Finetuning LLM

📊 Performance Optimization Techniques

• Quantization: 4-bit and 8-bit precision options for memory efficiency
• Parameter-Efficient Training: LoRA and PEFT for minimal computational overhead
• Mixed Precision: Advanced training acceleration with maintained model quality
• Memory Management: Gradient checkpointing and optimized batch processing

Parameter-Efficient Fine-Tuning

1. LoRA (Low-Rank Adaptation)

LoRA is a technique that reduces the number of trainable parameters during fine-tuning by decomposing weight updates into low-rank matrices. Instead of updating all model parameters, LoRA:

• Freezes the original pre-trained weights
• Adds small trainable matrices that capture the adaptation
• Reduces memory usage and training time significantly
• Maintains model performance while using fewer resources

LoRA Architecture:

Previous layer ────────► W' ────────► Next layer
                        R^n×m
                          ▲
                          │
                    ┌─────────────┐
                    │     W_AB    │ ← Trainable LoRA weights
                    │   R^n×m     │
                    └─────────────┘
                          ▲
                          │
              ┌─────┐    ┌─────┐
              │  A  │ ×  │  B  │ } r - rank (typically 4-64)
              │R^n×r│    │R^r×m│
              └─────┘    └─────┘
                r - rank

Where:

• W: Original frozen pre-trained weights
• A & B: Small trainable low-rank matrices (rank r << original dimensions)
• W_AB = A × B: The low-rank adaptation added to original weights
• W' = W + W_AB: Final effective weights during inference

2. QLoRA (Quantized Low-Rank Adaptation)

QLoRA extends LoRA by adding quantization to further optimize memory usage:

• Quantizes the base model to 4-bit precision using NF4 (Normal Float 4)
• Applies LoRA adapters on top of the quantized model
• Enables fine-tuning of larger models (like 65B parameters) on consumer GPUs
• Achieves up to 65% memory reduction compared to standard fine-tuning
• Uses double quantization and paged optimizers for additional efficiency

🚀 Featured Fine-Tuning Methodologies

Our advanced implementation demonstrates efficient model adaptation through:

Workflow Overview:

1. Environment Configuration: Establish a robust development environment with all required dependencies for QLora, and PEFT frameworks.
2. Dataset Engineering: Transform and preprocess your training data into optimal formats for effective model learning.
3. Hyperparameter Optimization: Fine-tune critical training parameters including learning rates, batch configurations, and epoch scheduling.
4. Training Execution: Launch the fine-tuning pipeline leveraging Mistral's architecture enhanced with QLora quantization and PEFT optimizations.
5. Model Validation: Conduct thorough performance evaluation using comprehensive validation metrics to ensure quality standards.
6. Production Deployment: Deploy your optimized model for real-world inference applications.

1. Llama 2 Fine-Tuning with LoRA

Specialized implementation for Llama 2 model fine-tuning featuring:

• 4-bit precision quantization for memory efficiency
• LoRA (Low-Rank Adaptation) for parameter-efficient training
• Comprehensive prompt template handling
• Production-ready model deployment workflows

Reference Implementation: Fine_tune_Llama_2.ipynb

🎯 Use Cases

• Domain-specific model adaptation
• Instruction following enhancement
• Knowledge injection and specialization
• Multi-task learning implementations
• Research and prototyping workflows