Datasets:
Not training ready
Hello @MoreThought , first and foremost I'd like to thank you for creating and uploading this dataset! That being said though, there is some missing or malformed data here that makes it near impossible to properly train on.
- Tool interactions are flattened into bare tool messages without tool names, arguments, IDs, or assistant tool_calls
- reasoning and final answers are not separated
- many traces contain empty assistant turns, and workflow/approval artifacts.
Please consider publishing a cleaned version with properly structured tool calls and a tool schema (row specific if different for each row), thanks again!
I appreciate your feedback, ill make sure to fix it soon.
Also, sorry for the 5 day wait.
Thanks so much! No worries on the delay, we're all busy people. Thanks again
Please keep me updated in this thread whenever you get the chance to update it, or if you don't think it will be updated. Hoping to do a new tune soon and would love to incorporate your data if it gets cleaned!
I am very sorry, I am working on a new project that requires all of my hardware, I may not update it soon.
I'll try to do both at once if possible, but if not i'll update it after.
Either way I will make sure to do it this week.
Again no pressure, just keep me posted if possible. Big thanks 🙏
same here! @armand0e does finetuning on fable 5.1 data actually improve performance?
obviously depends what you're looking for. For the most part it ends up destroying what it learned in it's later RL stages unless it's a really light SFT, but even then it adds a lot of noise to the model. It does help with style and performance in specific harnesses, it does help increase intelligence (though much less likely to when using LoRA/QLoRA compared to fft), but it does make the model drift from it's previous alignment. This is why I've been learning to make my own RL environments to help re-align the models after training them on fable data.
Overall I'd say it hurts the model if you just stop after SFT, but you can get some really good results if you take the time and compute to realign the model with GRPO and DPO afterwards.
same here! @armand0e does finetuning on fable 5.1 data actually improve performance?
obviously depends what you're looking for. For the most part it ends up destroying what it learned in it's later RL stages unless it's a really light SFT, but even then it adds a lot of noise to the model. It does help with style and performance in specific harnesses, it does help increase intelligence (though much less likely to when using LoRA/QLoRA compared to fft), but it does make the model drift from it's previous alignment. This is why I've been learning to make my own RL environments to help re-align the models after training them on fable data.
Overall I'd say it hurts the model if you just stop after SFT, but you can get some really good results if you take the time and compute to realign the model with GRPO and DPO afterwards.
It's done
Thanks! I'll take a look at it in a couple days :)