diff --git a/midtrain_sft_submit.sh b/midtrain_sft_submit.sh
deleted file mode 100644
index d596ffd..0000000
--- a/midtrain_sft_submit.sh
+++ /dev/null
@@ -1,84 +0,0 @@
-#!/bin/bash
-#SBATCH --account nvr_lpr_llm
-#SBATCH --partition interactive,batch_short,batch_block1,backfill
-#SBATCH --job-name=nanochat_multinode_d20    
-#SBATCH --nodes=1
-#SBATCH --ntasks-per-node=1
-#SBATCH --gpus-per-node=8
-#SBATCH --time=04:00:00
-#SBATCH --output=logs/nanochat_1node_d20-%j.out
-#SBATCH --mem=0
-#SBATCH --exclusive
-
-
-set -x  # Enable debug output
-
-export DATA_NAME=nemotron # nemotron # smoltalk
-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
-export NANOCHAT_BASE_DIR="$HOME/nanochat_cache"
-mkdir -p $NANOCHAT_BASE_DIR
-
-# -----------------------------------------------------------------------------
-# Multi-node defaults from Slurm environment
-
-export GPUS_PER_NODE=${GPUS_PER_NODE:-${SLURM_GPUS_ON_NODE:-8}}
-export NNODES=${NNODES:-${SLURM_NNODES:-2}}
-export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
-export MASTER_PORT=${MASTER_PORT:-29500}
-export RDZV_ENDPOINT=$MASTER_ADDR:$MASTER_PORT
-export NCCL_ASYNC_ERROR_HANDLING=1
-
-
-# 1️⃣ 创建或重建 venv（--clear 会先清空旧内容）
-[ -d ".venv" ] || uv venv "$HOME/nanochat_cache/.venv" # --clear
-
-# 2️⃣ 激活虚拟环境
-source "$HOME/nanochat_cache/.venv/bin/activate"
-
-# 3️⃣ 安装依赖（uv 会自动识别项目 pyproject.toml）
-cd /lustre/fs1/portfolios/nvr/projects/nvr_lpr_llm/users/sdiao/nanochat
-uv sync --active
-
-export WANDB_API_KEY="ec7a9c0701d404122e4fc5c7c7518ed17f5b03ca"
-export WANDB_RUN=data_${DATA_NAME}_base_${BASE_NAME}_${SLURM_JOB_ID}
-
-# python -m nanochat.report reset --exp_name=$WANDB_RUN
-
-# -----------------------------------------------------------------------------
-# Midtraining (teach the model conversation special tokens, tool use, multiple choice)
-
-# run midtraining and eval the model (multi-node)
-# mid_train loads from base_checkpoints/$WANDB_RUN and saves to mid_checkpoints/$WANDB_RUN
-srun --ntasks=$NNODES --ntasks-per-node=1 bash --noprofile --norc -lc 'source $HOME/nanochat_cache/.venv/bin/activate; torchrun --nnodes=$NNODES --nproc_per_node=$GPUS_PER_NODE --rdzv_endpoint=$RDZV_ENDPOINT --rdzv_id=$SLURM_JOB_ID --node_rank=$SLURM_NODEID -m scripts.mid_train -- --run=$WANDB_RUN --model_tag=$BASE_NAME --dataset_choice=$DATA_NAME'
-srun --ntasks=$NNODES --ntasks-per-node=1 bash --noprofile --norc -lc 'source $HOME/nanochat_cache/.venv/bin/activate; torchrun --nnodes=$NNODES --nproc_per_node=$GPUS_PER_NODE --rdzv_endpoint=$RDZV_ENDPOINT --rdzv_id=$SLURM_JOB_ID --node_rank=$SLURM_NODEID -m scripts.chat_eval -- -i mid --model-tag=$WANDB_RUN'
-
-# -----------------------------------------------------------------------------
-# Supervised Finetuning (domain adaptation to each sequence all by itself per row)
-
-# train sft and re-eval right away (should see a small bump) (multi-node)
-# chat_sft loads from mid_checkpoints/$WANDB_RUN and saves to chatsft_checkpoints/$WANDB_RUN
-srun --ntasks=$NNODES --ntasks-per-node=1 bash --noprofile --norc -lc 'source $HOME/nanochat_cache/.venv/bin/activate; torchrun --nnodes=$NNODES --nproc_per_node=$GPUS_PER_NODE --rdzv_endpoint=$RDZV_ENDPOINT --rdzv_id=$SLURM_JOB_ID --node_rank=$SLURM_NODEID -m scripts.chat_sft -- --run=$WANDB_RUN --model_tag=$WANDB_RUN --dataset_choice=$DATA_NAME'
-srun --ntasks=$NNODES --ntasks-per-node=1 bash --noprofile --norc -lc 'source $HOME/nanochat_cache/.venv/bin/activate; torchrun --nnodes=$NNODES --nproc_per_node=$GPUS_PER_NODE --rdzv_endpoint=$RDZV_ENDPOINT --rdzv_id=$SLURM_JOB_ID --node_rank=$SLURM_NODEID -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
\ No newline at end of file
diff --git a/pretrain_submit.sh b/pretrain_submit.sh
deleted file mode 100644
index ab8defa..0000000
--- a/pretrain_submit.sh
+++ /dev/null
@@ -1,184 +0,0 @@
-#!/bin/bash
-#SBATCH --account nvr_lpr_llm
-#SBATCH --partition interactive,batch_short,batch_block1,backfill
-#SBATCH --job-name=nanochat_multinode_d20    
-#SBATCH --nodes=1
-#SBATCH --ntasks-per-node=1
-#SBATCH --gpus-per-node=8
-#SBATCH --time=04:00:00
-#SBATCH --output=logs/nanochat_1node_d20-%j.out
-#SBATCH --mem=0
-#SBATCH --exclusive
-
-# This script is the "Best ChatGPT clone that $100 can buy",
-# It is designed to run in ~4 hours on 8XH100 node at $3/GPU/hour.
-
-# 1) Example launch (simplest):
-# bash speedrun.sh
-# 2) Example launch in a screen session (because the run takes ~4 hours):
-# screen -L -Logfile speedrun.log -S speedrun bash speedrun.sh
-# 3) Example launch with wandb logging, but see below for setting up wandb first:
-# WANDB_RUN=speedrun screen -L -Logfile speedrun.log -S speedrun bash speedrun.sh
-
-set -x  # Enable debug output
-
-DATA_NAME=climbmix_small
-export DATA_DIR=/lustre/fsw/portfolios/nvr/users/sdiao/nanochat/data/$DATA_NAME
-export MATRIX_LR=0.02
-
-# Default intermediate artifacts directory is in ~/.cache/nanochat
-export OMP_NUM_THREADS=1
-export NANOCHAT_BASE_DIR="$HOME/nanochat_cache"
-mkdir -p $NANOCHAT_BASE_DIR
-
-# -----------------------------------------------------------------------------
-# Multi-node defaults from Slurm environment
-
-export GPUS_PER_NODE=${GPUS_PER_NODE:-${SLURM_GPUS_ON_NODE:-8}}
-export NNODES=${NNODES:-${SLURM_NNODES:-2}}
-export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
-export MASTER_PORT=${MASTER_PORT:-29500}
-export RDZV_ENDPOINT=$MASTER_ADDR:$MASTER_PORT
-export NCCL_ASYNC_ERROR_HANDLING=1
-
-# -----------------------------------------------------------------------------
-# 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 "$HOME/nanochat_cache/.venv" ] || uv venv
-# # install the repo dependencies
-# uv sync
-# # activate venv so that `python` uses the project's venv instead of system python
-# source $HOME/nanochat_cache/.venv/bin/activate
-
-# 1️⃣ 创建或重建 venv（--clear 会先清空旧内容）
-[ -d ".venv" ] || uv venv "$HOME/nanochat_cache/.venv" # --clear
-
-# 2️⃣ 激活虚拟环境
-source "$HOME/nanochat_cache/.venv/bin/activate"
-
-# 3️⃣ 安装依赖（uv 会自动识别项目 pyproject.toml）
-cd /lustre/fs1/portfolios/nvr/projects/nvr_lpr_llm/users/sdiao/nanochat
-uv sync --active
-
-
-# -----------------------------------------------------------------------------
-# wandb setup
-# If you wish to use wandb for logging (it's nice!, recommended).
-# 1) Make sure to first log in to wandb, e.g. run:
-#    `wandb login`
-# 2) Set the WANDB_RUN environment variable when running this script, e.g.:
-#    `WANDB_RUN=d26 bash speedrun.sh`
-# if [ -z "$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=${DATA_NAME}_d20_1node_matrixlr${MATRIX_LR}_${SLURM_JOB_ID}
-
-# -----------------------------------------------------------------------------
-# During the course of the run, we will be writing markdown reports to the report/
-# directory in the base dir. This command clears it out and writes a header section
-# with a bunch of system info and a timestamp that marks the start of the run.
-python -m nanochat.report reset --exp_name=$WANDB_RUN
-
-# -----------------------------------------------------------------------------
-# Tokenizer
-
-# Install Rust / Cargo
-curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
-source "$HOME/.cargo/env"
-
-echo "VIRTUAL_ENV: $VIRTUAL_ENV"
-echo "CONDA_PREFIX: $CONDA_PREFIX"
-unset CONDA_PREFIX
-
-# Build the rustbpe Tokenizer
-# uv run 
-maturin develop --release --manifest-path rustbpe/Cargo.toml
-
-# Download the first ~2B characters of pretraining dataset
-# look at dev/repackage_data_reference.py for details on how this data was prepared
-# each data shard is ~250M chars
-# so we download 2e9 / 250e6 = 8 data shards at this point
-# each shard is ~100MB of text (compressed), so this is about ~800MB of data on disk
-python -m nanochat.dataset -n 8
-# Immediately also kick off downloading more shards in the background while tokenizer trains
-# See comment below for why 240 is the right number here
-python -m nanochat.dataset -n 240 &
-DATASET_DOWNLOAD_PID=$!
-# train the tokenizer with vocab size 2**16 = 65536 on ~2B characters of data
-# Use unique tokenizer name based on dataset
-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 $DATASET_DOWNLOAD_PID
-
-# Warm up venv on all nodes (ensures env is available everywhere)
-srun --ntasks=$NNODES --ntasks-per-node=1 bash --noprofile --norc -lc 'source $HOME/nanochat_cache/.venv/bin/activate; python -c "import torch; print(torch.cuda.device_count())"'
-
-# pretrain the d20 model (multi-node)
-srun --ntasks=$NNODES --ntasks-per-node=1 bash --noprofile --norc -lc 'source $HOME/nanochat_cache/.venv/bin/activate; torchrun --nnodes=$NNODES --nproc_per_node=$GPUS_PER_NODE --rdzv_endpoint=$RDZV_ENDPOINT --rdzv_id=$SLURM_JOB_ID --node_rank=$SLURM_NODEID -m scripts.base_train -- --depth=20 --run=$WANDB_RUN --data_dir=$DATA_DIR --matrix_lr=$MATRIX_LR --tokenizer_name=$TOKENIZER_NAME --model_tag=$WANDB_RUN'
-# evaluate the model on a larger chunk of train/val data and draw some samples (multi-node)
-srun --ntasks=$NNODES --ntasks-per-node=1 bash --noprofile --norc -lc 'source $HOME/nanochat_cache/.venv/bin/activate; torchrun --nnodes=$NNODES --nproc_per_node=$GPUS_PER_NODE --rdzv_endpoint=$RDZV_ENDPOINT --rdzv_id=$SLURM_JOB_ID --node_rank=$SLURM_NODEID -m scripts.base_loss --data_dir=$DATA_DIR --tokenizer_name=$TOKENIZER_NAME --model_tag=$WANDB_RUN'
-# evaluate the model on CORE tasks (multi-node)
-srun --ntasks=$NNODES --ntasks-per-node=1 bash --noprofile --norc -lc 'source $HOME/nanochat_cache/.venv/bin/activate; torchrun --nnodes=$NNODES --nproc_per_node=$GPUS_PER_NODE --rdzv_endpoint=$RDZV_ENDPOINT --rdzv_id=$SLURM_JOB_ID --node_rank=$SLURM_NODEID -m scripts.base_eval -- --model_tag=$WANDB_RUN'
-
-# # -----------------------------------------------------------------------------
-# # Midtraining (teach the model conversation special tokens, tool use, multiple choice)
-
-# # run midtraining and eval the model (multi-node)
-# # mid_train loads from base_checkpoints/$WANDB_RUN and saves to mid_checkpoints/$WANDB_RUN
-# srun --ntasks=$NNODES --ntasks-per-node=1 bash --noprofile --norc -lc 'source $HOME/nanochat_cache/.venv/bin/activate; torchrun --nnodes=$NNODES --nproc_per_node=$GPUS_PER_NODE --rdzv_endpoint=$RDZV_ENDPOINT --rdzv_id=$SLURM_JOB_ID --node_rank=$SLURM_NODEID -m scripts.mid_train -- --run=$WANDB_RUN --model_tag=$WANDB_RUN'
-# srun --ntasks=$NNODES --ntasks-per-node=1 bash --noprofile --norc -lc 'source $HOME/nanochat_cache/.venv/bin/activate; torchrun --nnodes=$NNODES --nproc_per_node=$GPUS_PER_NODE --rdzv_endpoint=$RDZV_ENDPOINT --rdzv_id=$SLURM_JOB_ID --node_rank=$SLURM_NODEID -m scripts.chat_eval -- -i mid --model_tag=$WANDB_RUN'
-
-# # -----------------------------------------------------------------------------
-# # Supervised Finetuning (domain adaptation to each sequence all by itself per row)
-
-# # train sft and re-eval right away (should see a small bump) (multi-node)
-# # chat_sft loads from mid_checkpoints/$WANDB_RUN and saves to chatsft_checkpoints/$WANDB_RUN
-# srun --ntasks=$NNODES --ntasks-per-node=1 bash --noprofile --norc -lc 'source $HOME/nanochat_cache/.venv/bin/activate; torchrun --nnodes=$NNODES --nproc_per_node=$GPUS_PER_NODE --rdzv_endpoint=$RDZV_ENDPOINT --rdzv_id=$SLURM_JOB_ID --node_rank=$SLURM_NODEID -m scripts.chat_sft -- --run=$WANDB_RUN --model_tag=$WANDB_RUN'
-# srun --ntasks=$NNODES --ntasks-per-node=1 bash --noprofile --norc -lc 'source $HOME/nanochat_cache/.venv/bin/activate; torchrun --nnodes=$NNODES --nproc_per_node=$GPUS_PER_NODE --rdzv_endpoint=$RDZV_ENDPOINT --rdzv_id=$SLURM_JOB_ID --node_rank=$SLURM_NODEID -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
diff --git a/speedrun.sh b/speedrun.sh
deleted file mode 100644
index 45277f4..0000000
--- a/speedrun.sh
+++ /dev/null
@@ -1,143 +0,0 @@
-#!/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