LLM Settings and Parameters
Core Parameters
Temperature
- Range: 0.0 to 2.0
- Purpose: Controls randomness in responses
- Use Cases:
- Low (0.0-0.3): Factual, consistent responses
- Medium (0.4-0.7): Balanced creativity
- High (0.8-2.0): More creative, varied outputs
Top-p (Nucleus Sampling)
- Range: 0.0 to 1.0
- Purpose: Controls response diversity
- Use Cases:
- Low (0.1-0.3): Focused, deterministic outputs
- Medium (0.4-0.7): Natural language generation
- High (0.8-1.0): More diverse responses
Max Tokens
- Purpose: Limits response length
- Considerations:
- Model context window
- Input token count
- Cost optimization
- Response completeness
Advanced Settings
Frequency Penalty
- Range: -2.0 to 2.0
- Purpose: Reduces word repetition
- Effects:
- Positive values: Discourage repetition
- Negative values: Allow repetition
- Zero: Neutral behavior
Presence Penalty
- Range: -2.0 to 2.0
- Purpose: Controls topic diversity
- Effects:
- Positive values: Encourage new topics
- Negative values: Stay on topic
- Zero: Balanced approach
Stop Sequences
- Purpose: Define response endpoints
- Examples:
- Custom delimiters
- End markers
- Special tokens
Context Window Settings
Input Context
- Token counting
- Context truncation
- Document chunking
- Memory management
Output Context
- Response formatting
- Stream handling
- Token budgeting
- Completion signals
Best Practices
Parameter Selection
- Match task requirements
- Test different combinations
- Monitor performance
- Adjust based on feedback
Optimization Tips
- Balance quality vs cost
- Consider latency impact
- Monitor token usage
- Implement caching
Use Case Examples
Creative Writing
{
"temperature": 0.8,
"top_p": 0.9,
"frequency_penalty": 0.3,
"presence_penalty": 0.3
}Factual Responses
{
"temperature": 0.2,
"top_p": 0.1,
"frequency_penalty": 0.0,
"presence_penalty": 0.0
}Code Generation
{
"temperature": 0.3,
"top_p": 0.2,
"frequency_penalty": 0.0,
"presence_penalty": 0.0
}