format

llms/mistral/convert.py

@@ -2,7 +2,6 @@
 
 import argparse
 import json
-import numpy as np
 from pathlib import Path
 
 import numpy as np

llms/mistral/test.py

@@ -2,11 +2,10 @@
 
 import unittest
 
+import mistral
 import mlx.core as mx
 from mlx.utils import tree_map
 
-import mistral
-
 
 class TestMistral(unittest.TestCase):
     def test_model(self):

llms/mixtral/convert.py

@@ -46,7 +46,7 @@ if __name__ == "__main__":
         args = json.load(fid)
         args["model_type"] = "mixtral"
     with open(model_path / "config.json", "w") as f:
-        json.dump(args, f, indent=4)        
+        json.dump(args, f, indent=4)
 
     torch_files = glob.glob(str(model_path / "consolidated.*.pt"))
     torch_files = sorted(torch_files, key=lambda tf: int(tf.split(".")[-2]))

llms/phi2/phi2.py

@@ -1,7 +1,7 @@
 import argparse
-from pathlib import Path
 import math
 from dataclasses import dataclass
+from pathlib import Path
 from typing import Optional
 
 import mlx.core as mx

llms/qwen/convert.py

@@ -1,8 +1,9 @@
 import argparse
-from transformers import AutoModelForCausalLM
+import json
+
 import numpy as np
 import torch
-import json
+from transformers import AutoModelForCausalLM
 
 
 def replace_key(key: str) -> str:

llms/qwen/qwen.py

@@ -1,6 +1,7 @@
 import argparse
-from dataclasses import dataclass
 import json
+from dataclasses import dataclass
+
 import mlx.core as mx
 import mlx.nn as nn
 from mlx.utils import tree_unflatten

speechcommands/kwt.py

@@ -1,9 +1,9 @@
 from typing import Any
+
 import mlx.core as mx
 import mlx.nn as nn
 from mlx.utils import tree_flatten
 
-
 __all__ = ["KWT", "kwt1", "kwt2", "kwt3"]
 
 

speechcommands/main.py

@@ -1,13 +1,13 @@
 import argparse
 import time
-import kwt
-import mlx.nn as nn
-import mlx.data as dx
-import mlx.core as mx
-import mlx.optimizers as optim
-from mlx.data.features import mfsc
-from mlx.data.datasets import load_speechcommands
 
+import kwt
+import mlx.core as mx
+import mlx.data as dx
+import mlx.nn as nn
+import mlx.optimizers as optim
+from mlx.data.datasets import load_speechcommands
+from mlx.data.features import mfsc
 
 parser = argparse.ArgumentParser(add_help=True)
 parser.add_argument(

t5/convert.py

@@ -47,8 +47,7 @@ def convert(model_name, dtype):
     dtype = getattr(np, dtype)
     model = T5ForConditionalGeneration.from_pretrained(model_name, torch_dtype="auto")
     weights = {
-        replace_key(k): v.numpy().astype(dtype)
-        for k, v in model.state_dict().items()
+        replace_key(k): v.numpy().astype(dtype) for k, v in model.state_dict().items()
     }
     file_name = model_name.replace("/", "-")
     print(f"Saving weights to {file_name}.npz")

t5/t5.py

@@ -337,7 +337,7 @@ class Tokenizer:
         self._tokenizer = T5Tokenizer.from_pretrained(
             args.model,
             legacy=False,
-            model_max_length=getattr(config, 'n_positions', 512)
+            model_max_length=getattr(config, "n_positions", 512),
         )
 
     @property