nice work! generating good example data is the most important part of finetuning.
imo summarization is also a fairly simple task -- I wouldn't be surprised if a fine-tuned open source model (eg llama 13 / mistral 7) would get to similar performance.
imo summarization is also a fairly simple task -- I wouldn't be surprised if a fine-tuned open source model (eg llama 13 / mistral 7) would get to similar performance.
However, thanks to this article I might revisit the summarization techniques used a try a fine tuned 3.5.
It would be great to see these techniques compared to Chat GPT 4 Turbo.
The issue was always quality, length of summarized text, and control of exactly what kind of summary.