Magnum_ShareGPT

## Credits - [anthracite](https://huggingface.co/anthracite-org) --- - [anthracite-org/Stheno-Data-Filtered](https://huggingface.co/datasets/anthracite-org/Stheno-Data-Filtered) - [anthracite-org/kalo-opus-instruct-22k-no-refusal](https://huggingface.co/datasets/anthracite-org/kalo-opus-instruct-22k-no-refusal) - [lodrick-the-lafted/NopmWritingStruct](https://huggingface.co/datasets/lodrick-the-lafted/NopmWritingStruct) - [NewEden/Gryphe-3.5-16k-Subset](https://huggingface.co/datasets/NewEden/Gryphe-3.5-16k-Subset) - [Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned](https://huggingface.co/datasets/Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned) - [Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned](https://huggingface.co/datasets/Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned) --- # This is the Magnum dataset converted into ShareGPT, sorted by token count We have so many benchmarks, but these are the two I think are the most important : **IFEVAL** and the [UGI leaderboard](https://huggingface.co/spaces/DontPlanToEnd/UGI-Leaderboard). I think that one of the best way to thoroughly compare different base models, is to finetune them using the exact same hyper parameters, and the exact same dataset. The Magnum dataset is both well established and open, so it's a good candidate for the above.