👁️ Understanding Vision Language Models (VLM) in LM-Kit.NET

📄 TL;DR

Vision Language Models (VLMs) are multimodal AI systems that can process and understand both images and text simultaneously. Unlike text-only LLMs, VLMs can analyze photographs, documents, charts, and screenshots while maintaining full conversational capabilities. In LM-Kit.NET, VLM support enables image-based chat, document understanding, visual question answering, and OCR through vision via the VlmOcr class and vision-capable models like Qwen2-VL and Gemma3-VL, all running locally on-device.

📚 What are Vision Language Models?

Definition: Vision Language Models are neural networks trained to understand both visual and textual information, enabling them to:

• Describe images in natural language
• Answer questions about visual content
• Extract text from images (OCR)
• Analyze documents with complex layouts
• Reason about charts, diagrams, and screenshots

The Multimodal Architecture

+--------------------------------------------------------------------------+
|                    Vision Language Model Architecture                    |
+--------------------------------------------------------------------------+
|                                                                          |
|  +-----------------+                      +-----------------+            |
|  |   Image Input   |                      |   Text Input    |            |
|  |                 |                      |                 |            |
|  |  +-----------+  |                      |  "What is in    |            |
|  |  |           |  |                      |   this image?"  |            |
|  |  |   [IMG]   |  |                      |                 |            |
|  |  |           |  |                      |                 |            |
|  |  +-----------+  |                      +--------┬--------+            |
|  +--------┬--------+                               |                     |
|           |                                        |                     |
|           v                                        v                     |
|  +-----------------+                      +-----------------+            |
|  |  Vision Encoder |                      | Text Tokenizer  |            |
|  |  (ViT / SigLIP) |                      |                 |            |
|  +--------┬--------+                      +--------┬--------+            |
|           |                                        |                     |
|           +----------------┬-----------------------+                     |
|                            |                                             |
|                            v                                             |
|                   +-----------------+                                    |
|                   |  Fusion Layer   |                                    |
|                   | (Cross-Attention|                                    |
|                   |  or Projection) |                                    |
|                   +--------┬--------+                                    |
|                            |                                             |
|                            v                                             |
|                   +-----------------+                                    |
|                   | Language Model  |                                    |
|                   |  (Transformer)  |                                    |
|                   +--------┬--------+                                    |
|                            |                                             |
|                            v                                             |
|                   +-----------------+                                    |
|                   |  Text Output    |                                    |
|                   | "A cat sitting  |                                    |
|                   |  on a couch..." |                                    |
|                   +-----------------+                                    |
|                                                                          |
+--------------------------------------------------------------------------+

VLM vs Text-Only LLM

Capability	Text-Only LLM	Vision Language Model
Text understanding	Yes	Yes
Image analysis	No	Yes
Document OCR	No	Yes
Chart interpretation	No	Yes
Screenshot analysis	No	Yes
Visual Q&A	No	Yes
Multimodal reasoning	No	Yes

🏗️ VLM Capabilities in LM-Kit.NET

Supported Vision Models

LM-Kit.NET supports several vision-capable model families:

Model Family	Model IDs	Strengths
Qwen2-VL	qwen2-vl:2b, qwen2-vl:7b	Excellent document understanding, multilingual
Gemma3-VL	gemma3:4b, gemma3:12b, gemma3:27b	Strong reasoning, visual Q&A

Core VLM Operations

+--------------------------------------------------------------------------+
|                      LM-Kit.NET VLM Capabilities                         |
+--------------------------------------------------------------------------+
|                                                                          |
|  +-----------------+  +-----------------+  +-----------------+           |
|  |   VlmOcr        |  |  Visual Chat    |  |  TextExtraction |           |
|  |                 |  |                 |  |  (Vision Mode)  |           |
|  |  • Text from    |  |  • Image Q&A    |  |                 |           |
|  |    images       |  |  • Description  |  |  • Structured   |           |
|  |  • Layout-aware |  |  • Analysis     |  |    extraction   |           |
|  |  • Handwriting  |  |  • Comparison   |  |  • Schema-based |           |
|  +-----------------+  +-----------------+  +-----------------+           |
|                                                                          |
|  +-----------------+  +-----------------+  +-----------------+           |
|  |  Categorization |  |  LayoutAnalysis |  |  Agent Vision   |           |
|  |  (Image Input)  |  |                 |  |                 |           |
|  |                 |  |  • Document     |  |  • Tool-assisted|           |
|  |  • Image class  |  |    structure    |  |    image tasks  |           |
|  |  • Document type|  |  • Region       |  |  • Multimodal   |           |
|  |  • Scene detect |  |    detection    |  |    workflows    |           |
|  +-----------------+  +-----------------+  +-----------------+           |
|                                                                          |
+--------------------------------------------------------------------------+

⚙️ Using VLMs in LM-Kit.NET

Basic Image Understanding

using LMKit.Model;
using LMKit.TextGeneration;
using LMKit.Graphics;

// Load a vision-capable model
var model = LM.LoadFromModelID("qwen2-vl:7b");

// Create chat instance
var chat = new MultiTurnConversation(model);

// Load image
var image = ImageData.FromFile("product_photo.jpg");

// Ask about the image
var response = chat.Submit(
    "Describe this product in detail. What are its key features?",
    image,
    CancellationToken.None
);

Console.WriteLine(response);

Vision-Based OCR

using LMKit.Model;
using LMKit.Graphics;

var model = LM.LoadFromModelID("gemma3:12b");

// Create VLM-based OCR
var vlmOcr = new VlmOcr(model);

// Extract text from scanned document
var result = vlmOcr.Execute(
    ImageData.FromFile("scanned_invoice.png"),
    CancellationToken.None
);

Console.WriteLine(result.Text);
// Preserves layout and structure from the original document

Structured Extraction from Images

using LMKit.Model;
using LMKit.Extraction;
using LMKit.Data;

var model = LM.LoadFromModelID("qwen2-vl:7b");

var extractor = new TextExtraction(model);

// Define schema for invoice extraction
extractor.Elements.Add(new TextExtractionElement("vendor", ElementType.String)
{
    Description = "Company name of the vendor"
});
extractor.Elements.Add(new TextExtractionElement("total", ElementType.Double)
{
    Description = "Total amount due"
});
extractor.Elements.Add(new TextExtractionElement("date", ElementType.Date)
{
    Description = "Invoice date"
});

// Extract from image using vision
extractor.SetContent(new Attachment("invoice_scan.jpg"));
extractor.PreferredInferenceModality = InferenceModality.Vision;

var result = extractor.Parse(CancellationToken.None);
Console.WriteLine(result.Json);

Document Classification with Vision

using LMKit.Model;
using LMKit.TextAnalysis;
using LMKit.Data;

var model = LM.LoadFromModelID("gemma3:4b");

var categorizer = new Categorization(model);
categorizer.Categories.Add("Invoice");
categorizer.Categories.Add("Contract");
categorizer.Categories.Add("Resume");
categorizer.Categories.Add("Receipt");
categorizer.Categories.Add("ID Document");

// Classify scanned document by visual appearance
categorizer.SetContent(new Attachment("unknown_document.pdf"));
var result = categorizer.Categorize(CancellationToken.None);

Console.WriteLine($"Document type: {result.Category} ({result.Confidence:P1})");

Multi-Image Comparison

using LMKit.Model;
using LMKit.TextGeneration;
using LMKit.Graphics;

var model = LM.LoadFromModelID("qwen2-vl:7b");
var chat = new MultiTurnConversation(model);

// Load multiple images
var images = new[]
{
    ImageData.FromFile("product_v1.jpg"),
    ImageData.FromFile("product_v2.jpg")
};

// Compare images
var response = chat.Submit(
    "Compare these two product images. What are the differences?",
    images,
    CancellationToken.None
);

Console.WriteLine(response);

🎯 VLM Use Cases

1. Document Intelligence

Process scanned documents, PDFs, and images to extract structured data:

• Invoice processing: Extract vendor, amounts, line items from scanned invoices
• Form parsing: Read filled forms and convert to structured data
• Receipt scanning: Capture expense data from photos of receipts
• ID verification: Extract information from identity documents

2. Visual Question Answering

Enable conversational interaction with images:

• Product analysis: Describe features, identify defects, compare variants
• Technical support: Analyze screenshots, identify UI elements, guide users
• Medical imaging: Describe findings (with appropriate disclaimers)
• Real estate: Analyze property photos, describe features

3. Content Moderation

Automatically analyze visual content:

• Safety screening: Detect inappropriate or harmful content
• Brand compliance: Verify visual assets meet brand guidelines
• Quality control: Identify defects in product images

4. Accessibility

Generate descriptions for visual content:

• Alt text generation: Create image descriptions for accessibility
• Scene description: Describe visual content for visually impaired users
• Chart interpretation: Convert visual data to textual summaries

📊 Inference Modalities

LM-Kit.NET supports three inference modalities for flexible content processing:

Modality	Description	Best For
Text	Text-only processing	Pure text documents, chat
Vision	Image-focused with VLM	Scanned documents, photos
Multimodal	Combined text and vision	Mixed content, PDFs with images

// Let LM-Kit choose the best modality automatically
extractor.PreferredInferenceModality = InferenceModality.Multimodal;

// Or force vision mode for image-heavy content
extractor.PreferredInferenceModality = InferenceModality.Vision;

// Or use text-only for better performance on text documents
extractor.PreferredInferenceModality = InferenceModality.Text;

📖 Key Terms

• Vision Language Model (VLM): A multimodal model that processes both images and text
• Vision Encoder: The component that converts images into embeddings (e.g., ViT, SigLIP)
• Cross-Attention: Mechanism allowing text tokens to attend to image features
• Visual Q&A: Task of answering questions about image content
• Multimodal: Systems that process multiple types of input (text, images, audio)
• Inference Modality: The type of content processing mode (text, vision, multimodal)

📚 Related API Documentation

• VlmOcr: Vision-based OCR
• ImageData: Image input handling
• Attachment: Universal document input
• InferenceModality: Processing mode selection
• MultiTurnConversation: Multimodal chat

🔗 Related Glossary Topics

• Intelligent Document Processing (IDP): End-to-end document automation
• Structured Data Extraction: Schema-based extraction from images
• AI Agents: Agents with vision capabilities
• Embeddings: Vector representations including image embeddings
• Attention Mechanism: Core of vision-language fusion

🌐 External Resources

• LLaVA (Liu et al., 2023): Visual Instruction Tuning
• Qwen-VL (Bai et al., 2023): Versatile Vision-Language Model
• PaLI (Chen et al., 2022): Pathways Language and Image model
• LM-Kit VLM Demo: Multimodal chat example

📝 Summary

Vision Language Models (VLMs) extend traditional language models with the ability to see and understand images, enabling powerful multimodal applications. In LM-Kit.NET, VLM support includes vision-based OCR (VlmOcr), visual chat (image Q&A), structured extraction from images, and document classification. Models like Qwen2-VL and Gemma3 provide strong vision capabilities that integrate seamlessly with LM-Kit's text processing features. By selecting the appropriate inference modality (Text, Vision, or Multimodal), developers can optimize for different content types. VLMs enable document intelligence, visual Q&A, content moderation, and accessibility applications, all running locally on-device for maximum privacy and performance.