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README.md

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+## Setup
+### Installation
+```bash
+# create and activate conda environment
+conda create -n cua-lite python=3.10 -y
+conda activate cua-lite
+
+# install pytorch with CUDA 12.4 (other pytorch/cuda versions should also work)
+conda install cuda=12.4 -c nvidia
+pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 \
+    --index-url https://download.pytorch.org/whl/cu124
+
+# install cua-lite package
+pip install -e .
+```
+
+### (optional) Slurm setup
+For [Slurm](https://slurm.schedmd.com/) users, update [`scripts/train.slurm.sh`](/scripts/train.slurm.sh) for your cluster:
+```diff
+- #SBATCH --partition=mllm_safety # Note: adjust this for your cluster
+- #SBATCH --quotatype=spot        # Note: adjust this for your cluster
++ #SBATCH --partition=YOUR_PARTITION
++ #SBATCH --quotatype=YOUR_QUOTATYPE
+```
+Next, create a directory for your job logs:
+```shell
+mkdir logs
+```
+This folder will store the log files generated by your sbatch jobs.

pyproject.toml

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+[build-system]
+requires = ["setuptools>=61", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[tool.setuptools]
+packages = ["cua_lite"]
+
+[project]
+name = "cua-lite"
+version = "0.1.0"
+description = "cua-lite: Simple Computer Use Agents"
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = [
+    "transformers==4.57.0",
+    "accelerate==1.11.0",
+    "deepspeed==0.18.0",
+    "peft==0.17.1",
+    "datasets==4.2.0",
+    "sentencepiece==0.2.0",
+    "torchmetrics",
+    "tyro",
+    "wandb",
+    "omegaconf",
+    "tqdm",
+    "matplotlib",
+    "pytest",
+    "rich",
+]
+
+[project.optional-dependencies]
+optional = [
+    "bitsandbytes==0.48.1",
+    "vllm==0.8.5.post1",
+    "flash-attn==2.8.3",
+]
+
+[tool.black]
+line-length = 88
+
+[tool.pytest.ini_options]
+testpaths = ["scripts/tests"]
+python_files = ["test_*.py"]
+addopts = "-v -ra"

scripts/accelerate_configs/cpu.yaml

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+compute_environment: LOCAL_MACHINE
+distributed_type: NO
+mixed_precision: "no"
+num_processes: 1
+machine_rank: 0
+num_machines: 1
+downcast_bf16: "no"

scripts/accelerate_configs/ddp.yaml

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+compute_environment: LOCAL_MACHINE
+distributed_type: MULTI_GPU
+downcast_bf16: 'no'
+machine_rank: 0
+num_machines: 1
+num_processes: 8

scripts/accelerate_configs/fsdp.yaml

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+compute_environment: LOCAL_MACHINE
+debug: false
+distributed_type: FSDP
+downcast_bf16: 'no'
+enable_cpu_affinity: false
+fsdp_config:
+  fsdp_activation_checkpointing: false
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_backward_prefetch: BACKWARD_PRE
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_forward_prefetch: true
+  fsdp_offload_params: false
+  fsdp_sharding_strategy: FULL_SHARD
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_sync_module_states: true
+  fsdp_use_orig_params: true
+  fsdp_version: 1
+machine_rank: 0
+main_training_function: main
+mixed_precision: bf16
+num_machines: 1
+num_processes: 8
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false

scripts/accelerate_configs/fsdp2.yaml

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+# Requires accelerate 1.7.0 or higher
+compute_environment: LOCAL_MACHINE
+debug: false
+distributed_type: FSDP
+downcast_bf16: 'no'
+enable_cpu_affinity: false
+fsdp_config:
+  fsdp_activation_checkpointing: false
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_offload_params: false
+  fsdp_reshard_after_forward: true
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_version: 2
+machine_rank: 0
+main_training_function: main
+mixed_precision: bf16
+num_machines: 1
+num_processes: 8
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false

scripts/accelerate_configs/zero1.yaml

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+compute_environment: LOCAL_MACHINE
+debug: false
+deepspeed_config:
+  deepspeed_multinode_launcher: standard
+  zero3_init_flag: false
+  zero_stage: 1
+distributed_type: DEEPSPEED
+downcast_bf16: 'no'
+machine_rank: 0
+main_training_function: main
+# mixed_precision: 'bf16'
+num_machines: 1
+num_processes: 8
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false

scripts/accelerate_configs/zero2.yaml

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+compute_environment: LOCAL_MACHINE
+debug: false
+deepspeed_config:
+  deepspeed_multinode_launcher: standard
+  offload_optimizer_device: none
+  offload_param_device: none
+  zero3_init_flag: false
+  zero_stage: 2
+distributed_type: DEEPSPEED
+downcast_bf16: 'no'
+machine_rank: 0
+main_training_function: main
+# mixed_precision: 'bf16'
+num_machines: 1
+num_processes: 8
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false

scripts/accelerate_configs/zero3.yaml

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+compute_environment: LOCAL_MACHINE
+debug: false
+deepspeed_config:
+  deepspeed_multinode_launcher: standard
+  offload_optimizer_device: none
+  offload_param_device: none
+  zero3_init_flag: true
+  zero3_save_16bit_model: true
+  zero_stage: 3
+distributed_type: DEEPSPEED
+downcast_bf16: 'no'
+machine_rank: 0
+main_training_function: main
+# mixed_precision: bf16
+num_machines: 1
+num_processes: 8
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false

scripts/train.slurm.sh

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+#!/usr/bin/env bash
+#SBATCH --job-name=dllm
+#SBATCH --nodes=1
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=24
+#SBATCH --ntasks-per-node=1
+#SBATCH --partition=mllm_safety
+#SBATCH --quotatype=spot
+#SBATCH --output=./logs/%x-%j.out
+#SBATCH --err=./logs/%x-%j.err
+#SBATCH --requeue
+#SBATCH --time=3-00:00:00
+
+# ===== Cluster variables =====
+NUM_NODES=${SLURM_NNODES}
+GPUS_PER_NODE=$(echo "$CUDA_VISIBLE_DEVICES" | tr ',' '\n' | wc -l)
+WORLD_SIZE=$((NUM_NODES * GPUS_PER_NODE))
+NODELIST=($(scontrol show hostnames "${SLURM_JOB_NODELIST}"))
+MASTER_ADDR=${NODELIST[0]}
+MASTER_PORT=$((20000 + SLURM_JOB_ID % 10000))
+TRAIN_NODES=("${NODELIST[@]}")
+
+echo "===== System Variables ====="
+{
+  echo "NUM_NODES=$NUM_NODES"
+  echo "GPUS_PER_NODE=$GPUS_PER_NODE"
+  echo "WORLD_SIZE=$WORLD_SIZE"
+  echo "MASTER_ADDR=$MASTER_ADDR"
+  echo "MASTER_PORT=$MASTER_PORT"
+} | column -t -s=
+
+echo "Nodes allocated:"
+for node in "${TRAIN_NODES[@]}"; do
+  echo "  - $node"
+done
+echo "============================"
+
+# ===== Environment =====
+export NCCL_ASYNC_ERROR_HANDLING=1
+export PYTHONPATH=.
+
+# ===== Default options =====
+accelerate_config="zero2"
+script_path="scripts/examples/llada_sft.py"
+
+# ===== Parse arguments =====
+# Stop parsing known options as soon as we hit an unknown one
+FORWARD_ARGS=()
+while [[ $# -gt 0 ]]; do
+  case "$1" in
+    --accelerate_config)
+      accelerate_config="$2"; shift 2 ;;
+    --script_path)
+      script_path="$2"; shift 2 ;;
+    *)
+      FORWARD_ARGS=("$@"); break ;;  # everything else goes to the training script
+  esac
+done
+
+echo "===== Script Variables ====="
+echo "--accelerate_config ${accelerate_config}"
+echo "--script_path ${script_path}"
+echo "--forwarded script args:"
+printf '%s\n' "${FORWARD_ARGS[@]}" | xargs -n 2
+echo "============================"
+
+# ===== Launch =====
+srun --nodes="${NUM_NODES}" --ntasks="${NUM_NODES}" --nodelist="${SLURM_JOB_NODELIST}" \
+  accelerate launch \
+    --config_file "scripts/accelerate_configs/${accelerate_config}.yaml" \
+    --num_machines "${NUM_NODES}" \
+    --num_processes "${WORLD_SIZE}" \
+    --main_process_ip "${MASTER_ADDR}" \
+    --main_process_port "${MASTER_PORT}" \
+    --machine_rank "${SLURM_PROCID}" \
+    --rdzv_backend c10d \
+    "${script_path}" "${FORWARD_ARGS[@]}"