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correct formatting of star sign in kto_trainer.mdx #2031

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Sep 8, 2024
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correct formatting of star sign in kto_trainer.mdx #2031

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a3fa570
correct formatting of star sign in kto_trainer.mdx
mattany Sep 6, 2024
6c183b5
fix markdown
mattany Sep 7, 2024
4e43778
one more try
mattany Sep 7, 2024
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4 changes: 2 additions & 2 deletions docs/source/kto_trainer.mdx
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Expand Up @@ -62,7 +62,7 @@ For a detailed example have a look at the `examples/scripts/kto.py` script. At a
The `beta` refers to the hyperparameter of the implicit reward, and the dataset contains the 3 entries listed above. Note that the `model` and `ref_model` need to have the same architecture (ie decoder only or encoder-decoder).

The `desirable_weight` and `undesirable_weight` refer to the weights placed on the losses for desirable/positive and undesirable/negative examples.
By default, they are both 1. However, if you have more of one or the other, then you should upweight the less common type such that the ratio of (`desirable_weight` * number of positives) to (`undesirable_weight` * number of negatives) is in the range 1:1 to 4:3.
By default, they are both 1. However, if you have more of one or the other, then you should upweight the less common type such that the ratio of (`desirable_weight` \\(\times\\) number of positives) to (`undesirable_weight` \\(\times\\) number of negatives) is in the range 1:1 to 4:3.

```py
training_args = KTOConfig(
Expand Down Expand Up @@ -99,4 +99,4 @@ To scale how much the auxiliary loss contributes to the total loss, use the hype

## KTOConfig

[[autodoc]] KTOConfig
[[autodoc]] KTOConfig
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