diff --git a/midtrain_sft_submit.sh b/midtrain_sft_submit.sh
index 49dabb3..d596ffd 100644
--- a/midtrain_sft_submit.sh
+++ b/midtrain_sft_submit.sh
@@ -14,7 +14,7 @@
 set -x  # Enable debug output
 
 export DATA_NAME=nemotron # nemotron # smoltalk
-export BASE_NAME=climbmix_1_9_d20_1node_matrixlr0.02_2309733 #climbmix_9_1_d20_1node_matrixlr0.02_2308728 #climbmix_8_2_d20_1node_matrixlr0.02_2309730 #climbmix_5_5_d20_1node_matrixlr0.02_2309731 #climbmix_2_8_d20_1node_matrixlr0.02_2309732 #climbmix_1_9_d20_1node_matrixlr0.02_2309733 #climbmix_d20_1node_matrixlr0.02_2298334 # fineweb_d20_1node # climbmix_d20_1node_matrixlr0.02_2298334 # nemotron-cc-hq_d20_1node_matrixlr0.02_2298371 # smollm_d20_1node_matrixlr0.02_2298373
+export BASE_NAME=climbmix_8_2_d20_1node_matrixlr0.02_2314630 #climbmix_9_1_d20_1node_matrixlr0.02_2308728 #climbmix_8_2_d20_1node_matrixlr0.02_2309730 #climbmix_5_5_d20_1node_matrixlr0.02_2309731 #climbmix_2_8_d20_1node_matrixlr0.02_2309732 #climbmix_1_9_d20_1node_matrixlr0.02_2309733 #climbmix_d20_1node_matrixlr0.02_2298334 # fineweb_d20_1node # climbmix_d20_1node_matrixlr0.02_2298334 # nemotron-cc-hq_d20_1node_matrixlr0.02_2298371 # smollm_d20_1node_matrixlr0.02_2298373
 
 # Default intermediate artifacts directory is in ~/.cache/nanochat
 export OMP_NUM_THREADS=1
diff --git a/pretrain_submit.sh b/pretrain_submit.sh
index bb9a902..ab8defa 100644
--- a/pretrain_submit.sh
+++ b/pretrain_submit.sh
@@ -22,7 +22,7 @@
 
 set -x  # Enable debug output
 
-DATA_NAME=climbmix_1_9
+DATA_NAME=climbmix_small
 export DATA_DIR=/lustre/fsw/portfolios/nvr/users/sdiao/nanochat/data/$DATA_NAME
 export MATRIX_LR=0.02
 
diff --git a/speedrun_nvidia.sh b/speedrun_nvidia.sh
new file mode 100644
index 0000000..811ea69
--- /dev/null
+++ b/speedrun_nvidia.sh
@@ -0,0 +1,141 @@
+#!/bin/bash
+
+# This script is the "Best ChatGPT clone that $100 can buy",
+# It is designed to run in ~4 hours on 8XH100 node at $3/GPU/hour.
+
+# 1) Example launch (simplest):
+# bash speedrun.sh
+# 2) Example launch in a screen session (because the run takes ~4 hours):
+# screen -L -Logfile speedrun.log -S speedrun bash speedrun.sh
+# 3) Example launch with wandb logging, but see below for setting up wandb first:
+# WANDB_RUN=speedrun screen -L -Logfile speedrun.log -S speedrun bash speedrun.sh
+set -x
+
+DATA_NAME=climbmix_small
+DATA_DIR=/lustre/fsw/portfolios/nvr/users/sdiao/nanochat/data/$DATA_NAME
+POST_TRAINING_DATA_NAME=nemotron
+
+# Default intermediate artifacts directory is in ~/.cache/nanochat
+export OMP_NUM_THREADS=1
+export NANOCHAT_BASE_DIR="/lustre/fsw/portfolios/nvr/users/sdiao/nanochat/.cache"
+mkdir -p $NANOCHAT_BASE_DIR
+
+# -----------------------------------------------------------------------------
+# Python venv setup with uv
+
+# install uv (if not already installed)
+command -v uv &> /dev/null || curl -LsSf https://astral.sh/uv/install.sh | sh
+# create a .venv local virtual environment (if it doesn't exist)
+[ -d ".venv" ] || uv venv
+# install the repo dependencies
+uv sync
+# activate venv so that `python` uses the project's venv instead of system python
+source .venv/bin/activate
+
+# -----------------------------------------------------------------------------
+# wandb setup
+# If you wish to use wandb for logging (it's nice!, recommended).
+# 1) Make sure to first log in to wandb, e.g. run:
+#    `wandb login`
+# 2) Set the WANDB_RUN environment variable when running this script, e.g.:
+#    `WANDB_RUN=d26 bash speedrun.sh`
+# if [ -z "$WANDB_RUN" ]; then
+#     # by default use "dummy" : it's handled as a special case, skips logging to wandb
+#     WANDB_RUN=dummy
+# fi
+export WANDB_API_KEY="ec7a9c0701d404122e4fc5c7c7518ed17f5b03ca"
+export WANDB_RUN=${DATA_NAME}_d20_1node
+
+# -----------------------------------------------------------------------------
+# During the course of the run, we will be writing markdown reports to the report/
+# directory in the base dir. This command clears it out and writes a header section
+# with a bunch of system info and a timestamp that marks the start of the run.
+python -m nanochat.report reset --exp_name=$WANDB_RUN
+
+# -----------------------------------------------------------------------------
+# Tokenizer
+
+# Install Rust / Cargo
+curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
+source "$HOME/.cargo/env"
+
+# Build the rustbpe Tokenizer
+uv run maturin develop --release --manifest-path rustbpe/Cargo.toml
+
+# # Download the first ~2B characters of pretraining dataset
+# # look at dev/repackage_data_reference.py for details on how this data was prepared
+# # each data shard is ~250M chars
+# # so we download 2e9 / 250e6 = 8 data shards at this point
+# # each shard is ~100MB of text (compressed), so this is about ~800MB of data on disk
+# python -m nanochat.dataset -n 8
+# # Immediately also kick off downloading more shards in the background while tokenizer trains
+# # See comment below for why 240 is the right number here
+# python -m nanochat.dataset -n 240 &
+# DATASET_DOWNLOAD_PID=$!
+# train the tokenizer with vocab size 2**16 = 65536 on ~2B characters of data
+export TOKENIZER_NAME="tokenizer_${DATA_NAME}"
+python -m scripts.tok_train --max_chars=2000000000 --data_dir=$DATA_DIR --tokenizer_name=$TOKENIZER_NAME
+# evaluate the tokenizer (report compression ratio etc.)
+python -m scripts.tok_eval --tokenizer_name=$TOKENIZER_NAME
+
+# -----------------------------------------------------------------------------
+# 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.
+# At 250M chars/shard, this is 54B / 250M ~= 216 shards needed for pretraining.
+# Round up to 240 for safety. At ~100MB/shard, this downloads ~24GB of data to disk.
+# (The total number of shards available in the entire dataset is 1822.)
+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  --data_dir=$DATA_DIR  --tokenizer_name=$TOKENIZER_NAME --model_tag=$WANDB_RUN
+# evaluate the model on a larger chunk of train/val data and draw some samples
+torchrun --standalone --nproc_per_node=8 -m scripts.base_loss  --data_dir=$DATA_DIR --tokenizer_name=$TOKENIZER_NAME --model_tag=$WANDB_RUN
+# evaluate the model on CORE tasks
+torchrun --standalone --nproc_per_node=8 -m scripts.base_eval --model_tag=$WANDB_RUN
+
+# -----------------------------------------------------------------------------
+# Midtraining (teach the model conversation special tokens, tool use, multiple choice)
+
+# run midtraining and eval the model
+torchrun --standalone --nproc_per_node=8 -m scripts.mid_train -- --run=$WANDB_RUN  --model_tag=$WANDB_RUN --dataset_choice=$POST_TRAINING_DATA_NAME
+torchrun --standalone --nproc_per_node=8 -m scripts.chat_eval -- -i mid --model_tag=$WANDB_RUN
+
+# -----------------------------------------------------------------------------
+# Supervised Finetuning (domain adaptation to each sequence all by itself per row)
+
+# train sft and re-eval right away (should see a small bump)
+torchrun --standalone --nproc_per_node=8 -m scripts.chat_sft -- --run=$WANDB_RUN --model_tag=$WANDB_RUN --dataset_choice=$POST_TRAINING_DATA_NAME
+torchrun --standalone --nproc_per_node=8 -m scripts.chat_eval -- -i sft --model_tag=$WANDB_RUN
+
+# chat with the model over CLI! Leave out the -p to chat interactively
+# python -m scripts.chat_cli -p "Why is the sky blue?"
+
+# even better, chat with your model over a pretty WebUI ChatGPT style
+# python -m scripts.chat_web
+
+# -----------------------------------------------------------------------------
+# Reinforcement Learning. Optional, and currently only on GSM8K
+# (optional)
+
+# run reinforcement learning
+# torchrun --standalone --nproc_per_node=8 -m scripts.chat_rl -- --run=$WANDB_RUN
+# eval the RL model only on GSM8K
+# torchrun --standalone --nproc_per_node=8 -m scripts.chat_eval -- -i rl -a GSM8K
+
+# -----------------------------------------------------------------------------
+# Generate the full report by putting together all the sections
+# report.md is the output and will be copied to current directory for convenience
+python -m nanochat.report generate --exp_name=$WANDB_RUN