diff --git a/docs/torchft.md b/docs/torchft.md
index 68733ec1ec..b39eddb6db 100644
--- a/docs/torchft.md
+++ b/docs/torchft.md
@@ -68,12 +68,12 @@ The `--training.global_batch_size` parameter refers to global batch size that wi
 
 #### Replica Group 0
 ```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 NGPU=4 ./run_train.sh --parallelism.data_parallel_shard_degree=4 --fault_tolerance.enable --fault_tolerance.group_size=2 --fault_tolerance.replica_id=0 --fault_tolerance.semi_sync_method="diloco" --experimental.custom_args_module=torchtitan.components.ft.config
+CONFIG_FILE=./torchtitan/models/llama3_ft/train_configs/debug_model.toml CUDA_VISIBLE_DEVICES=0,1,2,3 NGPU=4 ./run_train.sh --parallelism.data_parallel_shard_degree=4 --fault_tolerance.enable --fault_tolerance.group_size=2 --fault_tolerance.replica_id=0
 ```
 
 #### Replica Group 1
 ```bash
-CUDA_VISIBLE_DEVICES=4,5,6,7 NGPU=4 ./run_train.sh --parallelism.data_parallel_shard_degree=4 --fault_tolerance.enable --fault_tolerance.group_size=2 --fault_tolerance.replica_id=1 --fault_tolerance.semi_sync_method="diloco" --experimental.custom_args_module=torchtitan.components.ft.config
+CONFIG_FILE=./torchtitan/models/llama3_ft/train_configs/debug_model.toml CUDA_VISIBLE_DEVICES=4,5,6,7 NGPU=4 ./run_train.sh --parallelism.data_parallel_shard_degree=4 --fault_tolerance.enable --fault_tolerance.group_size=2 --fault_tolerance.replica_id=1
 ```
 
 ## Fault Tolerance Configuration Options
diff --git a/torchtitan/config/job_config.py b/torchtitan/config/job_config.py
index eb477941ca..138672c739 100644
--- a/torchtitan/config/job_config.py
+++ b/torchtitan/config/job_config.py
@@ -34,6 +34,20 @@ class Profiling:
     profile_freq: int = 10
     """How often to collect profile traces, in iterations"""
 
+    profiler_active: int = 1
+    """
+    The steps profiler is active for.
+
+    This is used to configure torch.profile.schedule.
+    """
+
+    profiler_warmup: int = 3
+    """
+    The number of warmup steps before the active step in each profiling cycle.
+
+    This is used to configure torch.profile.schedule.
+    """
+
     enable_memory_snapshot: bool = False
     """Whether to dump memory snapshot"""
 
diff --git a/torchtitan/models/llama3_ft/train_configs/debug_model.toml b/torchtitan/models/llama3_ft/train_configs/debug_model.toml
new file mode 100644
index 0000000000..000af2ac06
--- /dev/null
+++ b/torchtitan/models/llama3_ft/train_configs/debug_model.toml
@@ -0,0 +1,95 @@
+[job]
+dump_folder = "./outputs"
+description = "Llama 3 debug training"
+print_args = false
+
+[profiling]
+enable_profiling = true
+save_traces_folder = "profile_trace"
+profile_freq = 10
+profiler_active = 10
+profiler_warmup = 0
+enable_memory_snapshot = false
+save_memory_snapshot_folder = "memory_snapshot"
+
+[metrics]
+log_freq = 1
+disable_color_printing = false
+enable_tensorboard = false
+save_tb_folder = "tb"
+enable_wandb = false
+
+[model]
+name = "llama3"
+flavor = "debugmodel"
+# test folder with tokenizer.json, for debug purpose only
+hf_assets_path = "./tests/assets/tokenizer"
+# converters = ["float8"]
+
+[optimizer]
+name = "AdamW"
+lr = 8e-4
+eps = 1e-8
+
+[lr_scheduler]
+warmup_steps = 2  # lr scheduler warm up, normally 20% of the train steps
+decay_ratio = 0.8  # lr scheduler decay ratio, 80% of the train steps
+decay_type = "linear"
+min_lr_factor = 0.0
+
+[training]
+local_batch_size = 8
+seq_len = 2048
+max_norm = 1.0  # grad norm clipping
+steps = 100
+dataset = "c4_test"  # supported datasets: c4_test (2K), c4 (177M)
+
+[parallelism]
+data_parallel_replicate_degree = 1
+data_parallel_shard_degree = -1
+fsdp_reshard_after_forward = "default"  # default / never / always
+tensor_parallel_degree = 1
+enable_async_tensor_parallel = false
+pipeline_parallel_degree = 1
+context_parallel_degree = 1
+
+[checkpoint]
+enable = false
+folder = "checkpoint"
+interval = 10
+last_save_model_only = false
+export_dtype = "float32"
+async_mode = "disabled"  # ["disabled", "async", "async_with_pinned_mem"]
+
+[activation_checkpoint]
+mode = "selective"  # ["none", "selective", "full"]
+selective_ac_option = "2"  # 'int' = ac every positive int layer or 'op', ac based on ops policy
+
+[compile]
+enable = false
+components = ["model", "loss"]
+
+[quantize.linear.float8]
+enable_fsdp_float8_all_gather = false
+precompute_float8_dynamic_scale_for_fsdp = false
+filter_fqns = ["output"]
+
+[validation]
+enable = false
+dataset = "c4_validation"
+freq = 5
+steps = 10
+
+[comm]
+train_timeout_seconds = 15
+
+[fault_tolerance]
+enable = true
+sync_steps = 10
+num_fragments = 2
+semi_sync_method = "diloco"
+process_group = "nccl"
+process_group_timeout_ms = 10000
+
+[experimental]
+custom_args_module = "torchtitan.components.ft.config"
diff --git a/torchtitan/tools/profiling.py b/torchtitan/tools/profiling.py
index 0e851d335a..f398dba9b5 100644
--- a/torchtitan/tools/profiling.py
+++ b/torchtitan/tools/profiling.py
@@ -14,9 +14,6 @@
 from torchtitan.config import Profiling as ProfilingConfig
 from torchtitan.tools.logging import logger
 
-# the number of warmup steps before the active step in each profiling cycle
-WARMUP = 3
-
 # how much memory allocation/free ops to record in memory snapshots
 MEMORY_SNAPSHOT_MAX_ENTRIES = 100000
 
@@ -34,7 +31,11 @@ def maybe_enable_profiling(
 
     if enable_profiling:
         trace_dir = os.path.join(base_folder, profiling_config.save_traces_folder)
-        profile_freq = profiling_config.profile_freq
+        profile_freq, warmup, active = (
+            profiling_config.profile_freq,
+            profiling_config.profiler_warmup,
+            profiling_config.profiler_active,
+        )
 
         rank = torch.distributed.get_rank()
 
@@ -58,7 +59,6 @@ def trace_handler(prof):
         if not os.path.exists(trace_dir):
             os.makedirs(trace_dir, exist_ok=True)
 
-        warmup, active = WARMUP, 1
         wait = profile_freq - (active + warmup)
         assert (
             wait >= 0