vlm package: uv pip install mlx-vlm --system

Troubleshooting

"Model not found": First run downloads the model (~4GB). Wait for completion.

Out of memory: Close other applications. Model needs ~6GB free RAM.

Slow first inference: Model loading takes 10-15s on first use, subsequent calls are faster.

Weekly Installs
27
GitHub Stars
12
First Seen
Feb 21, 2026
Installed on
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