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update docs
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@@ -101,8 +101,11 @@ python src/lerobot/scripts/train.py \
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- **`--policy.compile_model=true`**: Enables model compilation for faster training
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- **`--policy.gradient_checkpointing=true`**: Reduces memory usage significantly during training
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- **`--policy.dtype=bfloat16`**: Use mixed precision training for efficiency
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- **`--policy.pretrained_path=pepijn223/pi0_base_fp32`**: The base π₀.₅ model to finetune, options are: `pepijn223/pi0_base_fp32`, `pepijn223/pi0_libero_fp32`, `pepijn223/pi0_droid_fp32`
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- **`--batch_size=32`**: Batch size for training, adapt this based on your GPU memory
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- **`--policy.pretrained_path=pepijn223/pi0_base_fp32`**: The base π₀ model you want to finetune, options are:
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- \*\* [pepijn223/pi0_base_fp32](https://huggingface.co/pepijn223/pi0_base_fp32)
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- \*\* [pepijn223/pi0_libero_fp32](https://huggingface.co/pepijn223/pi0_libero_fp32) (specifically trained on the Libero dataset)
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- \*\* [pepijn223/pi0_droid_fp32](https://huggingface.co/pepijn223/pi0_droid_fp32) (specifically trained on the Droid dataset)
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## License
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@@ -104,8 +104,11 @@ python src/lerobot/scripts/train.py \
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- **`--policy.compile_model=true`**: Enables model compilation for faster training
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- **`--policy.gradient_checkpointing=true`**: Reduces memory usage significantly during training
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- **`--policy.dtype=bfloat16`**: Use mixed precision training for efficiency
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- **`--policy.pretrained_path=pepijn223/pi05_base_fp32`**: The base π₀.₅ model to finetune, options are: `pepijn223/pi05_base_fp32`, `pepijn223/pi05_libero_fp32`, `pepijn223/pi05_droid_fp32`
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- **`--batch_size=32`**: Batch size for training, adapt this based on your GPU memory
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- **`--policy.pretrained_path=pepijn223/pi05_base_fp32`**: The base π₀.₅ model you want to finetune, options are:
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- \*\* [pepijn223/pi05_base_fp32](https://huggingface.co/pepijn223/pi05_base_fp32)
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- \*\* [pepijn223/pi05_libero_fp32](https://huggingface.co/pepijn223/pi05_libero_fp32) (specifically trained on the Libero dataset)
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- \*\* [pepijn223/pi05_droid_fp32](https://huggingface.co/pepijn223/pi05_droid_fp32) (specifically trained on the Droid dataset)
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## Performance Results
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