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This in-depth case study sheds light on when you can achieve GPT-4 level performance with a fine-tuned 7B parameter model.
Take SQL generation as an example.
Accuracy
🧿 Llama-2-7B: 3%
🧿 GPT-4: 79%
🧿 Llama-2-7B (fine-tuned): 86%
Out of the box, GPT-4 crushes Llama-2 (including the 70B parameter model), but when fine-tuned, Llama-2-7B wins.
That's superior accuracy with a model 2% of the size of GPT-4 (purportedly), therefore orders of magnitude cheaper. 🤯🤯🤯
This is an astounding lift 🙌🙌
But when does this work? 🤔
The math problem solving benchmark tells a different story.
Fine-tuning still gives a meaningful lift (doubling accuracy for the 7B model), but even the fine-tuned 70B model achieves only 62% accuracy (compared with 98% for GPT-4!!!). 🫢
The study goes into much more detail about when you might expect fine-tuning to give a big win, and when it still won't be enough.
https://t.co/7A6QqJxUqF
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A very special month for $MOG with many more things to come!