Use FSDP to fine-tune Llama 4 on an A4 Slurm cluster

Access Llama 4 by using Hugging Face

To use Hugging Face to access Llama 4, do the following:

1. Sign the consent agreement to use Llama 4 .

2. Create a Hugging Face read access token .

   Click Your Profile > Settings > Access tokens > +Create new token

3. Copy and save the read access token value. You use it later in this tutorial.

Prepare your environment

To prepare your environment, follow these steps:

1. Clone the Cluster Toolkit GitHub repository:

    git  
   clone  
   https://github.com/GoogleCloudPlatform/cluster-toolkit.git 
   

2. Create a Cloud Storage bucket:

    gcloud  
   storage  
   buckets  
   create  
   gs:// BUCKET_NAME 
     
    \ 
     
   --project = 
    PROJECT_ID 
    
   

   Replace the following:

   • BUCKET_NAME : a name for your Cloud Storage bucket that follows bucket naming requirements .

   • PROJECT_ID : the ID of the Google Cloud project where you want to create your Cloud Storage bucket.

Create an A4 Slurm cluster

To create an A4 Slurm cluster, follow these steps:

1. Go to the cluster-toolkit directory:

     cd 
     
   cluster-toolkit 
   

2. If it's your first time using Cluster Toolkit, then build the gcluster binary:

    make 
   

3. Go to the examples/machine-learning/a4-highgpu-8g directory:

     cd 
     
   examples/machine-learning/a4-highgpu-8g/ 
   

4. Open the a4high-slurm-deployment.yaml file, and then edit it as follows:

     terraform_backend_defaults 
    : 
     
    type 
    : 
     
    gcs 
     
    configuration 
    : 
     
    bucket 
    : 
     
     BUCKET_NAME 
    
    vars 
    : 
     
    deployment_name 
    : 
     
    a4-high 
     
    project_id 
    : 
     
     PROJECT_ID 
    
     
    region 
    : 
     
     REGION 
    
     
    zone 
    : 
     
     ZONE 
    
     
    a4h_cluster_size 
    : 
     
    2 
     
    a4h_reservation_name 
    : 
     
     RESERVATION_URL 
    
    
   

   Replace the following:

   • BUCKET_NAME : the name of the Cloud Storage bucket that you created in the previous section.

   • PROJECT_ID : the ID of the Google Cloud project where your Cloud Storage exists and where you want to create your Slurm cluster.

   • REGION : the region where your reservation exists.

   • ZONE : the zone where your reservation exists.

   • RESERVATION_URL : the URL of the reservation that you want to use to create your Slurm cluster. Based on the project in which the reservation exists, specify one of the following values:

     • The reservation exists in your project: RESERVATION_NAME

     • The reservation exists in a different project, and your project can use the reservation: projects/ RESERVATION_PROJECT_ID /reservations/ RESERVATION_NAME

5. Deploy the cluster:

    ./gcluster  
   deploy  
   -d  
   examples/machine-learning/a4-highgpu-8g/a4high-slurm-deployment.yaml  
   examples/machine-learning/a4-highgpu-8g/a4high-slurm-blueprint.yaml  
   --auto-approve 
   

   The ./gcluster deploy command is a two-phase process, which is as follows:

   • The first phase builds a custom image with all software pre-installed, which can take up to 35 minutes to complete.

   • The second phase deploys the cluster by using that custom image. This process should complete more quickly than the first phase.

   If the first phase succeeds but the second phase fails, then you can try to deploy the Slurm cluster again by skipping the first phase:

    ./gcluster  
   deploy  
   -d  
   examples/machine-learning/a4-highgpu-8g/a4high-slurm-deployment.yaml  
   examples/machine-learning/a4-highgpu-8g/a4high-slurm-blueprint.yaml  
   --auto-approve  
   --skip  
    "image" 
     
   -w 
   

Prepare your workload

To prepare your workload, you do the following:

1. Create workload scripts .

2. Upload scripts to the Slurm cluster .

3. Connect to the Slurm cluster .

4. Install frameworks and tools .

Create workload scripts

To create the scripts that your fine-tuning workload will use, follow these steps:

1. To set up the Python virtual environment, create the install_environment.sh file with the following content:

     #!/bin/bash 
    # This script sets up a consistent environment for FSDP training. 
    # It is meant to be run once on the login node of your Slurm cluster 
    set 
     
   -e # --- 1. Create the Python virtual environment --- 
    VENV_PATH 
    = 
    " 
    $HOME 
    /.venv/venv-fsdp" 
    if 
     
    [ 
     
   !  
   -d  
    " 
    $VENV_PATH 
    " 
     
    ] 
    ; 
     
    then 
     
    echo 
     
    "--- Creating Python virtual environment at 
    $VENV_PATH 
    ---" 
     
   python3  
   -m  
   venv  
    $VENV_PATH 
    else 
     
    echo 
     
    "--- Virtual environment already exists at 
    $VENV_PATH 
    ---" 
    fi 
    source 
     
    $VENV_PATH 
   /bin/activate # --- 2. Install Dependencies --- 
    echo 
     
    "--- [STEP 2.1] Upgrading build toolchain ---" 
   pip  
   install  
   --upgrade  
   pip  
   wheel  
   packaging echo 
     
    "--- [STEP 2.2] Installing PyTorch Nightly ---" 
   pip  
   install  
   --force-reinstall  
   --pre  
   torch  
   torchvision  
   torchaudio  
   --index-url  
   https://download.pytorch.org/whl/nightly/cu128 echo 
     
    "--- [STEP 2.3] Installing application dependencies ---" 
    if 
     
    [ 
     
   -f  
    "requirements-fsdp.txt" 
     
    ] 
    ; 
     
    then 
     
   pip  
   install  
   -r  
   requirements-fsdp.txt else 
     
    echo 
     
    "ERROR: requirements-fsdp.txt not found!" 
     
    exit 
     
    1 
    fi 
    # --- 3. Download the Model --- 
    echo 
     
    "--- [STEP 2.4] Downloading Llama4 model ---" 
    if 
     
    [ 
     
   -z  
    " 
    $HF_TOKEN 
    " 
     
    ] 
    ; 
     
    then 
     
    echo 
     
    "ERROR: The HF_TOKEN environment variable is not set." 
    ; 
     
    exit 
     
    1 
    ; 
    fi 
   pip  
   install  
   huggingface_hub [ 
   cli ] 
    # Execute the CLI using its full, explicit path 
    $VENV_PATH 
   /bin/huggingface-cli  
   download  
   meta-llama/Llama-4-Scout-17B-16E-Instruct  
   --local-dir  
   ~/Llama-4-Scout-17B-16E-Instruct  
   --token  
    $HF_TOKEN 
    echo 
     
    "--- Environment setup complete. ---" 
    
   

   This script sets up a reliable Python virtual environment, installs a PyTorch nightly build, and downloads the Llama 4 model.

2. To specify the Python dependencies for the training script, create a requirements-fsdp.txt file with the following content:

    transformers==4.55.0
   datasets==4.0.0
   peft==0.16.0
   accelerate==1.9.0
   trl==0.21.0
   
   # Other dependencies
   sentencepiece==0.2.0 
   

3. Specify llama4-train-distributed.py as the main training script:

     import 
     
    torch 
    from 
     
    datasets 
     
    import 
    load_dataset 
    from 
     
    peft 
     
    import 
    LoraConfig 
    , 
    PeftModel 
    from 
     
    transformers 
     
    import 
    ( 
    AutoModelForCausalLM 
    , 
    AutoTokenizer 
    , 
    TrainingArguments 
    , 
    HfArgumentParser 
    , 
    ) 
    from 
     
    torch.distributed 
     
    import 
    get_rank 
    , 
    get_world_size 
    from 
     
    transformers.models.llama4.modeling_llama4 
     
    import 
    Llama4TextDecoderLayer 
    from 
     
    trl 
     
    import 
    SFTTrainer 
    from 
     
    dataclasses 
     
    import 
    dataclass 
    , 
    field 
    from 
     
    typing 
     
    import 
    Optional 
    @dataclass 
    class 
     
    ScriptArguments 
    : 
    model_id 
    : 
    str 
    = 
    field 
    ( 
    metadata 
    = 
    { 
    "help" 
    : 
    "Hugging Face model ID from the Hub" 
    }) 
    dataset_name 
    : 
    str 
    = 
    field 
    ( 
    default 
    = 
    "philschmid/gretel-synthetic-text-to-sql" 
    , 
    metadata 
    = 
    { 
    "help" 
    : 
    "Dataset from the Hub" 
    }) 
    run_inference_after_training 
    : 
    bool 
    = 
    field 
    ( 
    default 
    = 
    False 
    , 
    metadata 
    = 
    { 
    "help" 
    : 
    "Run sample inference on rank 0 after training" 
    }) 
    dataset_subset_size 
    : 
    Optional 
    [ 
    int 
    ] 
    = 
    field 
    ( 
    default 
    = 
    None 
    , 
    metadata 
    = 
    { 
    "help" 
    : 
    "Number of samples to use from the dataset for training. If None, uses the full dataset." 
    }) 
    @dataclass 
    class 
     
    PeftArguments 
    : 
    lora_r 
    : 
    int 
    = 
    field 
    ( 
    default 
    = 
    16 
    , 
    metadata 
    = 
    { 
    "help" 
    : 
    "LoRA attention dimension" 
    }) 
    lora_alpha 
    : 
    int 
    = 
    field 
    ( 
    default 
    = 
    32 
    , 
    metadata 
    = 
    { 
    "help" 
    : 
    "LoRA alpha scaling factor" 
    }) 
    lora_dropout 
    : 
    float 
    = 
    field 
    ( 
    default 
    = 
    0.05 
    , 
    metadata 
    = 
    { 
    "help" 
    : 
    "LoRA dropout probability" 
    }) 
    @dataclass 
    class 
     
    SftTrainingArguments 
    ( 
    TrainingArguments 
    ): 
    max_length 
    : 
    Optional 
    [ 
    int 
    ] 
    = 
    field 
    ( 
    default 
    = 
    2048 
    , 
    metadata 
    = 
    { 
    "help" 
    : 
    "The maximum sequence length for SFTTrainer" 
    }) 
    packing 
    : 
    Optional 
    [ 
    bool 
    ] 
    = 
    field 
    ( 
    default 
    = 
    False 
    , 
    metadata 
    = 
    { 
    "help" 
    : 
    "Enable packing for SFTTrainer" 
    }) 
    ddp_find_unused_parameters 
    : 
    Optional 
    [ 
    bool 
    ] 
    = 
    field 
    ( 
    default 
    = 
    True 
    , 
    metadata 
    = 
    { 
    "help" 
    : 
    "When using FSDP activation checkpointing, this must be set to True" 
    }) 
    def 
     
    formatting_prompts_func 
    ( 
    example 
    ): 
    system_message 
    = 
    "You are a text to SQL query translator. Users will ask you questions in English and you will generate a SQL query based on the provided SCHEMA." 
    user_prompt 
    = 
    f 
    "### SCHEMA: 
    \n 
    { 
    example 
    [ 
    'sql_context' 
    ] 
    } 
    \n\n 
    ### USER QUERY: 
    \n 
    { 
    example 
    [ 
    'sql_prompt' 
    ] 
    } 
    " 
    response 
    = 
    f 
    " 
    \n\n 
    ### SQL QUERY: 
    \n 
    { 
    example 
    [ 
    'sql' 
    ] 
    } 
    " 
    return 
    f 
    " 
    { 
    system_message 
    } 
    \n\n 
    { 
    user_prompt 
    }{ 
    response 
    } 
    " 
    def 
     
    main 
    (): 
    parser 
    = 
    HfArgumentParser 
    (( 
    ScriptArguments 
    , 
    PeftArguments 
    , 
    SftTrainingArguments 
    )) 
    script_args 
    , 
    peft_args 
    , 
    training_args 
    = 
    parser 
    . 
    parse_args_into_dataclasses 
    () 
    training_args 
    . 
    gradient_checkpointing 
    = 
    True 
    training_args 
    . 
    gradient_checkpointing_kwargs 
    = 
    { 
    "use_reentrant" 
    : 
    False 
    } 
    training_args 
    . 
    optim 
    = 
    "adamw_torch_fused" 
    training_args 
    . 
    fsdp 
    = 
    "full_shard" 
    training_args 
    . 
    fsdp_config 
    = 
    { 
    "fsdp_auto_wrap_policy" 
    : 
    "TRANSFORMER_BASED_WRAP" 
    , 
    "fsdp_transformer_layer_cls_to_wrap" 
    : 
    [ 
    Llama4TextDecoderLayer 
    ], 
    "fsdp_state_dict_type" 
    : 
    "FULL_STATE_DICT" 
    , 
    "fsdp_offload_params" 
    : 
    False 
    , 
    "fsdp_forward_prefetch" 
    : 
    True 
    , 
    } 
    tokenizer 
    = 
    AutoTokenizer 
    . 
    from_pretrained 
    ( 
    script_args 
    . 
    model_id 
    , 
    trust_remote_code 
    = 
    True 
    ) 
    model 
    = 
    AutoModelForCausalLM 
    . 
    from_pretrained 
    ( 
    script_args 
    . 
    model_id 
    , 
    torch_dtype 
    = 
    torch 
    . 
    bfloat16 
    , 
    trust_remote_code 
    = 
    True 
    , 
    attn_implementation 
    = 
    "sdpa" 
    , 
    ) 
    peft_config 
    = 
    LoraConfig 
    ( 
    r 
    = 
    peft_args 
    . 
    lora_r 
    , 
    lora_alpha 
    = 
    peft_args 
    . 
    lora_alpha 
    , 
    lora_dropout 
    = 
    peft_args 
    . 
    lora_dropout 
    , 
    bias 
    = 
    "none" 
    , 
    task_type 
    = 
    "CAUSAL_LM" 
    , 
    target_modules 
    = 
    [ 
    "q_proj" 
    , 
    "v_proj" 
    , 
    "k_proj" 
    , 
    "o_proj" 
    , 
    "gate_proj" 
    , 
    "up_proj" 
    , 
    "down_proj" 
    ], 
    ) 
    rank 
    = 
    get_rank 
    () 
    world_size 
    = 
    get_world_size 
    () 
    dataset 
    = 
    load_dataset 
    ( 
    script_args 
    . 
    dataset_name 
    , 
    split 
    = 
    "train" 
    ) 
    if 
    script_args 
    . 
    dataset_subset_size 
    is 
    not 
    None 
    : 
    dataset 
    = 
    dataset 
    . 
    select 
    ( 
    range 
    ( 
    script_args 
    . 
    dataset_subset_size 
    )) 
    else 
    : 
    print 
    ( 
    f 
    "Using the full dataset with 
    { 
    len 
    ( 
    dataset 
    ) 
    } 
    samples." 
    ) 
    dataset 
    = 
    dataset 
    . 
    shuffle 
    ( 
    seed 
    = 
    training_args 
    . 
    seed 
    ) 
    print 
    ( 
    f 
    "Dataset shuffled with seed: 
    { 
    training_args 
    . 
    seed 
    } 
    ." 
    ) 
    if 
    world_size 
   > 1 
    : 
    print 
    ( 
    f 
    "Sharding dataset for Rank 
    { 
    rank 
    } 
    of 
    { 
    world_size 
    } 
    ." 
    ) 
    dataset 
    = 
    dataset 
    . 
    shard 
    ( 
    num_shards 
    = 
    world_size 
    , 
    index 
    = 
    rank 
    ) 
    print 
    ( 
    "Initializing SFTTrainer..." 
    ) 
    trainer 
    = 
    SFTTrainer 
    ( 
    model 
    = 
    model 
    , 
    args 
    = 
    training_args 
    , 
    train_dataset 
    = 
    dataset 
    , 
    peft_config 
    = 
    peft_config 
    , 
    formatting_func 
    = 
    formatting_prompts_func 
    , 
    processing_class 
    = 
    tokenizer 
    , 
    ) 
    trainer 
    . 
    train 
    () 
    trainer 
    . 
    save_model 
    ( 
    training_args 
    . 
    output_dir 
    ) 
    if 
    script_args 
    . 
    run_inference_after_training 
    and 
    trainer 
    . 
    is_world_process_zero 
    (): 
    del 
    model 
    del 
    trainer 
    torch 
    . 
    cuda 
    . 
    empty_cache 
    () 
    run_post_training_inference 
    ( 
    script_args 
    , 
    training_args 
    , 
    tokenizer 
    ) 
    def 
     
    run_post_training_inference 
    ( 
    script_args 
    , 
    training_args 
    , 
    tokenizer 
    ): 
     
    """ 
    Loads the fine-tuned PEFT adapter from the local output directory and runs inference. 
    This should only be called on rank 0 after training is complete. 
    """ 
    print 
    ( 
    " 
    \n 
    " 
    + 
    "=" 
    * 
    50 
    ) 
    print 
    ( 
    "=== RUNNING POST-TRAINING INFERENCE TEST ===" 
    ) 
    print 
    ( 
    "=" 
    * 
    50 
    + 
    " 
    \n 
    " 
    ) 
    # Load the base model and merge the adapter. 
    base_model 
    = 
    AutoModelForCausalLM 
    . 
    from_pretrained 
    ( 
    script_args 
    . 
    model_id 
    , 
    torch_dtype 
    = 
    torch 
    . 
    bfloat16 
    , 
    trust_remote_code 
    = 
    True 
    , 
    device_map 
    = 
    "auto" 
    ) 
    # Load the PEFT adapter and merge it into the base model 
    model 
    = 
    PeftModel 
    . 
    from_pretrained 
    ( 
    base_model 
    , 
    training_args 
    . 
    output_dir 
    ) 
    model 
    = 
    model 
    . 
    merge_and_unload 
    () 
    # Merge weights for faster inference 
    model 
    . 
    eval 
    () 
    # Define the test case 
    schema 
    = 
    "CREATE TABLE artists (Name TEXT, Country TEXT, Genre TEXT)" 
    system_message 
    = 
    "You are a text to SQL query translator. Users will ask you questions in English and you will generate a SQL query based on the provided SCHEMA." 
    question 
    = 
    "Show me all artists from the Country just north of the USA." 
    # This must match the formatting_func exactly 
    prompt 
    = 
    f 
    " 
    { 
    system_message 
    } 
    \n\n 
    ### SCHEMA: 
    \n 
    { 
    schema 
    } 
    \n\n 
    ### USER QUERY: 
    \n 
    { 
    question 
    } 
    \n\n 
    ### SQL QUERY: 
    \n 
    " 
    print 
    ( 
    f 
    "Test Prompt: 
    \n 
    { 
    prompt 
    } 
    " 
    ) 
    inputs 
    = 
    tokenizer 
    ( 
    prompt 
    , 
    return_tensors 
    = 
    "pt" 
    ) 
    . 
    to 
    ( 
    "cuda" 
    ) 
    print 
    ( 
    " 
    \n 
    --- Generating SQL... ---" 
    ) 
    outputs 
    = 
    model 
    . 
    generate 
    ( 
    ** 
    inputs 
    , 
    max_new_tokens 
    = 
    100 
    , 
    pad_token_id 
    = 
    tokenizer 
    . 
    eos_token_id 
    , 
    do_sample 
    = 
    False 
    , 
    temperature 
    = 
    None 
    , 
    top_p 
    = 
    None 
    , 
    ) 
    generated_sql 
    = 
    tokenizer 
    . 
    decode 
    ( 
    outputs 
    [ 
    0 
    ], 
    skip_special_tokens 
    = 
    True 
    )[ 
    len 
    ( 
    prompt 
    ):] 
    . 
    strip 
    () 
    print 
    ( 
    f 
    " 
    \n 
    --- Generated SQL Query ---" 
    ) 
    print 
    ( 
    generated_sql 
    ) 
    print 
    ( 
    " 
    \n 
    " 
    + 
    "=" 
    * 
    50 
    ) 
    print 
    ( 
    "=== INFERENCE TEST COMPLETE ===" 
    ) 
    print 
    ( 
    "=" 
    * 
    50 
    + 
    " 
    \n 
    " 
    ) 
    if 
    __name__ 
    == 
    "__main__" 
    : 
    main 
    () 
    
   

   This script utilizes the TRL Supervised Fine-Tuning (SFT) Trainer to manage FSDP training loops, Low-Rank Adaptation (LoRA) configuration, and data formatting.

4. To specify the tasks for the jobs to run on your Slurm cluster, create the submit.slurm file with the following content:

     #!/bin/bash 
    #SBATCH --job-name=llama4-fsdp-fixed 
    #SBATCH --nodes=2 
    #SBATCH --ntasks-per-node=8 
    #SBATCH --gpus-per-node=8 
    #SBATCH --partition=a4high 
    #SBATCH --output=llama4-%j.out 
    #SBATCH --error=llama4-%j.err 
    set 
     
   -e set 
     
   -x echo 
     
    "--- Slurm Job Started ---" 
    echo 
     
    "Job ID: 
    $SLURM_JOB_ID 
    " 
    echo 
     
    "Node List: 
    $SLURM_JOB_NODELIST 
    " 
    # --- Define Paths --- 
    LOCAL_SSD_PATH 
    = 
    "/mnt/localssd/job_ 
    ${ 
    SLURM_JOB_ID 
    } 
    " 
    VENV_PATH 
    = 
    " 
    ${ 
    HOME 
    } 
    /.venv/venv-fsdp" 
    MODEL_PATH 
    = 
    " 
    ${ 
    HOME 
    } 
    /Llama-4-Scout-17B-16E-Instruct" 
    # --- STAGE 1: Stage Data to Local SSD on Each Node --- 
   srun  
   --ntasks = 
    $SLURM_NNODES 
     
   --ntasks-per-node = 
    1 
     
   bash  
   -c  
    " 
    echo '--- Staging on node: 
    $( 
   hostname ) 
    ---' 
    mkdir -p 
    ${ 
    LOCAL_SSD_PATH 
    } 
    echo 'Copying virtual environment...' 
    rsync -a -q 
    ${ 
    VENV_PATH 
    } 
    / 
    ${ 
    LOCAL_SSD_PATH 
    } 
    /venv/ 
    echo 'Copying model weights...' 
    rsync -a --info=progress2 
    ${ 
    MODEL_PATH 
    } 
    / 
    ${ 
    LOCAL_SSD_PATH 
    } 
    /model/ 
    mkdir -p 
    ${ 
    LOCAL_SSD_PATH 
    } 
    /hf_cache 
    echo '--- Staging on 
    $( 
   hostname ) 
    complete ---' 
    " 
    echo 
     
    "--- Staging complete on all nodes ---" 
    # --- STAGE 2: Run the Training Job --- 
    echo 
     
    "--- Launching Distributed Training with GIB NCCL Plugin ---" 
    nodes 
    =( 
     
    $( 
     
   scontrol  
   show  
   hostnames  
    " 
    $SLURM_JOB_NODELIST 
    " 
     
    ) 
     
    ) 
    head_node 
    = 
    ${ 
    nodes 
    [0] 
    } 
    head_node_ip 
    = 
    $( 
   srun  
   --nodes = 
    1 
     
   --ntasks = 
    1 
     
   -w  
    " 
    $head_node 
    " 
     
   hostname  
   --ip-address ) 
    export 
     
    MASTER_ADDR 
    = 
    $head_node_ip 
    export 
     
    MASTER_PORT 
    = 
    29500 
    export 
     
    NCCL_SOCKET_IFNAME 
    = 
   enp0s19 export 
     
    NCCL_NET 
    = 
   gIB # export NCCL_DEBUG=INFO # Un-comment to diagnose NCCL issues if needed 
   srun  
   --cpu-bind = 
   none  
   --accel-bind = 
   g  
   bash  
   -c  
    ' 
    # Activate the environment from the local copy 
    source ' 
    ${ 
    LOCAL_SSD_PATH 
    } 
    '/venv/bin/activate 
    # Point Hugging Face cache to the local SSD 
    export HF_HOME=' 
    ${ 
    LOCAL_SSD_PATH 
    } 
    '/hf_cache 
    export RANK=$SLURM_PROCID 
    export WORLD_SIZE=$SLURM_NTASKS 
    export LOCAL_RANK=$SLURM_LOCALID 
    export LD_LIBRARY_PATH=/usr/local/gib/lib64:$LD_LIBRARY_PATH 
    source /usr/local/gib/scripts/set_nccl_env.sh 
    # --- Launch the training --- 
    python \ 
    ' 
    ${ 
    SLURM_SUBMIT_DIR 
    } 
    '/llama4-train-distributed.py \ 
    --model_id="' 
    ${ 
    LOCAL_SSD_PATH 
    } 
    '/model/" \ 
    --output_dir="' 
    ${ 
    LOCAL_SSD_PATH 
    } 
    '/outputs/" \ 
    --dataset_name="philschmid/gretel-synthetic-text-to-sql" \ 
    --seed=900913 \ 
    --bf16=True \ 
    --num_train_epochs=1 \ 
    --per_device_train_batch_size=2 \ 
    --gradient_accumulation_steps=4 \ 
    --learning_rate=2e-5 \ 
    --logging_steps=10 \ 
    --lora_r=16 \ 
    --lora_alpha=32 \ 
    --lora_dropout=0.05 \ 
    --run_inference_after_training 
    ' 
    # --- STAGE 3: Copy Final Results Back to Persistent Storage --- 
    echo 
     
    "--- Copying final results from local SSD to shared storage ---" 
    PERSISTENT_OUTPUT_DIR 
    = 
    " 
    ${ 
    HOME 
    } 
    /outputs/llama4_job_ 
    ${ 
    SLURM_JOB_ID 
    } 
    " 
   mkdir  
   -p  
    " 
    $PERSISTENT_OUTPUT_DIR 
    " 
    # Only copy from the head node where trl has combined the results 
   srun  
   --nodes = 
    1 
     
   --ntasks = 
    1 
     
   -w  
    " 
    $head_node 
    " 
     
    \ 
     
   rsync  
   -a  
   --info = 
   progress2  
    " 
    ${ 
    LOCAL_SSD_PATH 
    } 
    /outputs/" 
     
    " 
    ${ 
    PERSISTENT_OUTPUT_DIR 
    } 
    /" 
    # --- STAGE 4: Cleanup --- 
    echo 
     
    "--- Cleaning up local SSD on all nodes ---" 
   srun  
   --ntasks = 
    $SLURM_NNODES 
     
   --ntasks-per-node = 
    1 
     
   bash  
   -c  
    "rm -rf 
    ${ 
    LOCAL_SSD_PATH 
    } 
    " 
    echo 
     
    "--- Slurm Job Finished ---" 
    
   

Upload scripts to the Slurm cluster

To upload the scripts that you created in the previous section to the Slurm cluster, follow these steps:

1. To identify your login node, list all A4 VMs in your project:

    gcloud  
   compute  
   instances  
   list  
   --filter = 
    "machineType:a4-highgpu-8g" 
    
   

   The name of the login node is similar to a4-high-login-001 .

2. Upload your scripts to the login node's home directory:

    gcloud  
   compute  
   scp  
   --project = 
    " 
    $PROJECT_ID 
    " 
     
   --zone = 
    " 
    $ZONE 
    " 
     
   --tunnel-through-iap  
    \ 
     
   ./install_environment.sh  
    \ 
     
   ./requirements-fsdp.txt  
    \ 
     
   ./llama4-train-distributed.py  
    \ 
     
   ./submit.slurm  
    \ 
     
    " 
    ${ 
    LOGIN_NODE_NAME 
    } 
    " 
   :~/ 
   

   Replace LOGIN_NODE_NAME with the name of the login node.

Connect to the Slurm cluster

Connect to the Slurm cluster by connecting to the login node through SSH:

 gcloud  
compute  
ssh  
 LOGIN_NODE_NAME 
  
 \ 
  
--project = 
 PROJECT_ID 
  
 \ 
  
--tunnel-through-iap  
 \ 
  
--zone = 
 ZONE 
 

Install frameworks and tools

After you connect to the login node, install frameworks and tools by doing the following:

1. Export your Hugging Face token:

     # On the login node 
    export 
     
    HF_TOKEN 
    = 
    "hf_..." 
     
    # Replace with your token 
    
   

2. Run the installation script:

     # On the login node 
   chmod  
   +x  
   install_environment.sh
   ./install_environment.sh 
   

   This command sets up a virtual environment with all the required dependencies, and downloads the model weights into the ~/Llama-4-Scout-17B-16E-Instruct file.

   Because the model download is very large (~200 GB), this process takes around 30 minutes, depending on network conditions.

Start your fine-tuning workload

To start training your workload, do the following:

1. Submit the job to the Slurm scheduler:

    sbatch  
   submit.slurm 
   

2. On the login node in your Slurm cluster, you can monitor the job's progress by checking the output files created in your home directory:

     # On the login node 
   tail  
   -f  
   llama4-*.out 
   

   If your job successfully starts, then the .err file shows a progress bar that updates as your job progresses.

   This job should take a bit over an hour to complete on the Slurm Cluster. The job has two main phases:

   • Copying the large base model to the local SSD of each compute node.
   • The training job, which begins once the copying of the model is complete. This job takes about 35 minutes to run.