Comments (2)
I think that BLOOM might be a good candidate for that. After UL2 training you might want to try instruction tuning like BLOOMZ, FLAN or T0. But a good workaround could be (i) include instruction tuning samples (xp3mt, p3 etc) in the "prefix LM" objective function, (ii) include other objective function like span_corruption
and continue UL2 training.
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Thanks!
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Related Issues (20)
- Were the checkpoints selected based on the held-out performance or seen task performance? HOT 2
- How to convert megatron-deepspeed checkpoints to huggingface checkpoints ? HOT 4
- How to fineutne mT0 with specific down-stream data? HOT 3
- Questions on creating instruction data HOT 1
- Use Petals without sharing GPU HOT 11
- Controlled generation HOT 1
- how to convert model weights(e.g., bigscience/bloomz-560m-optimizer-states) to Hugging Face model.bin file? HOT 2
- how to repreduce bloomz-* HOT 6
- mT0-xxl finetuning HOT 6
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- Export mt0-xxl-mt to ONNX fails HOT 2
- Dose mt0&bloomz trained on dev, devtest datasets of Flores-200? HOT 2
- Parsing the xP3 dataset HOT 1
- Getting machine-translated prompts of xP3mt HOT 4
- P3megds URL is not available HOT 1
- Some datasets are not in xP3all HOT 4
- What is the training config? HOT 3
- I can't find the model weights that you used for experimentation. HOT 1
- Quesiton about MTFDataset HOT 1
- Why does the number of templates differ between languages? HOT 4
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