InstructVLA
Collection
Paper, Data and Checkpoints for ``InstructVLA: Vision-Language-Action Instruction Tuning from Understanding to Manipulation''
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14 items
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Updated
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1
checkpoints: the model in .pt
format
eval: the evaluation results with 3 random seeds
dataset_statistics.json: the normalization statistics for the dataset
#!/bin/bash
CKPT_LIST=(
"path/to/checkpoints/step-018000-epoch-87-loss=0.0409.pt"
)
# Loop over the checkpoint list and GPUs
for i in "${!CKPT_LIST[@]}"; do
GPU_ID=$((i % 8)) # Cycle through GPUs 0-7
CHECKPOINT="${CKPT_LIST[$i]}"
# Run the evaluation script for each checkpoint and GPU
CUDA_VISIBLE_DEVICES=$GPU_ID python deploy/libero/run_libero_eval.py \
--model_family instruct_vla \
--pretrained_checkpoint "$CHECKPOINT" \
--task_suite_name libero_spatial \
--local_log_dir Libero/release_ensemble \
--use_length -1 \
--center_crop True &
# --use_length == -1 : execute the ensembled action
# --use_length >= 1 : execute action_chunk[0:use_length]
# For this checkpoint, you should use action ensemble.
sleep 5
done
# Wait for all background jobs to finish
wait
Base model
nvidia/Eagle2-2B