Instructions to use Haongchen/MemoryVLA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Piper
How to use Haongchen/MemoryVLA with Piper:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
| action_dim: 7 | |
| action_model_type: DiT-L | |
| consolidate_type: tome | |
| data_root_dir: /training-results/haodong/Real/memoryvla_rlds | |
| dataloader_type: stream | |
| ema_decay: 0.999 | |
| fusion_type: gate | |
| future_action_window_size: 15 | |
| group_size: 16 | |
| hf_token: HF_TOKEN | |
| image_aug: false | |
| image_aug_mode: spatial | |
| is_resume: false | |
| load_all_data_for_training: true | |
| mem_length: 256 | |
| per_token_size: 256 | |
| pretrained_checkpoint: /training-results/haodong/MemoryVLA/code/pretrained/CogACT-Large/checkpoints/CogACT-Large.pt | |
| repeated_diffusion_steps: 4 | |
| resume_epoch: 0 | |
| resume_step: 0 | |
| retain_optimizer_checkpoints: 2 | |
| retrieval_layers: 2 | |
| run_id: piper-color-sorting-frozen16-ema-real21-20260823-053817 | |
| run_id_note: null | |
| run_root_dir: /training-results/haodong/MemoryVLA/runs/piper-color-sorting | |
| save_interval: 2000 | |
| save_on_terminate: true | |
| seed: 42 | |
| trackers: | |
| - jsonl | |
| - wandb | |
| update_fused: false | |
| use_ema: true | |
| use_timestep_pe: true | |
| vla: | |
| base_vlm: prism-dinosiglip-224px+7b | |
| data_mix: custom_finetuning | |
| enable_gradient_checkpointing: true | |
| enable_mixed_precision_training: true | |
| epochs: 100 | |
| expected_world_size: 16 | |
| freeze_llm_backbone: true | |
| freeze_vision_backbone: true | |
| global_batch_size: 32 | |
| learning_rate: 2.0e-05 | |
| lr_scheduler_type: linear-warmup+cosine-decay | |
| max_grad_norm: 1.0 | |
| max_steps: 20000 | |
| per_device_batch_size: 2 | |
| reduce_in_full_precision: true | |
| shuffle_buffer_size: 64 | |
| train_strategy: fsdp-full-shard | |
| type: prism-dinosiglip-224px+oxe+diffusion | |
| unfreeze_last_llm_layer: false | |
| vla_id: prism-dinosiglip-224px+oxe+diffusion | |
| warmup_ratio: 0.03 | |
| weight_decay: 0.0 | |
| wandb_entity: spikingtransformer | |
| wandb_project: Memory World Model | |