mirror of
https://github.com/karpathy/nanochat.git
synced 2025-12-06 04:12:13 +00:00
remove pretrain.sh and midtrain.sh
This commit is contained in:
Shizhe Diao
parent
dd8310c3d4
commit
1d34a19b87
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@ -1,84 +0,0 @@
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#!/bin/bash
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#SBATCH --account nvr_lpr_llm
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#SBATCH --partition interactive,batch_short,batch_block1,backfill
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#SBATCH --job-name=nanochat_multinode_d20
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#SBATCH --nodes=1
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#SBATCH --ntasks-per-node=1
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#SBATCH --gpus-per-node=8
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#SBATCH --time=04:00:00
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#SBATCH --output=logs/nanochat_1node_d20-%j.out
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#SBATCH --mem=0
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#SBATCH --exclusive
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set -x # Enable debug output
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export DATA_NAME=nemotron # nemotron # smoltalk
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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
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# Default intermediate artifacts directory is in ~/.cache/nanochat
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export OMP_NUM_THREADS=1
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export NANOCHAT_BASE_DIR="$HOME/nanochat_cache"
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mkdir -p $NANOCHAT_BASE_DIR
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# -----------------------------------------------------------------------------
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# Multi-node defaults from Slurm environment
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export GPUS_PER_NODE=${GPUS_PER_NODE:-${SLURM_GPUS_ON_NODE:-8}}
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export NNODES=${NNODES:-${SLURM_NNODES:-2}}
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export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
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export MASTER_PORT=${MASTER_PORT:-29500}
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export RDZV_ENDPOINT=$MASTER_ADDR:$MASTER_PORT
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export NCCL_ASYNC_ERROR_HANDLING=1
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# 1️⃣ 创建或重建 venv（--clear 会先清空旧内容）
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[ -d ".venv" ] || uv venv "$HOME/nanochat_cache/.venv" # --clear
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# 2️⃣ 激活虚拟环境
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source "$HOME/nanochat_cache/.venv/bin/activate"
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# 3️⃣ 安装依赖（uv 会自动识别项目 pyproject.toml）
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cd /lustre/fs1/portfolios/nvr/projects/nvr_lpr_llm/users/sdiao/nanochat
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uv sync --active
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export WANDB_API_KEY="ec7a9c0701d404122e4fc5c7c7518ed17f5b03ca"
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export WANDB_RUN=data_${DATA_NAME}_base_${BASE_NAME}_${SLURM_JOB_ID}
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# python -m nanochat.report reset --exp_name=$WANDB_RUN
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# -----------------------------------------------------------------------------
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# Midtraining (teach the model conversation special tokens, tool use, multiple choice)
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# run midtraining and eval the model (multi-node)
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# mid_train loads from base_checkpoints/$WANDB_RUN and saves to mid_checkpoints/$WANDB_RUN
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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=$DATA_NAME'
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srun --ntasks=$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=$WANDB_RUN'
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# -----------------------------------------------------------------------------
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# Supervised Finetuning (domain adaptation to each sequence all by itself per row)
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# train sft and re-eval right away (should see a small bump) (multi-node)
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# chat_sft loads from mid_checkpoints/$WANDB_RUN and saves to chatsft_checkpoints/$WANDB_RUN
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srun --ntasks=$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=$DATA_NAME'
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srun --ntasks=$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=$WANDB_RUN'
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# chat with the model over CLI! Leave out the -p to chat interactively
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# python -m scripts.chat_cli -p "Why is the sky blue?"
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# even better, chat with your model over a pretty WebUI ChatGPT style
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# python -m scripts.chat_web
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# -----------------------------------------------------------------------------
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# Reinforcement Learning. Optional, and currently only on GSM8K
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# (optional)
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# run reinforcement learning
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# torchrun --standalone --nproc_per_node=8 -m scripts.chat_rl -- --run=$WANDB_RUN
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# eval the RL model only on GSM8K
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# torchrun --standalone --nproc_per_node=8 -m scripts.chat_eval -- -i rl -a GSM8K
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# -----------------------------------------------------------------------------
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# Generate the full report by putting together all the sections
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# report.md is the output and will be copied to current directory for convenience
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python -m nanochat.report generate --exp_name=$WANDB_RUN
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@ -1,184 +0,0 @@
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#!/bin/bash
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#SBATCH --account nvr_lpr_llm
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#SBATCH --partition interactive,batch_short,batch_block1,backfill
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#SBATCH --job-name=nanochat_multinode_d20
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#SBATCH --nodes=1
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#SBATCH --ntasks-per-node=1
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#SBATCH --gpus-per-node=8
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#SBATCH --time=04:00:00
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#SBATCH --output=logs/nanochat_1node_d20-%j.out
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#SBATCH --mem=0
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#SBATCH --exclusive
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# This script is the "Best ChatGPT clone that $100 can buy",
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# It is designed to run in ~4 hours on 8XH100 node at $3/GPU/hour.
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# 1) Example launch (simplest):
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# bash speedrun.sh
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# 2) Example launch in a screen session (because the run takes ~4 hours):
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# screen -L -Logfile speedrun.log -S speedrun bash speedrun.sh
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# 3) Example launch with wandb logging, but see below for setting up wandb first:
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# WANDB_RUN=speedrun screen -L -Logfile speedrun.log -S speedrun bash speedrun.sh
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set -x # Enable debug output
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DATA_NAME=climbmix_small
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export DATA_DIR=/lustre/fsw/portfolios/nvr/users/sdiao/nanochat/data/$DATA_NAME
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export MATRIX_LR=0.02
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# Default intermediate artifacts directory is in ~/.cache/nanochat
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export OMP_NUM_THREADS=1
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export NANOCHAT_BASE_DIR="$HOME/nanochat_cache"
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mkdir -p $NANOCHAT_BASE_DIR
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# -----------------------------------------------------------------------------
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# Multi-node defaults from Slurm environment
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export GPUS_PER_NODE=${GPUS_PER_NODE:-${SLURM_GPUS_ON_NODE:-8}}
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export NNODES=${NNODES:-${SLURM_NNODES:-2}}
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export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
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export MASTER_PORT=${MASTER_PORT:-29500}
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export RDZV_ENDPOINT=$MASTER_ADDR:$MASTER_PORT
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export NCCL_ASYNC_ERROR_HANDLING=1
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# -----------------------------------------------------------------------------
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# Python venv setup with uv
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# # install uv (if not already installed)
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# command -v uv &> /dev/null || curl -LsSf https://astral.sh/uv/install.sh | sh
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# # create a .venv local virtual environment (if it doesn't exist)
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# [ -d "$HOME/nanochat_cache/.venv" ] || uv venv
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# # install the repo dependencies
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# uv sync
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# # activate venv so that `python` uses the project's venv instead of system python
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# source $HOME/nanochat_cache/.venv/bin/activate
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# 1️⃣ 创建或重建 venv（--clear 会先清空旧内容）
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[ -d ".venv" ] || uv venv "$HOME/nanochat_cache/.venv" # --clear
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# 2️⃣ 激活虚拟环境
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source "$HOME/nanochat_cache/.venv/bin/activate"
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# 3️⃣ 安装依赖（uv 会自动识别项目 pyproject.toml）
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cd /lustre/fs1/portfolios/nvr/projects/nvr_lpr_llm/users/sdiao/nanochat
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uv sync --active
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# -----------------------------------------------------------------------------
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# wandb setup
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# If you wish to use wandb for logging (it's nice!, recommended).
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# 1) Make sure to first log in to wandb, e.g. run:
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# `wandb login`
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# 2) Set the WANDB_RUN environment variable when running this script, e.g.:
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# `WANDB_RUN=d26 bash speedrun.sh`
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# if [ -z "$WANDB_RUN" ]; then
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# # by default use "dummy" : it's handled as a special case, skips logging to wandb
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# WANDB_RUN=dummy
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# fi
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export WANDB_API_KEY="ec7a9c0701d404122e4fc5c7c7518ed17f5b03ca"
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export WANDB_RUN=${DATA_NAME}_d20_1node_matrixlr${MATRIX_LR}_${SLURM_JOB_ID}
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# -----------------------------------------------------------------------------
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# During the course of the run, we will be writing markdown reports to the report/
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# directory in the base dir. This command clears it out and writes a header section
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# with a bunch of system info and a timestamp that marks the start of the run.
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python -m nanochat.report reset --exp_name=$WANDB_RUN
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# -----------------------------------------------------------------------------
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# Tokenizer
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# Install Rust / Cargo
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curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
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source "$HOME/.cargo/env"
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echo "VIRTUAL_ENV: $VIRTUAL_ENV"
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echo "CONDA_PREFIX: $CONDA_PREFIX"
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unset CONDA_PREFIX
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# Build the rustbpe Tokenizer
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# uv run
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maturin develop --release --manifest-path rustbpe/Cargo.toml
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# Download the first ~2B characters of pretraining dataset
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# look at dev/repackage_data_reference.py for details on how this data was prepared
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# each data shard is ~250M chars
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# so we download 2e9 / 250e6 = 8 data shards at this point
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# each shard is ~100MB of text (compressed), so this is about ~800MB of data on disk
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python -m nanochat.dataset -n 8
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# Immediately also kick off downloading more shards in the background while tokenizer trains
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# See comment below for why 240 is the right number here
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python -m nanochat.dataset -n 240 &
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DATASET_DOWNLOAD_PID=$!
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# train the tokenizer with vocab size 2**16 = 65536 on ~2B characters of data
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# Use unique tokenizer name based on dataset
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export TOKENIZER_NAME="tokenizer_${DATA_NAME}"
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python -m scripts.tok_train --max_chars=2000000000 --data_dir=$DATA_DIR --tokenizer_name=$TOKENIZER_NAME
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# evaluate the tokenizer (report compression ratio etc.)
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python -m scripts.tok_eval --tokenizer_name=$TOKENIZER_NAME
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# -----------------------------------------------------------------------------
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# Base model (pretraining)
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# Download the eval_bundle from s3 to evaluate CORE metric during training (~162MB)
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EVAL_BUNDLE_URL=https://karpathy-public.s3.us-west-2.amazonaws.com/eval_bundle.zip
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if [ ! -d "$NANOCHAT_BASE_DIR/eval_bundle" ]; then
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curl -L -o eval_bundle.zip $EVAL_BUNDLE_URL
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unzip -q eval_bundle.zip
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rm eval_bundle.zip
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mv eval_bundle $NANOCHAT_BASE_DIR
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fi
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# The d20 model is 561M parameters.
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# Chinchilla says #tokens = 20X #params, so we need 561e6 * 20 = 11.2B tokens.
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# Assume our tokenizer is 4.8 chars/token, this is 11.2B * 4.8 ~= 54B chars.
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# At 250M chars/shard, this is 54B / 250M ~= 216 shards needed for pretraining.
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# Round up to 240 for safety. At ~100MB/shard, this downloads ~24GB of data to disk.
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# (The total number of shards available in the entire dataset is 1822.)
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echo "Waiting for dataset download to complete..."
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wait $DATASET_DOWNLOAD_PID
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# Warm up venv on all nodes (ensures env is available everywhere)
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srun --ntasks=$NNODES --ntasks-per-node=1 bash --noprofile --norc -lc 'source $HOME/nanochat_cache/.venv/bin/activate; python -c "import torch; print(torch.cuda.device_count())"'
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# pretrain the d20 model (multi-node)
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srun --ntasks=$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=$WANDB_RUN'
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# evaluate the model on a larger chunk of train/val data and draw some samples (multi-node)
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srun --ntasks=$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=$WANDB_RUN'
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# evaluate the model on CORE tasks (multi-node)
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srun --ntasks=$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=$WANDB_RUN'
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# # -----------------------------------------------------------------------------
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# # Midtraining (teach the model conversation special tokens, tool use, multiple choice)
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# # run midtraining and eval the model (multi-node)
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# # mid_train loads from base_checkpoints/$WANDB_RUN and saves to mid_checkpoints/$WANDB_RUN
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# srun --ntasks=$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'
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# 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=$WANDB_RUN'
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# # -----------------------------------------------------------------------------
|
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# # Supervised Finetuning (domain adaptation to each sequence all by itself per row)
|
||||
|
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# # train sft and re-eval right away (should see a small bump) (multi-node)
|
||||
# # chat_sft loads from mid_checkpoints/$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'
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# 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=$WANDB_RUN'
|
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|
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# # chat with the model over CLI! Leave out the -p to chat interactively
|
||||
# # python -m scripts.chat_cli -p "Why is the sky blue?"
|
||||
|
||||
# # even better, chat with your model over a pretty WebUI ChatGPT style
|
||||
# # python -m scripts.chat_web
|
||||
|
||||
# # -----------------------------------------------------------------------------
|
||||
# # Reinforcement Learning. Optional, and currently only on GSM8K
|
||||
# # (optional)
|
||||
|
||||
# # run reinforcement learning
|
||||
# # torchrun --standalone --nproc_per_node=8 -m scripts.chat_rl -- --run=$WANDB_RUN
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# # eval the RL model only on GSM8K
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# # torchrun --standalone --nproc_per_node=8 -m scripts.chat_eval -- -i rl -a GSM8K
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# # -----------------------------------------------------------------------------
|
||||
# # 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
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143
speedrun.sh
143
speedrun.sh
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|
@ -1,143 +0,0 @@
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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 $3/GPU/hour.
|
||||
|
||||
# 1) Example launch (simplest):
|
||||
# bash speedrun.sh
|
||||
# 2) Example launch in a screen session (because the run takes ~4 hours):
|
||||
# screen -L -Logfile speedrun.log -S speedrun bash speedrun.sh
|
||||
# 3) Example launch with wandb logging, but see below for setting up wandb first:
|
||||
# WANDB_RUN=speedrun screen -L -Logfile speedrun.log -S speedrun bash speedrun.sh
|
||||
set -x
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DATA_NAME=smollm
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DATA_DIR=/lustre/fsw/portfolios/nvr/users/sdiao/nanochat/data/$DATA_NAME
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|
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# Default intermediate artifacts directory is in ~/.cache/nanochat
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export OMP_NUM_THREADS=1
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export NANOCHAT_BASE_DIR="$HOME/.cache/nanochat"
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mkdir -p $NANOCHAT_BASE_DIR
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Python venv setup with uv
|
||||
|
||||
# install uv (if not already installed)
|
||||
command -v uv &> /dev/null || curl -LsSf https://astral.sh/uv/install.sh | sh
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# create a .venv local virtual environment (if it doesn't exist)
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[ -d ".venv" ] || uv venv
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# install the repo dependencies
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||||
uv sync --extra gpu
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||||
# activate venv so that `python` uses the project's venv instead of system python
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||||
source .venv/bin/activate
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# wandb setup
|
||||
# If you wish to use wandb for logging (it's nice!, recommended).
|
||||
# 1) Make sure to first log in to wandb, e.g. run:
|
||||
# `wandb login`
|
||||
# 2) Set the WANDB_RUN environment variable when running this script, e.g.:
|
||||
# `WANDB_RUN=d26 bash speedrun.sh`
|
||||
# if [ -z "$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
|
||||
Loading…
Reference in New Issue
Block a user