> ## Documentation Index > Fetch the complete documentation index at: https://docs.monostate.ai/llms.txt > Use this file to discover all available pages before exploring further. # Model Types > Different architectures for different tasks # Understanding Model Types Different AI tasks require different model architectures. Think of it like choosing the right tool for the job - you wouldn't use a hammer to paint a wall. ## Language Models (LLMs) The most versatile models that understand and generate human language. ### What They Do Language models can: * Answer questions * Write content * Translate languages * Summarize text * Generate code * Follow instructions ### Common Models | Model | Size | Good For | Training Time | | ----------- | --------- | ---------------------------------- | ---------------- | | **GPT-2** | 124M-1.5B | Starting point, quick experiments | Minutes to hours | | **BERT** | 110M-340M | Understanding text, classification | Hours | | **T5** | 60M-11B | Text-to-text tasks | Hours to days | | **LLaMA** | 7B-70B | General purpose, chat | Days to weeks | | **Mistral** | 7B | Efficient, balanced performance | Hours to days | ### When to Use Choose language models when you need: * Natural language understanding * Text generation * Question answering * Conversational AI * Code generation ## Classification Models Specialized for sorting things into categories. ### Text Classification Categorize text into predefined groups: * Sentiment analysis (positive/negative) * Topic classification * Intent detection * Language detection **Best models**: BERT, DistilBERT, RoBERTa ### Image Classification Identify what's in an image: * Object recognition * Medical diagnosis * Quality control * Content moderation **Best models**: ResNet, EfficientNet, Vision Transformer (ViT) ### Multimodal Classification Handle both text and images: * Meme understanding * Document analysis * Product categorization **Best models**: CLIP, LayoutLM, ALIGN ## Token Classification Labels individual words or tokens in text. ### Named Entity Recognition (NER) Find and label specific information: * Names of people, places, organizations * Dates and times * Product names * Medical terms ### Part-of-Speech Tagging Identify grammatical roles: * Nouns, verbs, adjectives * Sentence structure analysis **Best models**: BERT-NER, RoBERTa-token, SpaCy transformers ## Sequence-to-Sequence Transform one sequence into another. ### Translation Convert text between languages: * Document translation * Real-time chat translation * Code translation ### Summarization Condense long text: * Article summaries * Meeting notes * Report digests ### Question Answering Extract answers from context: * Customer support * Document Q\&A * Educational tools **Best models**: T5, BART, mT5 (multilingual) ## Computer Vision Models Process and understand images. ### Object Detection Find and locate objects in images: * Bounding boxes around objects * Count items * Track movement **Best models**: YOLO, Faster R-CNN, DETR ### Image Segmentation Pixel-level understanding: * Medical imaging * Autonomous driving * Photo editing **Best models**: U-Net, Mask R-CNN, SAM ### Image Generation Create new images: * Art generation * Product visualization * Data augmentation **Best models**: Stable Diffusion, DALL-E, Midjourney ## Tabular Models Work with structured data like spreadsheets. ### Regression Predict continuous values: * Price prediction * Sales forecasting * Risk scoring ### Classification Categorize rows: * Customer churn * Fraud detection * Disease diagnosis **Best models**: XGBoost, CatBoost, TabNet ## Choosing the Right Model ### Consider Your Data | Data Type | Recommended Models | | -------------------------- | -------------------- | | Short text (\< 512 tokens) | BERT, DistilBERT | | Long text (> 512 tokens) | Longformer, BigBird | | Conversations | DialoGPT, Blenderbot | | Code | CodeBERT, CodeT5 | | Multiple languages | mBERT, XLM-RoBERTa | | Images | ResNet, EfficientNet | | Images + Text | CLIP, ALIGN | | Structured data | XGBoost, CatBoost | ### Consider Your Resources **Limited Resources (\< 8GB GPU)** * DistilBERT (66M parameters) * MobileBERT (25M parameters) * TinyBERT (15M parameters) **Moderate Resources (8-16GB GPU)** * BERT-base (110M parameters) * GPT-2 small (124M parameters) * RoBERTa-base (125M parameters) **Good Resources (24GB+ GPU)** * GPT-2 large (774M parameters) * T5-large (770M parameters) * LLaMA 7B (7B parameters) ### Consider Your Accuracy Needs **Speed over accuracy** * Use distilled models (DistilBERT, DistilGPT-2) * Smaller architectures * Quantized models **Accuracy over speed** * Use larger models * Ensemble multiple models * Longer training times ## Model Sizes and Trade-offs ### Parameters Count Parameters are the adjustable parts of a model. More parameters usually mean: * Better understanding * Higher accuracy * More memory needed * Slower inference ### Size Guidelines | Size | Parameters | Use Case | Training Data Needed | | --------- | ---------- | ---------------------- | -------------------- | | **Tiny** | \< 50M | Mobile apps, real-time | 100s examples | | **Small** | 50M-150M | Standard applications | 1000s examples | | **Base** | 150M-500M | Production systems | 10,000s examples | | **Large** | 500M-3B | High accuracy needs | 100,000s examples | | **XL** | 3B+ | State-of-the-art | Millions examples | ## Pre-trained vs From Scratch ### Use Pre-trained Models **99% of the time**, start with a pre-trained model: * Already understands language/images * Needs less training data * Faster to train * Better results ### Train From Scratch Only When * Working with unique data types * Special domain (medical, legal) * Custom architectures * Research purposes ## Fine-tuning Strategies ### Full Fine-tuning Update all model parameters: * Best accuracy * Needs more memory * Risk of overfitting ### LoRA (Low-Rank Adaptation) Update only small adapters: * 90% less memory * Faster training * Slightly lower accuracy * Perfect for large models ### Prompt Tuning Train only prompt embeddings: * Minimal memory * Very fast * Good for few-shot learning ### Freeze Strategies Freeze some layers: * **Freeze early layers**: Keep general features * **Freeze late layers**: Keep task-specific features * **Gradual unfreezing**: Start frozen, slowly unfreeze ## Multi-task Models Some models can handle multiple tasks: ### T5 Family * Text summarization * Translation * Question answering * Classification Just change the prompt prefix: * "summarize: ..." * "translate English to French: ..." * "question: ... context: ..." ### FLAN Models Pre-trained on many tasks: * Better zero-shot performance * More flexible * Good instruction following ## Specialized Architectures ### Transformers The current standard: * Parallel processing * Long-range dependencies * Most modern models ### CNNs (Convolutional Neural Networks) Still great for images: * Efficient * Well-understood * Good for edge devices ### RNNs (Recurrent Neural Networks) Older but still useful: * Sequential data * Time series * Streaming applications ## Listen: Beyond LLMs - A Deep Dive A 45-minute conversation about model types beyond language models, covering vision, tabular, and specialized architectures.