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---
datasets:
- IPEC-COMMUNITY/libero_spatial_no_noops_1.0.0_lerobot
base_model:
- nvidia/Eagle2-2B
tags:
- vision-language-model
- manipulation
- robotics
pipeline_tag: robotics
---
# Model Card for InstructVLA LIBERO-Spatial
- checkpoints: the model in `.pt` format
- eval: the evaluation results with 3 random seeds
- dataset_statistics.json: the normalization statistics for the dataset
## Evaluation:
```bash
#!/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
``` |