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end-to-end tutorial for finetuning an LLM with the UI #9

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@burtenshaw burtenshaw changed the base branch from project/domain-eval to main November 18, 2024 13:30
@burtenshaw burtenshaw changed the title first commit end-to-end tutorial for finetuning an LLM with the UI Nov 18, 2024
@burtenshaw burtenshaw marked this pull request as ready for review November 18, 2024 13:31
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| save-total-limit | 2 | Limit for total number of saved checkpoints |
| seed | 42 | Random seed for reproducibility |

![alt text](../images/ui-finetune/autotrain_params.png)

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it seems you are still using the original dataset and not the improved/exported one?

For a real-world use case, you would want to to evaluate your model on the task that you plan to use it for. In this guide on [custom evaluation](domain-eval/README.md) we show how to do that.

```sh
lighteval accelerate \

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perhaps we can either add code that runs and do the correct installs or only redirect the user to the other blog?

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The dataset configurator lets you define a feedback task in the UI based on the dataset that you're importing. In our case, we will use the default task, and add a question for relevance. This will allow us to filter the dataset based on categories or topics.

![alt text](../images/ui-finetune/argilla_config.png)
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This image looks to me a bit misleading due to the label in comparison to what you want to achieve/configure.

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