diff --git a/src/accelerate/accelerator.py b/src/accelerate/accelerator.py
index 1c3782067..7aaee2de7 100644
--- a/src/accelerate/accelerator.py
+++ b/src/accelerate/accelerator.py
@@ -196,7 +196,6 @@ def __init__(
         self,
         device_placement: bool = True,
         split_batches: bool = False,
-        fp16: bool = None,
         mixed_precision: Union[PrecisionType, str] = None,
         gradient_accumulation_steps: int = 1,
         cpu: bool = False,
@@ -220,13 +219,6 @@ def __init__(
                     f"Unknown mixed_precision mode: {mixed_precision}. Choose between {PrecisionType.list()}"
                 )
 
-        if fp16:
-            warnings.warn(
-                "`fp16=True` is deprecated and will be removed in version 0.15.0 of 🤗 Accelerate. Use `mixed_precision='fp16'` instead.",
-                FutureWarning,
-            )
-            mixed_precision = "fp16"
-
         if dynamo_backend is not None:
             dynamo_backend = DynamoBackend(dynamo_backend.upper())
 
diff --git a/src/accelerate/commands/launch.py b/src/accelerate/commands/launch.py
index fd5ad7d60..afb6498f3 100644
--- a/src/accelerate/commands/launch.py
+++ b/src/accelerate/commands/launch.py
@@ -20,7 +20,6 @@
 import os
 import subprocess
 import sys
-import warnings
 from ast import literal_eval
 from pathlib import Path
 from typing import Dict, List
@@ -64,7 +63,6 @@
     "--multi-gpu": "Distributed GPUs",
     "--tpu": "TPU",
     "--mps": "MPS",
-    "--use_mps_device": "MPS",
     "--use_deepspeed": "DeepSpeed Arguments",
     "--use_fsdp": "FSDP Arguments",
     "--use_megatron_lm": "Megatron-LM Arguments",
@@ -165,12 +163,6 @@ def launch_command_parser(subparsers=None):
     hardware_args.add_argument(
         "--tpu", default=False, action="store_true", help="Whether or not this should launch a TPU training."
     )
-    hardware_args.add_argument(
-        "--use_mps_device",
-        default=False,
-        action="store_true",
-        help="This argument is deprecated, use `--mps` instead.",
-    )
 
     # Resource selection arguments
     resource_args = parser.add_argument_group(
@@ -191,12 +183,6 @@ def launch_command_parser(subparsers=None):
         "Choose between FP16 and BF16 (bfloat16) training. "
         "BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.",
     )
-    resource_args.add_argument(
-        "--fp16",
-        default=False,
-        action="store_true",
-        help="This argument is deprecated, use `--mixed_precision fp16` instead.",
-    )
     resource_args.add_argument(
         "--num_processes", type=int, default=None, help="The total number of processes to be launched in parallel."
     )
@@ -525,12 +511,6 @@ def simple_launcher(args):
 
     current_env = os.environ.copy()
     current_env["ACCELERATE_USE_CPU"] = str(args.cpu or args.use_cpu)
-    if args.use_mps_device:
-        warnings.warn(
-            '`use_mps_device` flag is deprecated and will be removed in version 0.15.0 of 🤗 Accelerate. Use "--mps" instead.',
-            FutureWarning,
-        )
-        args.mps = True
     current_env["ACCELERATE_USE_MPS_DEVICE"] = str(args.mps)
     if args.mps:
         current_env["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
@@ -550,13 +530,6 @@ def simple_launcher(args):
             f"Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}."
         )
 
-    if args.fp16:
-        warnings.warn(
-            "`fp16` is deprecated and will be removed in version 0.15.0 of 🤗 Accelerate. Use `mixed_precision fp16` instead.",
-            FutureWarning,
-        )
-        mixed_precision = "fp16"
-
     current_env["ACCELERATE_MIXED_PRECISION"] = str(mixed_precision)
 
     try:
@@ -614,13 +587,6 @@ def multi_gpu_launcher(args):
     except ValueError:
         raise ValueError(f"Unknown mixed_precision mode: {mixed_precision}. Choose between {PrecisionType.list()}.")
 
-    if args.fp16:
-        warnings.warn(
-            "`fp16` is deprecated and will be removed in version 0.15.0 of 🤗 Accelerate. Use `mixed_precision fp16` instead.",
-            FutureWarning,
-        )
-        mixed_precision = "fp16"
-
     current_env["ACCELERATE_MIXED_PRECISION"] = str(mixed_precision)
 
     try:
@@ -749,13 +715,6 @@ def deepspeed_launcher(args):
             f"Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}."
         )
 
-    if args.fp16:
-        warnings.warn(
-            '--fp16 flag is deprecated and will be removed in version 0.15.0 of 🤗 Accelerate. Use "--mixed_precision fp16" instead.',
-            FutureWarning,
-        )
-        mixed_precision = "fp16"
-
     current_env["PYTHONPATH"] = env_var_path_add("PYTHONPATH", os.path.abspath("."))
     current_env["ACCELERATE_MIXED_PRECISION"] = str(mixed_precision)
     current_env["ACCELERATE_USE_DEEPSPEED"] = "true"
@@ -925,10 +884,6 @@ def sagemaker_launcher(sagemaker_config: SageMakerConfig, args):
             f"Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}."
         )
 
-    if args.fp16:
-        warnings.warn('--fp16 flag is deprecated. Use "--mixed_precision fp16" instead.', FutureWarning)
-        mixed_precision = "fp16"
-
     try:
         dynamo_backend = DynamoBackend(args.dynamo_backend.upper())
     except ValueError:
@@ -1054,15 +1009,12 @@ def launch_command(args):
 
                 # Those args are handled separately
                 if (
-                    name not in ["compute_environment", "fp16", "mixed_precision", "distributed_type"]
+                    name not in ["compute_environment", "mixed_precision", "distributed_type"]
                     and getattr(args, name, None) is None
                 ):
                     setattr(args, name, attr)
         if not args.mixed_precision:
-            if args.fp16:
-                args.mixed_precision = "fp16"
-            else:
-                args.mixed_precision = defaults.mixed_precision
+            args.mixed_precision = defaults.mixed_precision
         if args.dynamo_backend is None:
             warned.append("\t`--dynamo_backend` was set to a value of `'no'`")
             args.dynamo_backend = "no"
diff --git a/src/accelerate/launchers.py b/src/accelerate/launchers.py
index 5768ec06b..2c9db6a75 100644
--- a/src/accelerate/launchers.py
+++ b/src/accelerate/launchers.py
@@ -15,7 +15,6 @@
 import os
 import sys
 import tempfile
-import warnings
 
 import torch
 
@@ -23,7 +22,7 @@
 from .utils import PrecisionType, PrepareForLaunch, patch_environment
 
 
-def notebook_launcher(function, args=(), num_processes=None, use_fp16=False, mixed_precision="no", use_port="29500"):
+def notebook_launcher(function, args=(), num_processes=None, mixed_precision="no", use_port="29500"):
     """
     Launches a training function, using several processes if it's possible in the current environment (TPU with
     multiple cores for instance).
@@ -104,13 +103,6 @@ def notebook_launcher(function, args=(), num_processes=None, use_fp16=False, mix
                     "function."
                 )
 
-            if use_fp16:
-                warnings.warn(
-                    "`fp16=True` is deprecated and will be removed in version 0.15.0 of 🤗 Accelerate. Use `mixed_precision='fp16'` instead.",
-                    FutureWarning,
-                )
-                mixed_precision = "fp16"
-
             # torch.distributed will expect a few environment variable to be here. We set the ones common to each
             # process here (the other ones will be set be the launcher).
             with patch_environment(
diff --git a/src/accelerate/test_utils/scripts/test_script.py b/src/accelerate/test_utils/scripts/test_script.py
index 6897d9084..ddb053a8a 100644
--- a/src/accelerate/test_utils/scripts/test_script.py
+++ b/src/accelerate/test_utils/scripts/test_script.py
@@ -275,29 +275,6 @@ def training_check():
         assert torch.allclose(old_model.a, model.a), "Did not obtain the same model on CPU or distributed training."
         assert torch.allclose(old_model.b, model.b), "Did not obtain the same model on CPU or distributed training."
 
-        # TEST that previous fp16 flag still works
-        print("Legacy FP16 training check.")
-        AcceleratorState._reset_state()
-        accelerator = Accelerator(fp16=True)
-        train_dl = DataLoader(train_set, batch_size=batch_size, shuffle=True, generator=generator)
-        model = RegressionModel()
-        optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
-
-        train_dl, model, optimizer = accelerator.prepare(train_dl, model, optimizer)
-        set_seed(42)
-        generator.manual_seed(42)
-        for _ in range(3):
-            for batch in train_dl:
-                model.zero_grad()
-                output = model(batch["x"])
-                loss = torch.nn.functional.mse_loss(output, batch["y"])
-                accelerator.backward(loss)
-                optimizer.step()
-
-        model = accelerator.unwrap_model(model).cpu()
-        assert torch.allclose(old_model.a, model.a), "Did not obtain the same model on CPU or distributed training."
-        assert torch.allclose(old_model.b, model.b), "Did not obtain the same model on CPU or distributed training."
-
     # BF16 support is only for CPU + TPU, and some GPU
     if is_bf16_available():
         # Mostly a test that BF16 doesn't crash as the operation inside the model is not converted to BF16



Discussion: body: _The documentation is not available anymore as the PR was closed or merged._