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+#!/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 $3/GPU/hour.
+
+# 1) Example launch (simplest):
+# bash speedrun.sh
+# 2) Example launch in a screen session (because the run takes ~4 hours):
+# screen -L -Logfile speedrun.log -S speedrun bash speedrun.sh
+# 3) Example launch with wandb logging, but see below for setting up wandb first:
+# WANDB_RUN=speedrun screen -L -Logfile speedrun.log -S speedrun bash speedrun.sh
+set -x
+
+DATA_NAME=smollm
+DATA_DIR=/lustre/fsw/portfolios/nvr/users/sdiao/nanochat/data/$DATA_NAME
+
+# Default intermediate artifacts directory is in ~/.cache/nanochat
+export OMP_NUM_THREADS=1
+export NANOCHAT_BASE_DIR="$HOME/.cache/nanochat"
+mkdir -p $NANOCHAT_BASE_DIR
+
+# -----------------------------------------------------------------------------
+# Python venv setup with uv
+
+# install uv (if not already installed)
+command -v uv &> /dev/null || curl -LsSf https://astral.sh/uv/install.sh | sh
+# create a .venv local virtual environment (if it doesn't exist)
+[ -d ".venv" ] || uv venv
+# install the repo dependencies
+uv sync --extra gpu
+# activate venv so that `python` uses the project's venv instead of system python
+source .venv/bin/activate
+
+# -----------------------------------------------------------------------------
+# wandb setup
+# If you wish to use wandb for logging (it's nice!, recommended).
+# 1) Make sure to first log in to wandb, e.g. run:
+#    `wandb login`
+# 2) Set the WANDB_RUN environment variable when running this script, e.g.:
+#    `WANDB_RUN=d26 bash speedrun.sh`
+# if [ -z "$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=fineweb_d20_test
+
+# -----------------------------------------------------------------------------
+# 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=$!
+# train the tokenizer with vocab size 2**16 = 65536 on ~2B characters of data
+python -m scripts.tok_train --max_chars=2000000000
+# evaluate the tokenizer (report compression ratio etc.)
+python -m scripts.tok_eval
+
+# -----------------------------------------------------------------------------
+# Base model (pretraining)
+
+# Download the eval_bundle from s3 to evaluate CORE metric during training (~162MB)
+EVAL_BUNDLE_URL=https://karpathy-public.s3.us-west-2.amazonaws.com/eval_bundle.zip
+if [ ! -d "$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.
+# 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=$DATA_DIR
+# evaluate the model on a larger chunk of train/val data and draw some samples
+torchrun --standalone --nproc_per_node=8 -m scripts.base_loss  --data_dir=$DATA_DIR
+# evaluate the model on CORE tasks
+torchrun --standalone --nproc_per_node=8 -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
+
+# -----------------------------------------------------------------------------
+# 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=$WANDB_RUN
+torchrun --standalone --nproc_per_node=8 -m scripts.chat_eval -- -i sft
+
+# chat with the model over CLI! Leave out the -p to chat interactively
+# python -m scripts.chat_cli -p "Why is the sky blue?"
+
+# even better, chat with your model over a pretty WebUI ChatGPT style
+# python -m scripts.chat_web
+
+# -----------------------------------------------------------------------------
+# Reinforcement Learning. Optional, and currently only on GSM8K
+# (optional)
+
+# run reinforcement learning
+# torchrun --standalone --nproc_per_node=8 -m scripts.chat_rl -- --run=$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