diff --git a/nanochat/common.py b/nanochat/common.py
index 05e371c..86c18de 100644
--- a/nanochat/common.py
+++ b/nanochat/common.py
@@ -89,6 +89,16 @@ def get_dist_info():
     else:
         return False, 0, 0, 1
 
+def autodetect_device_type():
+    # prefer to use CUDA if available, otherwise use MPS, otherwise fallback on CPU
+    if torch.cuda.is_available():
+        device_type = "cuda"
+    if torch.backends.mps.is_available():
+        device_type = "mps"
+    device_type = "cpu"
+    print0(f"Autodetected device type: {device_type}")
+    return device_type
+
 def compute_init(device_type="cuda"): # cuda|cpu|mps
     """Basic initialization that we keep doing over and over, so make common."""
 
diff --git a/scripts/base_eval.py b/scripts/base_eval.py
index a566d49..2d58d87 100644
--- a/scripts/base_eval.py
+++ b/scripts/base_eval.py
@@ -19,7 +19,7 @@ import yaml
 import pandas as pd
 import torch
 
-from nanochat.common import compute_init, compute_cleanup, print0, get_base_dir
+from nanochat.common import compute_init, compute_cleanup, print0, get_base_dir, autodetect_device_type
 from nanochat.tokenizer import HuggingFaceTokenizer
 from nanochat.checkpoint_manager import load_model
 from nanochat.core_eval import evaluate_task
@@ -121,8 +121,10 @@ def main():
     assert len(sys.argv) in [1, 2], "Usage: python base_eval.py [hf_path]"
 
     # distributed / precision setup
-    ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init()
-    autocast_ctx = torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16)
+    device_type = autodetect_device_type()
+    ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init(device_type)
+    dtype = torch.bfloat16 if device_type == "cuda" else torch.float32 # use fp32 on CPU|MPS
+    autocast_ctx = torch.amp.autocast(device_type=device_type, dtype=dtype)
 
     # Load model and tokenizer from command line or from file system
     if len(sys.argv) >= 2:
diff --git a/scripts/base_loss.py b/scripts/base_loss.py
index ba3876d..1609d83 100644
--- a/scripts/base_loss.py
+++ b/scripts/base_loss.py
@@ -9,7 +9,7 @@ torchrun --standalone --nproc_per_node=8 -m scripts.base_loss
 import os
 import torch
 from nanochat.checkpoint_manager import load_model
-from nanochat.common import compute_init, print0, compute_cleanup
+from nanochat.common import compute_init, print0, compute_cleanup, autodetect_device_type
 from nanochat.dataloader import tokenizing_distributed_data_loader
 from nanochat.tokenizer import get_token_bytes
 from nanochat.loss_eval import evaluate_bpb
@@ -20,15 +20,16 @@ device_batch_size = 32
 split_tokens = 20*524288  # number of tokens to evaluate per split
 model_tag = None # optional model tag for the output directory name
 model_step = None # optional model step for the output directory name
+device_type = "" # cuda|cpu|mps (empty => autodetect)
 exec(open(os.path.join('nanochat', 'configurator.py')).read()) # overrides from command line or config file
 
 # Load the base model and the tokenizer
-ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init()
+device_type = autodetect_device_type() if device_type == "" else device_type
+ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init(device_type)
+dtype = torch.bfloat16 if device_type == "cuda" else torch.float32 # use fp32 on CPU|MPS
 model, tokenizer, meta = load_model("base", device, phase="eval", model_tag=model_tag, step=model_step)
 sequence_len = meta["model_config"]["sequence_len"] # could be arbitrary really
-
-# Set up the precision we'll run with
-autocast_ctx = torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16)
+autocast_ctx = torch.amp.autocast(device_type=device_type, dtype=dtype)
 
 # Evaluate the loss on each split
 tokens_per_step = device_batch_size * sequence_len * ddp_world_size
diff --git a/scripts/base_train.py b/scripts/base_train.py
index ebc5ff4..147fce2 100644
--- a/scripts/base_train.py
+++ b/scripts/base_train.py
@@ -7,7 +7,7 @@ or distributed as:
 
 torchrun --nproc_per_node=8 base_train.py
 
-python -m scripts.base_train --device_type=cpu --depth=4 --max_seq_len=512 --device_batch_size=1 --eval_tokens=512 --total_batch_size=512 --num_iterations=1000
+python -m scripts.base_train --device_type=cpu --depth=4 --max_seq_len=512 --device_batch_size=1 --eval_tokens=512 --core_metric_max_per_task=8 --total_batch_size=512 --num_iterations=500
 If you have a Macbook, you're better off using device_type=mps instead of cpu
 """
 
@@ -19,7 +19,7 @@ import torch
 
 from nanochat.gpt import GPT, GPTConfig
 from nanochat.dataloader import tokenizing_distributed_data_loader
-from nanochat.common import compute_init, compute_cleanup, print0, DummyWandb, print_banner, get_base_dir
+from nanochat.common import compute_init, compute_cleanup, print0, DummyWandb, print_banner, get_base_dir, autodetect_device_type
 from nanochat.tokenizer import get_tokenizer, get_token_bytes
 from nanochat.checkpoint_manager import save_checkpoint
 from nanochat.loss_eval import evaluate_bpb
@@ -31,7 +31,7 @@ print_banner()
 # User settings
 run = "dummy" # wandb run name default ("dummy" is special - we won't log to wandb)
 # Runtime
-device_type = "cuda" # cuda|cpu|mps
+device_type = "" # cuda|cpu|mps (empty => autodetect good device type default, in order: CUDA > MPS > CPU)
 # Model architecture
 depth = 20 # the depth of the Transformer model to train, rest of the kwargs are derived
 max_seq_len = 2048 # max context length
@@ -62,6 +62,7 @@ user_config = {k: globals()[k] for k in config_keys} # will be useful for loggin
 # -----------------------------------------------------------------------------
 
 # Compute init
+device_type = autodetect_device_type() if device_type == "" else device_type
 ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init(device_type)
 master_process = ddp_rank == 0 # this process will do logging, checkpointing etc.
 dtype = torch.bfloat16 if device_type == "cuda" else torch.float32 # use fp32 on CPU|MPS