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": 2e-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" | |
| } |