Fine-tune your own Llama 2 to replace GPT-3.5/4
Analysis
The article discusses fine-tuning open-source LLMs, specifically Llama 2, to achieve performance comparable to GPT-3.5/4. It highlights the process, including data labeling, fine-tuning, efficient inference, and cost/performance evaluation. The author provides code examples and emphasizes the effectiveness of fine-tuning, even with a relatively small number of examples. It also acknowledges the advantages of prompting.
Key Takeaways
- •Fine-tuning LLMs can achieve performance comparable to larger models like GPT-3.5/4.
- •The process involves data labeling, fine-tuning, and efficient inference.
- •Fine-tuning can be effective with a relatively small number of examples (50+).
- •The article provides code examples for practical implementation.
Reference
“The 7B model we train here matches GPT-4’s labels 95% of the time on the test set, and for the 5% of cases where they disagree it’s often because the correct answer is genuinely ambiguous.”