SFT loss mask
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- Python
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Description
I'm using slime for GLM-4.5-AIR SFT training.
Since GLM-4.5 doesn’t use a dedicated end-of-turn token, the next <|user|> or <|observation|> tag should serve as the end marker for the current assistant response.
I noticed that in the function gen_multi_turn_loss_mask_qwen, the entire user segment’s loss_mask is set to 0.
Would this cause the model to never learn the loss associated with generating the end-of-turn boundary (i.e., the transition to the next <|user|> or <|observation|>) during training?
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 inspecting the gen_multi_turn_loss_mask_qwen function and the SFT loss-mask handling for GLM-4.5-AIR. Trace how the next <|user|> or <|observation|> boundary is represented, then determine whether the current mask includes the assistant-to-boundary transition. Done means the expected boundary-loss behavior is established and the issue has a clearly defined follow-up change or explanation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100