핵심 개념
Supervised fine-tuning is an effective method to customize pre-trained language models like Llama 3.1 for specific use cases, improving performance and adding new capabilities at a lower cost compared to using closed-source models.
초록
This article provides a comprehensive overview of supervised fine-tuning (SFT) for large language models (LLMs). It compares SFT to prompt engineering techniques, explains the main SFT methods (full fine-tuning, LoRA, and QLoRA), and demonstrates how to efficiently fine-tune the Llama 3.1 8B model using the Unsloth library on Google Colab.
The key highlights include:
- SFT is a technique to improve and customize pre-trained LLMs by retraining them on a smaller dataset of instructions and answers. It can enhance performance, add new knowledge, or adapt the model to specific tasks and domains.
- The three main SFT techniques are full fine-tuning, LoRA, and QLoRA. LoRA and QLoRA are parameter-efficient methods that introduce small adapters instead of retraining the entire model, reducing memory usage and training time.
- The article demonstrates how to fine-tune Llama 3.1 8B using QLoRA and the Unsloth library, which provides 2x faster training and 60% memory savings compared to other options. The fine-tuned model is then saved in various formats, including GGUF, for deployment and further use.
- The article also provides suggestions for evaluating the fine-tuned model, aligning it with user preferences, quantizing it for faster inference, and deploying it on platforms like Hugging Face Spaces.
통계
Llama 3.1 8B model has 8 billion parameters.
The fine-tuned model using QLoRA only trains 42 million out of 8 billion parameters (0.5196%).
The fine-tuning on 100k samples took 4 hours and 45 minutes on an A100 GPU.
인용구
"Instead of using frozen, general-purpose LLMs like GPT-4o and Claude 3.5, you can fine-tune Llama 3.1 for your specific use cases to achieve better performance and customizability at a lower cost."
"LoRA and QLoRA are parameter-efficient methods that introduce small adapters instead of retraining the entire model, reducing memory usage and training time."
"Unsloth provides 2x faster training and 60% memory savings compared to other options, making it ideal in a constrained environment like Colab."