Comments (1)
I implemented reward modelling but maybe we should just use the trl
library implementations for RLHF/reward etc.
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Related Issues (20)
- [P1] I am bit confused how to reproduce Table 2 (all baselines + main method) HOT 3
- [P0] Adding DPO Support HOT 8
- [P0] Why is the number of trainable parameters for prefix-tuning is 0.11% HOT 7
- [P1] TypeError: Object of type type is not JSON serializable HOT 6
- [P2] Pyreft tensorboard integration
- [P1] Location of code for "LM training and serving with ReFT" HOT 2
- [P0] compreft.ipynb error = KeyError: 'subspaces' HOT 4
- [P1] Confirmation of alpaca_eval version HOT 4
- [P1] Intuitive-wise, should we keep the projection orthogonal during training? HOT 2
- [P1] catastrophic forgetting HOT 1
- [P1] RuntimeError: cutlassF: no kernel found to launch! HOT 4
- Getting issue while loading Phi3 in reft_model HOT 6
- [P1] How did you create the validation set for Commonsense reasoning hyperparameter tuning? HOT 5
- [P0] Additional intervention arguments are not saved correctly, e.g. `add_bias`
- [P1] Getting error as IntervenableModel.train() takes 1 positional argument but 2 were given HOT 2
- [P1] Convert reft model to hf model HOT 1
- [P0] Make `make_last_position_supervised_data_module` parallelizable to speed up processing! HOT 2
- [P1] Loading REFT fro RoBERTa Models HOT 3
- [P1] TypeError: train() takes 1 positional argument but 2 were given HOT 1
- [P1] Loreft example gsm8k train gives: RuntimeError: output with shape [64, 1, 7] doesn't match the broadcast shape [64, 0, 7] HOT 3
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