Instructions to use Duke-CEI-SVD/traj-mc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Duke-CEI-SVD/traj-mc with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Duke-CEI-SVD/traj-mc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,193 Bytes
72e3ce3 | 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 | #!/bin/bash
#SBATCH --job-name=tm_diffgate
#SBATCH --partition=athena-genai,athena,athena-mini,athena-small
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=2
#SBATCH --mem=8G
#SBATCH --time=00:10:00
#SBATCH --output=/home/tl356/LLaDA/logs/%j_tm_diffgate.out
#SBATCH --error=/home/tl356/LLaDA/logs/%j_tm_diffgate.err
#
# HARD GATE between compression and eval. Runs summary_diff.py on the two
# all-linear r=0.7 summaries; exits non-zero on any mismatch beyond noise fields.
# Downstream BASE/OURS eval jobs are submitted with --dependency=afterok:<this>,
# so a FAIL here auto-cancels them (Slurm never runs eval on a failed gate).
# sbatch --dependency=afterok:<Jbase_all>:<Jours_all> jobs/diff_gate.sbatch weights/base/all_r07 weights/ours/all_r07
set -euo pipefail
source /home/tl356/miniconda3/etc/profile.d/conda.sh
conda activate llada
cd /home/tl356/LLaDA/trajmc_main
BASE=${1:-weights/base/all_r07}
OURS=${2:-weights/ours/all_r07}
echo "node=$(hostname) diff_gate base=$BASE ours=$OURS start=$(date)"
python analysis/summary_diff.py --base "$BASE" --ours "$OURS"
rc=$?
echo "diff_gate exit=$rc (0=PASS -> eval proceeds; nonzero=FAIL -> eval cancelled) $(date)"
exit $rc
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