Is it possible to perform validation during SFT training to monitor overfitting in Slime?
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Description
Dear Slime Team,
I am using Slime for LLM SFT. I noticed that all the SFT examples you provided lack evaluation settings. I tried various configurations myself but was unable to calculate the validation dataset loss during the training process to observe if the model is overfitting.
I also reviewed your source code. It appears that your framework does not support running validation simultaneously with training, limiting observations to performance on the training dataset only.
Is my understanding correct? If I am mistaken, could you please advise on how to enable validation during SFT?
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the SFT examples and the training-related source code referenced in the issue, then trace whether a validation dataset and loss calculation can be configured during training. Done means establishing whether simultaneous validation is supported and, if not, defining the configuration or implementation needed to monitor validation loss and overfitting.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100