Add llms subdir + update README (#145)

* add llms subdir + update README

* nits

* use same pre-commit as mlx

* update readmes a bit

* format

t5/convert.py

@@ -1,6 +1,5 @@
-from transformers import T5ForConditionalGeneration
 import numpy as np
-
+from transformers import T5ForConditionalGeneration
 
 SHARED_REPLACEMENT_PATTERNS = [
     (".block.", ".layers."),
@@ -48,8 +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/hf_t5.py

@@ -1,7 +1,7 @@
-from transformers import T5ForConditionalGeneration, T5EncoderModel, AutoTokenizer
-
 import argparse
 
+from transformers import AutoTokenizer, T5EncoderModel, T5ForConditionalGeneration
+
 
 def embed(t5_model: str):
     batch = [
@@ -51,4 +51,3 @@ if __name__ == "__main__":
         embed(args.model)
     else:
         generate(args.model)
-

t5/t5.py

@@ -1,11 +1,11 @@
 import argparse
-from typing import Optional, Tuple, List
 from time import perf_counter_ns
+from typing import List, Optional, Tuple
 
-import numpy as np
 import mlx.core as mx
 import mlx.nn as nn
-from mlx.utils import tree_unflatten, tree_map
+import numpy as np
+from mlx.utils import tree_map, tree_unflatten
 from transformers import T5Config, T5Tokenizer
 
 
@@ -166,7 +166,7 @@ class DenseActivation(nn.Module):
             self.act = nn.relu
         elif activation == "gelu":
             self.act = nn.gelu
-        elif activation ==  "silu":
+        elif activation == "silu":
             self.act = nn.silu
         else:
             raise ValueError(f"Unknown activation: {activation}")
@@ -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