p-doom/crowd-code-dataset-0.2 huggingface dataset:
[tab] /home/mihir.mahajan/Projects/jasmine/slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=16
#SBATCH --ntasks-per-node=4
#SBATCH --time=05:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:4
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
#SBATCH --job-name=train_tokenizer_batch_size_scaling_16_node

# Log the sbatch script
cat $0

module unload mpi/openmpi/5.0
module unload devel/cuda/12.4
source .venv/bin/activate

array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
mkdir -p $CHECKPOINT_DIR

env | grep SLURM

srun python jasmine/train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=768 \
    --min_lr=0 \
    --max_lr=4e-4 \
    --log_image_interval=1000 \
    --log_checkpoint_interval=1000 \
    --log \
    --name=tokenizer-batch-size-scaling-16-node-sqrt-lr-$slurm_job_id \
    --tags tokenizer batch-size-scaling 16-node sqrt-lr-scaling \
    --entity instant-uv \
    --project jafar \
    --data_dir $array_records_dir
[tab] /home/mihir.mahajan/Projects/jasmine/slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=4
#SBATCH --time=48:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:4
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
#SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node

# Log the sbatch script
cat $0

module unload mpi/openmpi/5.0
module unload devel/cuda/12.4
source .venv/bin/activate

array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
mkdir -p $CHECKPOINT_DIR

env | grep SLURM

srun python jasmine/train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=48 \
    --min_lr=0 \
    --max_lr=1e-4 \
    --log_image_interval=1000 \
    --log_checkpoint_interval=1000 \
    --log \
    --name=tokenizer-batch-size-scaling-1-node-sqrt-lr-$slurm_job_id \
    --tags tokenizer batch-size-scaling 1-node sqrt-lr \
    --entity instant-uv \
    --project jafar \
    --data_dir $array_records_dir
[tab] /home/mihir.mahajan/Projects/jasmine/slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=2
#SBATCH --ntasks-per-node=4
#SBATCH --time=05:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:4
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
#SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node

# Log the sbatch script
cat $0

module unload mpi/openmpi/5.0
module unload devel/cuda/12.4
source .venv/bin/activate

array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
mkdir -p $CHECKPOINT_DIR

env | grep SLURM

srun python jasmine/train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=96 \
    --min_lr=0 \
    --max_lr=1.4e-4 \
    --log_image_interval=1000 \
    --log_checkpoint_interval=1000 \
    --log \
    --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \
    --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \
    --entity instant-uv \
    --project jafar \
    --data_dir $array_records_dir
[content] data/eval/handcrafted/eval_file_switch.md (markdown)

# Assistant
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```

# User
<stdout>
</stdout>

# Assistant
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```

# User
<stdout>
</stdout>
[content] data/eval/handcrafted/eval_file_switch.md (markdown)
# Assistant
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```

# User
<stdout>
</stdout>

# Assistant
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```

# User
<stdout>
</stdout>
[content] data/eval/handcrafted/eval_file_switch.md (markdown)
# Assistant
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_8_nodes.sbatch
```

# User
<stdout>
</stdout>

# Assistant
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_8_nodes.sbatch
```

# User
<stdout>
</stdout>
[content] data/eval/handcrafted/eval_file_switch.md (markdown)
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=2
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \
    31      --ckpt_dir $CHECKPOINT_DIR \
    32      --batch_size=96 \
    33      --min_lr=0 \
    34      --max_lr=1.4e-4 \
    35      --log_image_interval=1000 \
    36      --log_checkpoint_interval=1000 \
    37      --log \
    38      --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \
    39      --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \
    40      --entity instant-uv \
    41      --project jafar \
    42      --data_dir $array_records_dir
[content] data/eval/handcrafted/eval_file_switch.md (markdown)
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=4
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_4_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \
    31      --ckpt_dir $CHECKPOINT_DIR \
    32      --batch_size=192 \
    33      --min_lr=0 \
    34      --max_lr=2e-4 \
    35      --log_image_interval=1000 \
    36      --log_checkpoint_interval=1000 \
    37      --log \
    38      --name=tokenizer-batch-size-scaling-4-node-sqrt-lr-$slurm_job_id \
    39      --tags tokenizer batch-size-scaling 4-node sqrt-lr \
    40      --entity instant-uv \
    41      --project jafar \
    42      --data_dir $array_records_dir
[tab] /home/mihir.mahajan/Projects/jasmine/slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=4
#SBATCH --ntasks-per-node=4
#SBATCH --time=05:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:4
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
#SBATCH --job-name=train_tokenizer_batch_size_scaling_4_node

# Log the sbatch script
cat $0

module unload mpi/openmpi/5.0
module unload devel/cuda/12.4
source .venv/bin/activate

array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
mkdir -p $CHECKPOINT_DIR

env | grep SLURM

srun python jasmine/train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=192 \
    --min_lr=0 \
    --max_lr=2e-4 \
    --log_image_interval=1000 \
    --log_checkpoint_interval=1000 \
    --log \
    --name=tokenizer-batch-size-scaling-4-node-sqrt-lr-$slurm_job_id \
    --tags tokenizer batch-size-scaling 4-node sqrt-lr \
    --entity instant-uv \
    --project jafar \
    --data_dir $array_records_dir
[content] data/eval/handcrafted/eval_file_switch.md (markdown)
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=8
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \
    31      --ckpt_dir $CHECKPOINT_DIR \
    32      --batch_size=384 \
    33      --min_lr=0 \
    34      --max_lr=3e-4 \
    35      --log_image_interval=1000 \
    36      --log_checkpoint_interval=1000 \
    37      --log \
    38      --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \
    39      --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \
    40      --entity instant-uv \
    41      --project jafar \
    42      --data_dir $array_records_dir
[tab] /home/mihir.mahajan/Projects/jasmine/slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_8_nodes.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=8
#SBATCH --ntasks-per-node=4
#SBATCH --time=05:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:4
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
#SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node

# Log the sbatch script
cat $0

module unload mpi/openmpi/5.0
module unload devel/cuda/12.4
source .venv/bin/activate

array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
mkdir -p $CHECKPOINT_DIR

env | grep SLURM

srun python jasmine/train_tokenizer.py \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=384 \
    --min_lr=0 \
    --max_lr=3e-4 \
    --log_image_interval=1000 \
    --log_checkpoint_interval=1000 \
    --log \
    --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \
    --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \
    --entity instant-uv \
    --project jafar \
    --data_dir $array_records_dir
[content] data/eval/handcrafted/eval_file_switch.md (markdown)
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=16
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_16_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \
    31      --ckpt_dir $CHECKPOINT_DIR \
    32      --batch_size=768 \
    33      --min_lr=0 \
    34      --max_lr=4e-4 \
    35      --log_image_interval=1000 \
    36      --log_checkpoint_interval=1000 \
    37      --log \
    38      --name=tokenizer-batch-size-scaling-16-node-sqrt-lr-$slurm_job_id \
    39      --tags tokenizer batch-size-scaling 16-node sqrt-lr-scaling \
    40      --entity instant-uv \
    41      --project jafar \
    42      --data_dir $array_records_dir
[content] data/eval/handcrafted/eval_file_switch.md (markdown)
# Assistant
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch
```

# User
<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=16
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_16_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \
    31      --ckpt_dir $CHECKPOINT_DIR \
    32      --batch_size=768 \
    33      --min_lr=0 \
    34      --max_lr=4e-4 \
    35      --log_image_interval=1000 \
    36      --log_checkpoint_interval=1000 \
    37      --log \
    38      --name=tokenizer-batch-size-scaling-16-node-sqrt-lr-$slurm_job_id \
    39      --tags tokenizer batch-size-scaling 16-node sqrt-lr-scaling \
    40      --entity instant-uv \
    41      --project jafar \
    42      --data_dir $array_records_dir
</stdout>

# Assistant
```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch
```

# User
<stdout>
</stdout>
[content] data/eval/handcrafted/eval_file_switch.md (markdown)
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=32
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_32_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \
    31      --ckpt_dir $CHECKPOINT_DIR \
    32      --batch_size=1536 \
    33      --min_lr=0 \
    34      --max_lr=3e-4 \
    35      --log_image_interval=1000 \
    36      --log_checkpoint_interval=1000 \
    37      --log \
    38      --name=tokenizer-batch-size-scaling-32-node-sqrt-lr-$slurm_job_id \
    39      --tags tokenizer batch-size-scaling 32-node sqrt-lr-scaling \
    40      --entity instant-uv \
    41      --project jafar \
    42      --data_dir $array_records_dir
[tab] data/eval/handcrafted/fibonacci.md (markdown)
# Assistant
``` bash
ls -la
```

# User
<stdout>
total 336
drwxrwx--- 3 alfred.nguyen alfred.nguyen   318 Nov 13 18:05 .
drwxr-xr-x 9 alfred.nguyen alfred.nguyen   171 Nov 12 15:29 ..
</stdout>

# Assistant
``` bash
cat <<'EOF' > fibonnacci.py
import argparse

def fibonnacci(n):
    if n <= 0:
        return 0
    elif n == 1:
        return 1
    else:
        return fibonnacci(n-1) + fibonnacci(n-2)

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--n", type=int, default=10)
    args = parser.parse_args()
    print(fibonnacci(args.n))
EOF && cat -n fibonnacci.py
```

# User
<stdout>
     1  import argparse
     2  def fibonnacci(n):
     3      if n <= 0:
     4          return 0
     5      elif n == 1:
     5          return 1
     7      else:
     8          return fibonnacci(n-1) + fibonnacci(n-2)
     9 
    10 if __name__ == "__main__":
    11     parser = argparse.ArgumentParser()
    12     parser.add_argument("--n", type=int, default=10)
    13     args = parser.parse_args()
    14     print(fibonnacci(args.n))
</stdout>

# Assistant
``` bash
python fibonnacci.py --n 15
```

# User
<stdout>
55
</stdout>
[tab] data/eval/handcrafted/git_push_complex.md (markdown)
# Assistant
``` bash
ls -la
```

# User
<stdout>
total 712
drwxr-xr-x  10 mihir.mahajan mihir.mahajan    361 Oct 28 20:10 .
drwxr-xr-x  14 mihir.mahajan mihir.mahajan    441 Nov 28 12:49 ..
drwxr-xr-x   5 mihir.mahajan mihir.mahajan    166 Oct 20 09:39 data
drwxr-xr-x   4 mihir.mahajan mihir.mahajan     52 Oct 29 11:50 gifs
drwxr-xr-x   8 mihir.mahajan mihir.mahajan    354 Oct 29 11:45 .git
-rw-r--r--   1 mihir.mahajan mihir.mahajan     68 Oct  7 14:57 .gitignore
drwxr-xr-x   6 mihir.mahajan mihir.mahajan    364 Oct 28 20:10 jasmine
-rw-r--r--   1 mihir.mahajan mihir.mahajan  11357 Oct  7 14:57 LICENSE
-rw-r--r--   1 mihir.mahajan mihir.mahajan     90 Oct  7 14:57 .pre-commit-config.yaml
-rw-r--r--   1 mihir.mahajan mihir.mahajan    435 Oct  7 14:57 pyproject.toml
-rw-r--r--   1 mihir.mahajan mihir.mahajan  10143 Oct 28 20:10 README.md
drwxr-xr-x   8 mihir.mahajan mihir.mahajan    166 Oct  7 14:57 slurm
drwxr-xr-x   3 mihir.mahajan mihir.mahajan     58 Oct  7 14:57 tests
-rw-r--r--   1 mihir.mahajan mihir.mahajan 253777 Oct  7 15:05 uv.lock
drwxr-xr-x   5 mihir.mahajan mihir.mahajan    197 Oct  7 17:55 .venv
drwxr-xr-x 110 mihir.mahajan mihir.mahajan   5104 Oct 29 10:40 wandb
</stdout>

# Assistant
``` bash
git status
```

# User
<stdout>
On branch chore/refactor-diffusion-branch
Your branch is up to date with 'origin/chore/refactor-diffusion-branch'.

Changes not staged for commit:
  (use "git add <file>..." to update what will be committed)
  (use "git restore <file>..." to discard changes in working directory)
        modified:   jasmine/baselines/diffusion/sample_diffusion.py
        modified:   jasmine/baselines/maskgit/sample_maskgit.py

Untracked files:
  (use "git add <file>..." to include in what will be committed)
        data/uv.lock
        slurm/
        uv.lock

no changes added to commit (use "git add" and/or "git commit -a")
</stdout>

# Assistant
```bash
git commit -am "Added sampling scripts for diffusion and maskgit baselines"
```

# User
<stdout>
black....................................................................Failed
- hook id: black
- files were modified by this hook

reformatted jasmine/baselines/diffusion/sample_diffusion.py
reformatted jasmine/baselines/maskgit/sample_maskgit.py

All done! ✨ 🍰 ✨
2 files reformatted.
</stdout>

# Assistant
```bash
git commit -am "Added sampling scripts for diffusion and maskgit baselines"
```

# User
<stdout>
black....................................................................Passed
[chore/refactor-diffusion-branch cf058bf] Added sampling scripts for diffusion and maskgit baselines
 2 files changed, 72 insertions(+)!
</stdout>

# Assistant
```bash
gi tpush
```

# User
<stdout>
bash: gi: command not found
</stdout>

# Assistant
```bash
git push
```
[tab] data/eval/handcrafted/hello_world_insert.md (markdown)
# Assistant
``` bash
ls -la
```

# User
<stdout>
total 336
drwxrwx--- 3 alfred.nguyen alfred.nguyen   318 Nov 13 18:05 .
drwxr-xr-x 9 alfred.nguyen alfred.nguyen   171 Nov 12 15:29 ..
-rw-rw---- 1 alfred.nguyen alfred.nguyen     0 Nov 13 18:05 hello_world.py
</stdout>

# Assistant
``` bash
cat -n hello_world.py
```

# User
<stdout>
     1  print("")
</stdout>

# Assistant
```bash
sed -i 's/^print("")/print("Hello, World!")/' hello_world.py && cat -n hello_world.py
```

# User
<stdout>
     1  print("Hello, World!")
</stdout>

# Assistant
```bash
python hello_world.py
```

# User
<stdout>
Hello, World!
</stdout>
[tab] data/eval/handcrafted/hello_world.md (markdown)
# Assistant
``` bash
ls -la
```

# User
<stdout>
total 336
drwxrwx--- 3 alfred.nguyen alfred.nguyen   318 Nov 13 18:05 .
drwxr-xr-x 9 alfred.nguyen alfred.nguyen   171 Nov 12 15:29 ..
</stdout>

# Assistant
``` bash
cat <<'EOF' > hello_world.py
print("Hello, World!")
EOF && cat -n hello_world.py
```

# User
<stdout>
     1  print("Hello, World!")
</stdout>

# Assistant
```bash
python hello_world.py
```

# User
<stdout>
Hello, World!
</stdout>
[tab] data/eval/handcrafted/palindrome.md (markdown)
# Assistant
``` bash
ls -la
```

# User
<stdout>
total 336
drwxrwx--- 3 alfred.nguyen alfred.nguyen   318 Nov 13 18:05 .
drwxr-xr-x 9 alfred.nguyen alfred.nguyen   171 Nov 12 15:29 ..
</stdout>

# Assistant
``` bash
cat <<'EOF' > palindrome_checker.py
def is_palindrome(text):
    text = text.lower().replace(" ", "")
    return text == text[::-1]

if __name__ == "__main__":
    import argparse
    parser = argparse.ArgumentParser(description="Check if text is a palindrome")
    parser.add_argument("text", help="Text to check")
    args = parser.parse_args()
    if is_palindrome(args.text):
        print(f'"{args.text}" is a palindrome')
    else:
        print(f'"{args.text}" is not a palindrome')
EOF && cat -n palindrome_checker.py
```

# User
<stdout>
     1  def is_palindrome(text):
     2      text = text.lower().replace(" ", "")
     3      return text == text[::-1]
     4 
     5  if __name__ == "__main__":
     6      import argparse
     7      parser = argparse.ArgumentParser(description="Check if text is a palindrome")
     8      parser.add_argument("text", help="Text to check")
     9      args = parser.parse_args()
    10      if is_palindrome(args.text):
    11          print(f'"{args.text}" is a palindrome')
    12      else:
    13          print(f'"{args.text}" is not a palindrome')
</stdout>

# Assistant
``` bash
python palindrome_checker.py racecar
```

# User
<stdout>
"racecar" is a palindrome
</stdout>

# Assistant
``` bash
python palindrome_checker.py hello
```

# User
<stdout>
"hello" is not a palindrome
</stdout>
[tab] data/eval/handcrafted/simple_calc_correct_error.md (markdown)

# Assistant
``` bash
ls -la
```

# User
<stdout>
total 336
drwxrwx--- 3 alfred.nguyen alfred.nguyen   318 Nov 13 18:05 .
drwxr-xr-x 9 alfred.nguyen alfred.nguyen   171 Nov 12 15:29 ..
-rw-rw---- 1 alfred.nguyen alfred.nguyen     0 Nov 13 18:05 calculator.py
</stdout>


# Assistant
``` bash
cat -n calculator.py
```

# User
<stdout>
     1  def add(a, b):
     2      return a + b
     3
     4  def subtract(a, b):
     5      return a - b
     6
     7  def multiply(a, b):
     8      return a * b
     9
     10  def divide(a, b):
     11      if b == 0:
     12          return "Error: Division by zero"
     13      return a // b
     14
     15  if __name__ == "__main__":
     16      import argparse
     17      parser = argparse.ArgumentParser(description="Simple calculator")
     18      parser.add_argument("operation", choices=["add", "subtract", "multiply", "divide"])
     19      parser.add_argument("a", type=float)
     20      parser.add_argument("b", type=float)
     21      args = parser.parse_args()
     22
     23      if args.operation == "add":
     24          result = add(args.a, args.b)
     25      elif args.operation == "subtract":
     26          result = subtract(args.a, args.b)
     27      elif args.operation == "multiply":
     28          result = multiply(args.a, args.b)
     29      else:
     30          result = divide(args.a, args.b)
     31
     32      print(result)
</stdout>

# Assistant
``` bash
python calculator.py add 5 3
```

# User
<stdout>
8.0
</stdout>

# Assistant
``` bash
python calculator.py multiply 4 7
```

# User
<stdout>
28.0
</stdout>

# Assistant
``` bash
python calculator.py divide 5 2
```

# User
<stdout>
2
</stdout>

# Assistant
``` bash
sed -i 's/^    return a \/\/ b/    return a \/ b/' calculator.py && cat -n calculator.py
```

# User
<stdout>
     1  def add(a, b):
     2      return a + b
     3
     4  def subtract(a, b):
     5      return a - b
     6
     7  def multiply(a, b):
     8      return a * b
     9
    10  def divide(a, b):
    11      if b == 0:
    12          return "Error: Division by zero"
    13      return a / b
    14
    15  if __name__ == "__main__":
    16      import argparse
    17      parser = argparse.ArgumentParser(description="Simple calculator")
    18      parser.add_argument("operation", choices=["add", "subtract", "multiply", "divide"])
    19      parser.add_argument("a", type=float)
    20      parser.add_argument("b", type=float)
    21      args = parser.parse_args()
    22
    23      if args.operation == "add":
    24          result = add(args.a, args.b)
    25      elif args.operation == "subtract":
    26          result = subtract(args.a, args.b)
    27      elif args.operation == "multiply":
    28          result = multiply(args.a, args.b)
    29      else:
    30          result = divide(args.a, args.b)
    31
    32      print(result)
</stdout>

# Assistant
``` bash
python calculator.py divide 5 2
```

# User
<stdout>
2.5
</stdout>
[tab] data/eval/handcrafted/simple_calc.md (markdown)
# User 
Create a simple calculator program that can add, subtract, multiply, and divide two numbers.

# Assistant
``` bash
cat <<'EOF' > calculator.py
def add(a, b):
    return a + b

def subtract(a, b):
    return a - b

def multiply(a, b):
    return a * b

def divide(a, b):
    if b == 0:
        return "Error: Division by zero"
    return a / b

if __name__ == "__main__":
    import argparse
    parser = argparse.ArgumentParser(description="Simple calculator")
    parser.add_argument("operation", choices=["add", "subtract", "multiply", "divide"])
    parser.add_argument("a", type=float)
    parser.add_argument("b", type=float)
    args = parser.parse_args()

    if args.operation == "add":
        result = add(args.a, args.b)
    elif args.operation == "subtract":
        result = subtract(args.a, args.b)
    elif args.operation == "multiply":
        result = multiply(args.a, args.b)
    else:
        result = divide(args.a, args.b)

    print(result)
EOF && cat -n calculator.py
```

# User
<stdout>
     1  def add(a, b):
     2      return a + b
     3
     4  def subtract(a, b):
     5      return a - b
     6
     7  def multiply(a, b):
     8      return a * b
     9
     10  def divide(a, b):
     11      if b == 0:
     12          return "Error: Division by zero"
     13      return a / b
     14
     15  if __name__ == "__main__":
     16      import argparse
     17      parser = argparse.ArgumentParser(description="Simple calculator")
     18      parser.add_argument("operation", choices=["add", "subtract", "multiply", "divide"])
     19      parser.add_argument("a", type=float)
     20      parser.add_argument("b", type=float)
     21      args = parser.parse_args()
     22
     23      if args.operation == "add":
     24          result = add(args.a, args.b)
     25      elif args.operation == "subtract":
     26          result = subtract(args.a, args.b)
     27      elif args.operation == "multiply":
     28          result = multiply(args.a, args.b)
     29      else:
     30          result = divide(args.a, args.b)
     31
     32      print(result)
</stdout>

# Assistant
``` bash
python calculator.py add 5 3
```

# User
<stdout>
8
</stdout>

# Assistant
``` bash
python calculator.py subtract 9 4
```

# User
<stdout>
5
</stdout>

# Assistant
``` bash
python calculator.py multiply 4 7
```

# User
<stdout>
28
</stdout>

# Assistant
``` bash
python calculator.py divide 5 2
```

# User
<stdout>
2.5
</stdout>