How This Demo Works
This interactive demo simulates the fine-tuning process for AI language models. Select a task type, adjust training parameters, and observe how customization improves performance on specific NLP tasks. After training completes, test the model with provided examples or your own text to see results in real-time.
Parameter Explanations
Base Model Size (2B-20B)
Larger models typically perform better but require more computational resources. Industry standard sizes range from 7B to 13B parameters for most applications.
Dataset Size (1K-100K)
More training data generally leads to better performance, but quality matters more than quantity. Dataset sizes of 10K-50K examples are common for specialized tasks.
Learning Rate
Controls how quickly the model adapts to new data. Too high can cause unstable training; too low can result in slow convergence. Optimal values typically range from 0.00001 to 0.0001.
Batch Size
The number of examples processed in parallel during training. Larger batches provide more stable gradients but use more memory. Values between 16-32 often provide a good balance for most fine-tuning tasks.
Training Method
Different approaches to fine-tuning affect parameter efficiency and results. Full fine-tuning updates all parameters while LoRA and PEFT use more efficient techniques that modify fewer parameters.
Epochs
The number of complete passes through the training dataset. More epochs allow deeper learning but risk overfitting. For fine-tuning, 2-5 epochs is typically sufficient to see significant improvements.
How These Parameters Work Together
Fine-tuning is a balancing act between model capacity, data quality, and training configuration. Larger models with more data generally perform better, but require careful tuning of learning rate and batch size to converge efficiently. The training method determines how extensively the base model is modified, with full fine-tuning providing the most customization but requiring more computational resources than parameter-efficient methods like LoRA or PEFT. The number of epochs impacts how thoroughly the model adapts to the new task—too few and it may underfit, too many and it risks overfitting to your specific examples.
Task Types Explained
Sentiment Analysis
Determines whether text expresses positive, negative, or neutral emotions. In this demo, try entering product reviews or opinions to see the model identify sentiment with a confidence score. Fine-tuning improves the model's ability to recognize subtle emotional cues and domain-specific expressions.
Text Classification
Identifies the predominant parts of speech in text samples. Enter any text to see the model analyze the distribution of nouns, verbs, adjectives, etc. As training progresses, the model becomes more adept at recognizing grammatical patterns and linguistic structures.
Text Summarization
Condenses longer text into a concise summary while preserving key information. Try entering a news article or lengthy paragraph to see how the model extracts and synthesizes the most important points. Fine-tuning improves both the relevance and readability of generated summaries.
Select a Task Type
Choose from different domains to see how model customization impacts performance.
Test Your Examples
Click a sample or enter your own text to analyze
Training Progress
Model ready - Type text above to analyze