Instructions to use coisini9293/lingbot_pot14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use coisini9293/lingbot_pot14 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("coisini9293/lingbot_pot14", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add configs/vla_pot14.yaml
Browse files- configs/vla_pot14.yaml +50 -0
configs/vla_pot14.yaml
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# pot14 自采数据微调配置(单活动臂 7 维 + 三路相机)
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# 基于 robotwin 骨架,数据来自 scripts/convert_pot14_to_lerobot.py
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model:
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model_path: ../models/pretrained/lingbot-vla-4b
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tokenizer_path: ../models/pretrained/Qwen2.5-VL-3B-Instruct
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data:
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datasets_type: vla
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data_name: pot14
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train_path: ../data/processed/pot14_right_arm
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joints:
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- arm.position: 6
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- effector.position: 1
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cameras:
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- camera_top
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- camera_wrist_left
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- camera_wrist_right
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num_workers: 4
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pin_memory: true
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prefetch_factor: 2
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video_backend: torchcodec
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norm_type: meanstd
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norm_stats_file: assets/norm_stats/pot14_right_arm.json
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train:
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output_dir: "output/pot14/"
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data_parallel_mode: fsdp2
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enable_full_shard: false
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module_fsdp_enable: true
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use_compile: true
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rmpad: false
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rmpad_with_pos_ids: false
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ulysses_parallel_size: 1
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freeze_vision_encoder: false
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tokenizer_max_length: 72
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max_action_dim: 75
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max_state_dim: 75
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lr: 5.0e-5
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lr_decay_style: constant
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micro_batch_size: 2
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gradient_accumulation_steps: 16
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max_steps: 1024
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ckpt_manager: dcp
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save_steps: 0
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save_epochs: 0
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max_checkpoints_to_keep: 1
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save_optimizer: false
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enable_fp32: true
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enable_resume: false
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