Boosting LLM Performance with Unsloth and SDPA Integration 🚀

We're thrilled to unveil two major upgrades to MonsterTuner, designed to supercharge your LLM fine-tuning: Unsloth and Scaled Dot-Product Attention (SDPA). These innovations bring significant enhancements in performance, efficiency, and context length.

Unsloth Integration:

Unsloth eliminates inefficiencies and redundancies, delivering over 100% performance improvement during fine-tuning.

In our benchmark tests, Unsloth-optimized models completed tasks in just 40 seconds compared to 87 seconds for non-Unsloth variants.

Performance Highlights:

• LLaMa3 8B:

  • Normal context length: 2048
  • With Unsloth: 3072

• Tiny LLaMa:

  • Normal context length: 2048
  • With Unsloth: 6144

• Codellama 34B:

  • Normal context length: 256
  • With Unsloth: 512

SDPA Integration:

SDPA enhances model focus, efficiency, and scalability by computing scaled attention scores, dynamically improving context and relationships in data.

Performance Highlights:

• LLaMa 2:

  • Without SDPA: 1024
  • With SDPA: 3072

• Falcon 40B:

  • Without SDPA: 256
  • With SDPA: 512

• Apple/OpenELM-3B:

  • Without SDPA: 2048
  • With SDPA: 4096

Key Benefits of using Unsloth and SDPA in LLM fine-tuning:

• Enhanced Efficiency: Reduce computational costs and improve model performance.
• Improved Focus: Prioritize essential information for better task performance.
• Parallelization and Speed: Achieve faster and more efficient computations.
• Scalability: Handle larger models and datasets effortlessly.
• Long-Range Dependencies: Model long-range dependencies with ease.

For more information, please refer to the detailed blog.

Experience enhanced performance by launching your fine-tuning job here.

Publish Date: 17-07-2024