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: 906 Bytes
76d1db9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | #!/bin/bash
# Clean single-run launcher for ssh/nohup use.
# bash eval/_launch.sh <GPU> <task> <arm> [weights_or_-] [limit_or_-] [nproc]
set -euo pipefail
GPU=$1; TASK=$2; ARM=$3; WEIGHTS=${4:-'-'}; LIMIT=${5:-'-'}; NPROC=${6:-1}
source /home/tl356/miniconda3/etc/profile.d/conda.sh
conda activate llada
cd /home/tl356/LLaDA/trajmc_main
export CUDA_VISIBLE_DEVICES=$GPU
export HF_DATASETS_OFFLINE=1 HF_HUB_OFFLINE=1 HF_ALLOW_CODE_EVAL=1
export HF_DATASETS_TRUST_REMOTE_CODE=true TOKENIZERS_PARALLELISM=false
export PYTORCH_ALLOC_CONF=expandable_segments:True
ARGS=(--task "$TASK" --arm "$ARM" --num_processes "$NPROC")
[ "$WEIGHTS" != "-" ] && ARGS+=(--weights "$WEIGHTS")
[ "$LIMIT" != "-" ] && ARGS+=(--limit "$LIMIT")
echo "launch host=$(hostname) gpu=$GPU task=$TASK arm=$ARM weights=$WEIGHTS limit=$LIMIT start=$(date)"
python eval/run_lmeval.py "${ARGS[@]}"
echo "done task=$TASK arm=$ARM $(date)"
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