88 lines
No EOL
1.7 KiB
Markdown
88 lines
No EOL
1.7 KiB
Markdown
# Language Models Implementation
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This directory contains implementations and examples for various language models.
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## GPT-4 Integration
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### Implementation Examples
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- Basic API integration
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- Advanced prompt engineering
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- Context management
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- Response handling
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### Best Practices
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- Token management
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- Error handling
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- Rate limiting
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- Cost optimization
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## Claude Integration
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### System Prompts
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- Role-based prompting
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- Task-specific prompts
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- Context management
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- Output formatting
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### Usage Patterns
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- Conversation management
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- Multi-turn dialogues
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- Context preservation
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- Response parsing
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## LLaMA Integration
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### Custom Implementations
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- Model loading
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- Inference optimization
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- Memory management
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- Batch processing
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### Optimizations
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- Quantization
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- Model pruning
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- Hardware acceleration
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- Performance tuning
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## Usage Examples
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```python
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# Example: GPT-4 Integration
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from openai import OpenAI
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client = OpenAI()
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response = client.chat.completions.create(
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model="gpt-4",
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messages=[
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Hello, how are you?"}
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]
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)
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```
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## Best Practices
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1. Always handle API errors gracefully
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2. Implement proper rate limiting
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3. Use appropriate model parameters
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4. Monitor token usage
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5. Cache responses when appropriate
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6. Implement proper logging
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7. Use environment variables for API keys
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## Performance Considerations
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- Token usage optimization
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- Response time monitoring
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- Cost tracking
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- Resource utilization
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- Scaling strategies
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## Contributing
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Please follow these guidelines when contributing:
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1. Include clear documentation
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2. Add usage examples
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3. Implement error handling
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4. Add performance benchmarks
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5. Include unit tests |