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Inference and deployment

Evaluation

Open-Loop Eval

export QWEN25_PATH=Qwen/Qwen2.5-VL-3B-Instruct
python scripts/open_loop_eval.py --model_path path_to_posttraining_ckpt --data_path path_to_validation_data --use_length 50
# If `--data_path` is omitted, the script defaults to the training dataset specified in the YAML config (`data.train_path`).

Note:
For inference, the model path (path_to_posttraining_ckpt, located in train.output_dir/checkpoints/*/hf_ckpt) must include:

weights in .safetensors format

config.json

lingbotvla_cli.yaml

Robotwin

export QWEN25_PATH=path_to_Qwen2.5-VL-3B-Instruct
python -m deploy.lingbot_vla_policy \
 --model_path path_to_posttraining_ckpt \
 --use_compile \
 --use_length 50 \
 --port port

Real-Robot Deployment

export QWEN25_PATH=path_to_Qwen2.5-VL-3B-Instruct
python -m deploy.lingbot_vla_policy \
 --model_path path_to_posttraining_ckpt \
 --use_compile \
 --use_length 25
# You can set --num_denoising_step to 5 if you want to speed up the evaluation.

Evaluation