LLaMA 2 Developer Guide
Model Overview
LLaMA 2 is Meta's open-source language model with three sizes, each with different hardware requirements and capabilities.
Model Specs
| Model | Params | VRAM (FP16) | Hardware |
|---|---|---|---|
| 7B | 7B | 13GB | RTX 3090+ |
| 13B | 13B | 25GB | A100 40GB |
| 70B | 70B | 140GB | Multi-GPU |
Performance
| Task | 7B | 13B | 70B |
|---|---|---|---|
| MMLU | 45.3 | 54.8 | 68.9 |
| Math | 4.1 | 6.7 | 13.5 |
| Code | 12.8 | 18.3 | 29.9 |
Quick Start
from transformers import LlamaForCausalLM, LlamaTokenizer
model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")
tokenizer = LlamaTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
# Generate text
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
Optimization Recommendations
- Quantization: Use INT8 or INT4 quantization for memory efficiency
- Batch Processing: Implement dynamic batching for throughput optimization
- Caching: Leverage KV-cache for multi-turn conversations
- Hardware: GPU with sufficient VRAM or distributed inference setup
Safety and Responsible Use
Known Limitations
- Factual Accuracy: May generate plausible but incorrect information
- Temporal Knowledge: Training cutoff limits recent information access
- Context Constraints: 4K token limit restricts long document processing
- Bias Potential: May reflect biases present in training data
Risk Mitigation Strategies
- Content Filtering: Implement robust output filtering systems
- Human Oversight: Maintain human review for critical applications
- Monitoring: Continuous assessment of output quality and safety
- User Education: Clear communication of model limitations
Recommended Monitoring Metrics
- Output factual accuracy rates
- Bias detection across demographic groups
- Harmful content generation frequency
- User satisfaction and trust scores
- Performance consistency over time
Licensing and Compliance
License Structure
- Research Use: Permitted under standard license terms
- Commercial Use: Requires separate commercial licensing agreement
- Distribution: Restrictions apply to model redistribution and modification
Compliance Requirements
- Acceptance of Meta's Acceptable Use Policy
- Implementation of appropriate safety measures
- Regular monitoring and reporting for commercial deployments
- Adherence to data privacy and security standards
Integration Best Practices
Development Workflow
- Model Selection: Choose appropriate size based on performance requirements and hardware constraints
- Fine-tuning: Consider task-specific fine-tuning for specialized applications
- Safety Implementation: Deploy comprehensive safety measures before production use
- Performance Testing: Conduct thorough evaluation on representative datasets
- Monitoring Setup: Establish continuous monitoring and feedback systems
Production Deployment
- Infrastructure: Ensure adequate computational resources and scaling capabilities
- Latency Optimization: Implement caching and batching strategies
- Fallback Systems: Design robust error handling and fallback mechanisms
- Security: Implement proper authentication and access controls
Support and Resources
Technical Documentation
- Official Meta AI Documentation
- Hugging Face Model Hub
- Research Paper: "Llama 2: Open Foundation and Fine-Tuned Chat Models"
Community Resources
- GitHub repositories with implementation examples
- Community forums and discussion groups
- Third-party tools and integrations
- Performance optimization guides
This documentation is based on publicly available information about LLaMA 2 and follows industry best practices for AI model documentation. For the most current information, consult official Meta AI resources.