File size: 2,592 Bytes
319eb16 | 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 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | # ReWIND
uv run python robometer/evals/run_baseline_eval.py \
reward_model=rewind \
model_path=rewardfm/rewind-scale-rfm1M-32layers-8frame-20260118-180522 \
custom_eval.eval_types=[reward_alignment] \
custom_eval.reward_alignment=[rbm-1m-id,rbm-1m-ood] \
custom_eval.use_frame_steps=true \
custom_eval.subsample_n_frames=5 \
custom_eval.reward_alignment_max_trajectories=30 \
max_frames=8 \
model_config.batch_size=64
# Robo-Dopamine (run with venv Python so vLLM is found; do not use uv run)
.venv-robodopamine/bin/python robometer/evals/run_baseline_eval.py \
reward_model=robodopamine \
model_path=tanhuajie2001/Robo-Dopamine-GRM-3B \
custom_eval.eval_types=[reward_alignment] \
custom_eval.reward_alignment=[rbm-1m-id,rbm-1m-ood] \
custom_eval.use_frame_steps=false \
custom_eval.reward_alignment_max_trajectories=30 \
max_frames=64 \
model_config.batch_size=1
# VLAC
uv run --extra vlac --python .venv-vlac/bin/python robometer/evals/run_baseline_eval.py \
reward_model=vlac \
model_path=InternRobotics/VLAC \
custom_eval.eval_types=[reward_alignment] \
custom_eval.reward_alignment=[rbm-1m-id,rbm-1m-ood] \
custom_eval.use_frame_steps=false \
custom_eval.reward_alignment_max_trajectories=30 \
custom_eval.pad_frames=false \
max_frames=64
# RoboReward-4B
uv run python robometer/evals/run_baseline_eval.py \
reward_model=roboreward \
model_path=teetone/RoboReward-4B \
custom_eval.eval_types=[reward_alignment] \
custom_eval.reward_alignment=[rbm-1m-id,rbm-1m-ood] \
custom_eval.use_frame_steps=true \
custom_eval.subsample_n_frames=5 \
custom_eval.reward_alignment_max_trajectories=30 \
max_frames=64 \
model_config.batch_size=32
# Robometer-4B
uv run python robometer/evals/run_baseline_eval.py \
reward_model=rbm \
model_path=aliangdw/Robometer-4B \
custom_eval.eval_types=[reward_alignment] \
custom_eval.reward_alignment=[rbm-1m-id,rbm-1m-ood] \
custom_eval.use_frame_steps=true \
custom_eval.subsample_n_frames=5 \
custom_eval.reward_alignment_max_trajectories=30 \
max_frames=4 \
model_config.batch_size=32
# Robometer-4B Libero Ablation
uv run python robometer/evals/run_baseline_eval.py \
reward_model=rbm \
model_path=aliangdw/Robometer-4B-LIBERO \
custom_eval.eval_types=[reward_alignment] \
custom_eval.reward_alignment=[libero_pi0] \
custom_eval.use_frame_steps=false \
custom_eval.reward_alignment_max_trajectories=100 \
max_frames=4 \
model_config.batch_size=32 |