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Merge remote-tracking branch 'upstream/chore/bump_transformers_v5' into feature/add-multitask-dit

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#	pyproject.toml
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Bryson Jones
2026-02-24 09:10:12 -08:00
parent 428ea89ff2 b9cb947bd2
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@@ -52,7 +52,7 @@ This approach can transform **any existing VLM** into a VLA by training it to pr
You have two options for the FAST tokenizer:
1. **Use the pre-trained tokenizer**: The `physical-intelligence/fast` tokenizer was trained on 1M+ real robot action sequences and works as a general-purpose tokenizer.
1. **Use the pre-trained tokenizer**: The `lerobot/fast-action-tokenizer` tokenizer was trained on 1M+ real robot action sequences and works as a general-purpose tokenizer.
2. **Train your own tokenizer**: For maximum performance on your specific dataset, you can finetune the tokenizer on your own data.
@@ -114,15 +114,15 @@ lerobot-train \
### Key Training Parameters
| Parameter | Description | Default |
| -------------------------------------- | -------------------------------------------------- | ---------------------------- |
| `--policy.gradient_checkpointing=true` | Reduces memory usage significantly during training | `false` |
| `--policy.dtype=bfloat16` | Use mixed precision training for efficiency | `float32` |
| `--policy.chunk_size` | Number of action steps to predict (action horizon) | `50` |
| `--policy.n_action_steps` | Number of action steps to execute | `50` |
| `--policy.max_action_tokens` | Maximum number of FAST tokens per action chunk | `256` |
| `--policy.action_tokenizer_name` | FAST tokenizer to use | `physical-intelligence/fast` |
| `--policy.compile_model=true` | Enable torch.compile for faster training | `false` |
| Parameter | Description | Default |
| -------------------------------------- | -------------------------------------------------- | ------------------------------- |
| `--policy.gradient_checkpointing=true` | Reduces memory usage significantly during training | `false` |
| `--policy.dtype=bfloat16` | Use mixed precision training for efficiency | `float32` |
| `--policy.chunk_size` | Number of action steps to predict (action horizon) | `50` |
| `--policy.n_action_steps` | Number of action steps to execute | `50` |
| `--policy.max_action_tokens` | Maximum number of FAST tokens per action chunk | `256` |
| `--policy.action_tokenizer_name` | FAST tokenizer to use | `lerobot/fast-action-tokenizer` |
| `--policy.compile_model=true` | Enable torch.compile for faster training | `false` |
## Inference