black format

lora/convert.py

@@ -30,7 +30,7 @@ if __name__ == "__main__":
     torch_path = Path(args.torch_model)
     if not os.path.exists(args.mlx_model):
         os.makedirs(args.mlx_model)
-    mlx_path =  Path(args.mlx_model)
+    mlx_path = Path(args.mlx_model)
 
     state = torch.load(str(torch_path / "consolidated.00.pth"))
     np.savez(
@@ -57,5 +57,3 @@ if __name__ == "__main__":
             config["hidden_dim"] = state["layers.0.feed_forward.w1.weight"].shape
     with open(mlx_path / "params.json", "w") as outfile:
         json.dump(config, outfile)
-
-

lora/lora.py

@@ -20,9 +20,13 @@ import wikisql
 
 
 def build_parser():
-    parser = argparse.ArgumentParser(description="LoRA finetuning with Llama or Mistral")
+    parser = argparse.ArgumentParser(
+        description="LoRA finetuning with Llama or Mistral"
+    )
     parser.add_argument(
-        "--model", required=True, help="A path to the model files containing the tokenizer, weights, config."
+        "--model",
+        required=True,
+        help="A path to the model files containing the tokenizer, weights, config.",
     )
     # Generation args
     parser.add_argument(
@@ -227,6 +231,7 @@ def generate(model, prompt, tokenizer, args):
 
     def generate_step():
         temp = args.temp
+
         def sample(logits):
             if temp == 0:
                 return mx.argmax(logits, axis=-1)