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
File size: 1,285 Bytes
4d7d3fd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | # pot14 自采数据微调配置(单活动臂 7 维 + 三路相机)
# 基于 robotwin 骨架,数据来自 scripts/convert_pot14_to_lerobot.py
model:
model_path: ../models/pretrained/lingbot-vla-4b
tokenizer_path: ../models/pretrained/Qwen2.5-VL-3B-Instruct
data:
datasets_type: vla
data_name: pot14
train_path: ../data/processed/pot14_right_arm
joints:
- arm.position: 6
- effector.position: 1
cameras:
- camera_top
- camera_wrist_left
- camera_wrist_right
num_workers: 4
pin_memory: true
prefetch_factor: 2
video_backend: torchcodec
norm_type: meanstd
norm_stats_file: assets/norm_stats/pot14_right_arm.json
train:
output_dir: "output/pot14/"
data_parallel_mode: fsdp2
enable_full_shard: false
module_fsdp_enable: true
use_compile: true
rmpad: false
rmpad_with_pos_ids: false
ulysses_parallel_size: 1
freeze_vision_encoder: false
tokenizer_max_length: 72
max_action_dim: 75
max_state_dim: 75
lr: 5.0e-5
lr_decay_style: constant
micro_batch_size: 2
gradient_accumulation_steps: 16
max_steps: 1024
ckpt_manager: dcp
save_steps: 0
save_epochs: 0
max_checkpoints_to_keep: 1
save_optimizer: false
enable_fp32: true
enable_resume: false
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