diff --git a/speedrun.sh b/speedrun.sh
index 32c8870..6aaf92b 100644
--- a/speedrun.sh
+++ b/speedrun.sh
@@ -15,6 +15,9 @@ export OMP_NUM_THREADS=1
 export NANOCHAT_BASE_DIR="$HOME/.cache/nanochat"
 mkdir -p $NANOCHAT_BASE_DIR
 
+# Number of processes per node for distributed training
+NPROC_PER_NODE=4
+
 # -----------------------------------------------------------------------------
 # Python venv setup with uv
 
@@ -23,7 +26,7 @@ command -v uv &> /dev/null || curl -LsSf https://astral.sh/uv/install.sh | sh
 # create a .venv local virtual environment (if it doesn't exist)
 [ -d ".venv" ] || uv venv
 # install the repo dependencies
-uv sync --extra gpu
+uv sync
 # activate venv so that `python` uses the project's venv instead of system python
 source .venv/bin/activate
 
@@ -73,6 +76,15 @@ python -m scripts.tok_eval
 # -----------------------------------------------------------------------------
 # Base model (pretraining)
 
+# Download the eval_bundle from s3 to evaluate CORE metric during training (~162MB)
+EVAL_BUNDLE_URL=https://karpathy-public.s3.us-west-2.amazonaws.com/eval_bundle.zip
+if [ ! -d "$NANOCHAT_BASE_DIR/eval_bundle" ]; then
+    curl -L -o eval_bundle.zip $EVAL_BUNDLE_URL
+    unzip -q eval_bundle.zip
+    rm eval_bundle.zip
+    mv eval_bundle $NANOCHAT_BASE_DIR
+fi
+
 # The d20 model is 561M parameters.
 # Chinchilla says #tokens = 20X #params, so we need 561e6 * 20 = 11.2B tokens.
 # Assume our tokenizer is 4.8 chars/token, this is 11.2B * 4.8 ~= 54B chars.
@@ -83,29 +95,25 @@ echo "Waiting for dataset download to complete..."
 wait $DATASET_DOWNLOAD_PID
 
 # pretrain the d20 model
-torchrun --standalone --nproc_per_node=8 -m scripts.base_train -- --depth=20 --run=$WANDB_RUN
+torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.base_train -- --depth=20 --run=$WANDB_RUN
 # evaluate the model on a larger chunk of train/val data and draw some samples
-torchrun --standalone --nproc_per_node=8 -m scripts.base_loss
+torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.base_loss
 # evaluate the model on CORE tasks
-torchrun --standalone --nproc_per_node=8 -m scripts.base_eval
+torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.base_eval
 
 # -----------------------------------------------------------------------------
 # Midtraining (teach the model conversation special tokens, tool use, multiple choice)
 
-# download 2.3MB of synthetic identity conversations to impart a personality to nanochat
-# see dev/gen_sft_data.py for details on how this data was prepared and to get a sense of how you can easily tune it
-curl -L -o $NANOCHAT_BASE_DIR/identity_conversations.jsonl https://karpathy-public.s3.us-west-2.amazonaws.com/identity_conversations.jsonl
-
 # run midtraining and eval the model
-torchrun --standalone --nproc_per_node=8 -m scripts.mid_train -- --run=$WANDB_RUN
-torchrun --standalone --nproc_per_node=8 -m scripts.chat_eval -- -i mid
+torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.mid_train -- --run=$WANDB_RUN
+torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.chat_eval -- -i mid
 
 # -----------------------------------------------------------------------------
 # Supervised Finetuning (domain adaptation to each sequence all by itself per row)
 
 # train sft and re-eval right away (should see a small bump)
-torchrun --standalone --nproc_per_node=8 -m scripts.chat_sft -- --run=$WANDB_RUN
-torchrun --standalone --nproc_per_node=8 -m scripts.chat_eval -- -i sft
+torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.chat_sft -- --run=$WANDB_RUN
+torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.chat_eval -- -i sft
 
 # chat with the model over CLI! Leave out the -p to chat interactively
 # python -m scripts.chat_cli -p "Why is the sky blue?"
@@ -118,9 +126,9 @@ torchrun --standalone --nproc_per_node=8 -m scripts.chat_eval -- -i sft
 # (optional)
 
 # run reinforcement learning
-# torchrun --standalone --nproc_per_node=8 -m scripts.chat_rl -- --run=$WANDB_RUN
+# torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.chat_rl -- --run=$WANDB_RUN
 # eval the RL model only on GSM8K
-# torchrun --standalone --nproc_per_node=8 -m scripts.chat_eval -- -i rl -a GSM8K
+# torchrun --standalone --nproc_per_node=$NPROC_PER_NODE -m scripts.chat_eval -- -i rl -a GSM8K
 
 # -----------------------------------------------------------------------------
 # Generate the full report by putting together all the sections