Buckets:
| # Neuron TRL Trainers | |
| [TRL](https://huggingface.co/docs/trl/en/index)-compatible trainers for AWS Trainium accelerators. | |
| ## NeuronSFTTrainer | |
| ### NeuronSFTConfig[[optimum.neuron.NeuronSFTConfig]] | |
| #### optimum.neuron.NeuronSFTConfig[[optimum.neuron.NeuronSFTConfig]] | |
| [Source](https://github.com/huggingface/optimum-neuron/blob/vr_1097/optimum/neuron/trainers/sft_config.py#L34) | |
| Configuration class for Neuron-optimized SFT training. | |
| Inherits from both NeuronTrainingArguments (for Trainium-specific settings) and | |
| trl's SFTConfig (for SFT-specific settings). | |
| Key Neuron-specific behavior: | |
| - padding_free is always set to False to avoid recompilation on Trainium devices | |
| - All other SFT parameters from trl 0.24.0+ are supported | |
| ### NeuronSFTTrainer[[optimum.neuron.NeuronSFTTrainer]] | |
| #### optimum.neuron.NeuronSFTTrainer[[optimum.neuron.NeuronSFTTrainer]] | |
| [Source](https://github.com/huggingface/optimum-neuron/blob/vr_1097/optimum/neuron/trainers/sft_trainer.py#L132) | |
| `SFTTrainer` adapted for Neuron (Trainium) devices. | |
| compute_lossoptimum.neuron.NeuronSFTTrainer.compute_losshttps://github.com/huggingface/optimum-neuron/blob/vr_1097/optimum/neuron/trainers/sft_trainer.py#L403[{"name": "model", "val": ""}, {"name": "inputs", "val": ""}, {"name": "return_outputs", "val": " = False"}, {"name": "num_items_in_batch", "val": " = None"}] | |
| Compute training loss for Neuron-optimized training. | |
| #### log[[optimum.neuron.NeuronSFTTrainer.log]] | |
| [Source](https://github.com/huggingface/optimum-neuron/blob/vr_1097/optimum/neuron/trainers/sft_trainer.py#L387) | |
| Override SFTTrainer's log method to use NeuronTrainer's implementation. | |
| SFTTrainer has custom metrics tracking that we don't use for Neuron training. | |
| #### training_step[[optimum.neuron.NeuronSFTTrainer.training_step]] | |
| [Source](https://github.com/huggingface/optimum-neuron/blob/vr_1097/optimum/neuron/trainers/sft_trainer.py#L413) | |
| Perform a training step for Neuron-optimized training. |
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