LLM Fine-Tuning Playground
Model Settings
This text is likely an example sentence used for typing practice (probability: 87%).
Fine-Tuning Details
This model was fine-tuned on a custom dataset of 10,000 example sentences using PyTorch and Hugging Face Transformers. Achieved 92% accuracy on validation set.
Prompt Engineering Toolkit
Prompt Strategy
Classify the following product review as either "positive" or "negative":
"{review_text}"
Answer:
Strategy Benefits
- No training examples needed
- Fastest to implement
- Works for broad tasks
Limitations
- Lower accuracy on niche tasks
- May require prompt tuning
- Vulnerable to ambiguous tasks
Social Trend Forecasting
Forecast Settings
or drag and drop
Forecast visualization will appear here
Feature importance analysis will appear here
Model Architecture
This forecasting pipeline combines time-series analysis with social media metadata (likes, shares, author credibility) to predict virality. The SHAP explainer highlights which features most influence predictions.
Real-Time Sentiment Analysis
Model Settings
Key Sentiment Drivers
Model Details
This sentiment analyzer was fine-tuned on 50,000 product reviews from multiple domains. Achieves 89% accuracy on held-out test data with F1-score of 0.91 for positive/negative classification.