Comments (4)
How many different responses do you expect? I could see that there might not be enough variety in the responses in a large batch and maybe training on smaller batches would help.
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@simonlevine what architecture are you using? Can you share with us the logs of your training?
Also I second @lvwerra 's comment about the batches
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Hi, thank you for the advice, I'll tune the batch size (and forward batch size). Without going into too much detail, architecture is akin to GPT (2), but trained to generate biological sequences. The prompt is about 100 characters and the response is a fixed length of about 30. I can share training logs when I have more consistent results. Thanks agaIn!
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Ok, I am closing this for now - feel free to reopen if there are any news :)
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Related Issues (20)
- PPOTrainer Appears to incorrectly handle `pad_token_id` HOT 1
- Prompt format clarification for ORPO
- TrlParser not working
- excessive RAM usage with quantized base model and LORA with SFTTrainer HOT 3
- ValueError: The model is offloaded on CPU or disk - CPU & disk offloading is not supported for ValueHead models. HOT 1
- Warning message in `DataCollatorForCompletionOnlyLM` is misleading when only `response_template` is missing in the batch
- `RichProgressCallback` would break model evaluation and prediction HOT 1
- Multi-GPU Training with DPO Full Parameter Stucks HOT 1
- UserWarning: Could not find a config file
- concatenated_forward when self.ref_model is not provided HOT 1
- which model should i choose if i wanna try DPO algorithm?
- PPOTrainer behavior with `device_map = "auto"` HOT 1
- None ref_model in ppo train HOT 1
- FSDP/ZeRO3 Support for QLoRA in DPO?
- Use `SFTTrainer` for completion-only model without `DataCollatorForCompletionOnlyLM`
- `disable_dropout` not used in KTOTrainer
- [question] how to apply model parallism to solve cuda memory error HOT 6
- misleading warning message HOT 1
- How should I set the SFT label? HOT 1
- dpo cli command error HOT 5
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