diff --git a/README.md b/README.md
index 2f7d1bb0b..c38c9ad10 100644
--- a/README.md
+++ b/README.md
@@ -31,24 +31,19 @@ torchrun --nproc-per-node=8 scripts/train.py configs/c4-tiny.yaml \
 
 #### Running on Cirrascale via [beaker-gantry](https://github.com/allenai/beaker-gantry)
 
+Check the script at [`scripts/olmo-small-ablation-on-gantry.sh`](scripts/olmo-small-ablation-on-gantry.sh) for an example on how to run a training job on Cirrascale.
+
+After installing `beaker-gantry`, you can launch a training job like this:
+
 ```bash
-gantry run \
-  --workspace ai2/llm-testing \
-  --task-name "OLMo-tiny-c4" \
-  --priority "high" \
-  --beaker-image olmo-torch2-gantry \
-  --cluster ai2/general-cirrascale-a100-80g-ib \
-  --gpus 8 \
-  --nfs \
-  --env-secret WANDB_API_KEY=WANDB_API_KEY \
-  --env LOG_FILTER_TYPE=local_rank0_only \
-  --env OMP_NUM_THREADS=8 \
-  --shared-memory 10GiB \
-  --venv base \
-  --yes \
-  -- /bin/bash -c 'torchrun --nproc-per-node 8 scripts/train.py configs/c4-tiny.yaml --save_folder=/net/nfs.cirrascale/allennlp/llm-checkpoints/tmp --run_name=c4-tiny-test-run'
+CONFIG_PATH=configs/choose_a_config.yml \
+LOAD_PATH=/optional/path/to/checkpoint/ \
+  bash scripts/olmo-small-ablation-on-gantry.sh
 ```
 
+if `CONFIG_PATH` is not specified, the default config is `configs/olmo-small-ablation.yaml`;
+if `LOAD_PATH` is not specified, the training will start from scratch.
+
 #### Running on LUMI via Slurm
 
 First read our [LUMI](./LUMI.md) documentation, but submitting a new job essentially just boils down to running this:
diff --git a/scripts/olmo-small-ablation-on-gantry.sh b/scripts/olmo-small-ablation-on-gantry.sh
new file mode 100644
index 000000000..4e660f0fe
--- /dev/null
+++ b/scripts/olmo-small-ablation-on-gantry.sh
@@ -0,0 +1,59 @@
+#!/bin/bash
+
+set -ex
+
+# check if LOAD_PATH is provided as an environment variable; if so, create an argument
+# to pass to the training script
+if [ -z ${LOAD_PATH+x} ]; then
+  LOAD_PATH_ARG=""
+else
+  LOAD_PATH_ARG="--load_path=${LOAD_PATH}"
+fi
+
+
+# check if CONFIG PATH is provided as an environment variable;
+# if so, use that instead of olmo-small-ablation.yaml
+if [ -z ${CONFIG_PATH+x} ]; then
+  export CONFIG_PATH=configs/olmo-small-ablation.yaml
+else
+  export CONFIG_PATH="${CONFIG_PATH}"
+fi
+
+# get run name, we will use this as task name in gantry
+RUN_NAME=$(cat $CONFIG_PATH | grep -ohP "^run_name\:\w*(.+)$" | sed 's/run_name:\s*//')
+
+# get a hash of the load path and config path; take the first 8 characters
+RUN_HASH=$(echo "${LOAD_PATH_ARG}-${CONFIG_PATH}" | md5sum | cut -c 1-8)
+
+# compose the two
+FULL_RUN_NAME="${RUN_NAME}-${RUN_HASH}"
+
+# check if there is an env var called 'WANDB_API_KEY' and if so, create a flag
+# to pass to gantry
+if [ -z ${WANDB_API_KEY+x} ]; then
+  WANDB_API_KEY_ARG="--env-secret WANDB_API_KEY=WANDB_API_KEY"
+else
+  WANDB_API_KEY_ARG="--env WANDB_API_KEY=${WANDB_API_KEY}"
+fi
+
+NUM_NODES=4
+
+gantry run \
+  --workspace ai2/llm-testing \
+  --task-name "${FULL_RUN_NAME}" \
+  --description "${FULL_RUN_NAME}" \
+  --priority "normal" \
+  --beaker-image olmo-torch2-gantry \
+  --cluster ai2/general-cirrascale-a100-80g-ib \
+  --gpus 8 \
+  --replicas ${NUM_NODES} \
+  --leader-selection  \
+  --host-networking \
+  --nfs \
+  ${WANDB_API_KEY_ARG} \
+  --env LOG_FILTER_TYPE=local_rank0_only \
+  --env OMP_NUM_THREADS=8 \
+  --shared-memory 10GiB \
+  --venv base \
+  --yes \
+  -- /bin/bash -c "torchrun --nnodes ${NUM_NODES}:${NUM_NODES} --nproc-per-node 8 --rdzv_id=101 --rdzv_backend=c10d --rdzv_endpoint=\$BEAKER_LEADER_REPLICA_HOSTNAME:29400 scripts/train.py ${CONFIG_PATH} --run_name=${FULL_RUN_NAME} ${LOAD_PATH_ARG} --device_train_microbatch_size=8 ${@}"