hf correction

bert/hf_model.py

@@ -25,7 +25,7 @@ def run(bert_model: str):
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(
-        description="Run the BERT model using HuggingFace Transformers."
+        description="Run the BERT model using Hugging Face Transformers."
     )
     parser.add_argument(
         "--bert-model",

mixtral/README.md

@@ -14,7 +14,7 @@ For example with Homebrew:
 brew install git-lfs
 ```
 
-Download the models from HuggingFace:
+Download the models from Hugging Face:
 
 ```
 git clone https://huggingface.co/someone13574/mixtral-8x7b-32kseqlen

stable_diffusion/README.md

@@ -1,9 +1,9 @@
 Stable Diffusion
 ================
 
-Stable Diffusion in MLX. The implementation was ported from Huggingface's
+Stable Diffusion in MLX. The implementation was ported from Hugging Face's
 [diffusers](https://huggingface.co/docs/diffusers/index) and we are fetching
-and using the weights available on the Huggingface Hub by Stability AI at
+and using the weights available on the Hugging Face Hub by Stability AI at
 [stabilitiai/stable-diffusion-2-1](https://huggingface.co/stabilityai/stable-diffusion-2-1).
 
 ![out](generated-mlx.png)    

stable_diffusion/stable_diffusion/model_io.py

@@ -169,7 +169,7 @@ def _check_key(key: str, part: str):
 
 
 def load_unet(key: str = _DEFAULT_MODEL, float16: bool = False):
-    """Load the stable diffusion UNet from Huggingface Hub."""
+    """Load the stable diffusion UNet from Hugging Face Hub."""
     _check_key(key, "load_unet")
 
     # Download the config and create the model
@@ -199,7 +199,7 @@ def load_unet(key: str = _DEFAULT_MODEL, float16: bool = False):
 
 
 def load_text_encoder(key: str = _DEFAULT_MODEL, float16: bool = False):
-    """Load the stable diffusion text encoder from Huggingface Hub."""
+    """Load the stable diffusion text encoder from Hugging Face Hub."""
     _check_key(key, "load_text_encoder")
 
     # Download the config and create the model
@@ -226,7 +226,7 @@ def load_text_encoder(key: str = _DEFAULT_MODEL, float16: bool = False):
 
 
 def load_autoencoder(key: str = _DEFAULT_MODEL, float16: bool = False):
-    """Load the stable diffusion autoencoder from Huggingface Hub."""
+    """Load the stable diffusion autoencoder from Hugging Face Hub."""
     _check_key(key, "load_autoencoder")
 
     # Download the config and create the model
@@ -255,7 +255,7 @@ def load_autoencoder(key: str = _DEFAULT_MODEL, float16: bool = False):
 
 
 def load_diffusion_config(key: str = _DEFAULT_MODEL):
-    """Load the stable diffusion config from Huggingface Hub."""
+    """Load the stable diffusion config from Hugging Face Hub."""
     _check_key(key, "load_diffusion_config")
 
     diffusion_config = _MODELS[key]["diffusion_config"]

stable_diffusion/stable_diffusion/tokenizer.py

@@ -81,7 +81,7 @@ class Tokenizer:
         if isinstance(text, list):
             return [self.tokenize(t, prepend_bos, append_eos) for t in text]
 
-        # Lower case cleanup and split according to self.pat. Huggingface does
+        # Lower case cleanup and split according to self.pat. Hugging Face does
         # a much more thorough job here but this should suffice for 95% of
         # cases.
         clean_text = regex.sub(r"\s+", " ", text.lower())