Update README.md

HuggingFace -> Hugging Face

README.md

@@ -7,7 +7,7 @@ This repository records [EleutherAI](https://www.eleuther.ai)'s library for trai
 
 For those looking for a TPU-centric codebase, we recommend [Mesh Transformer JAX](https://github.com/kingoflolz/mesh-transformer-jax).
 
-**If you are not looking to train models with billions of parameters from scratch, this is likely the wrong library to use. For generic inference needs, we recommend you use the HuggingFace `transformers` library instead which supports GPT-NeoX models.**
+**If you are not looking to train models with billions of parameters from scratch, this is likely the wrong library to use. For generic inference needs, we recommend you use the Hugging Face `transformers` library instead which supports GPT-NeoX models.**
 
 # Contents
 
@@ -24,7 +24,7 @@ For those looking for a TPU-centric codebase, we recommend [Mesh Transformer JAX
 - [Training and Finetuning](#training-and-finetuning)
 - [Inference](#inference)
 - [Evaluation](#evaluation)
-- [Exporting to HuggingFace](#exporting-to-huggingface)
+- [Exporting to Hugging Face](#exporting-to-hugging-face)
 - [Monitoring](#monitoring)
  * [Weights & Biases](#wandb)
  * [TensorBoard](#tensorboard)
@@ -192,7 +192,7 @@ Or use the 20B tokenizer (for which only a single Vocab file is needed):
 
 - Vocab: https://the-eye.eu/public/AI/models/GPT-NeoX-20B/slim_weights/20B_tokenizer.json
 
-(alternatively, you can provide any tokenizer file that can be loaded by Huggingface's tokenizers library with the `Tokenizer.from_pretrained()` command)
+(alternatively, you can provide any tokenizer file that can be loaded by Hugging Face's tokenizers library with the `Tokenizer.from_pretrained()` command)
 
 You can now pretokenize your data using `tools/preprocess_data.py`, the arguments for which are detailed below:
 
@@ -277,7 +277,7 @@ Although this is not strictly necessary, we find it useful to define the model p
 
 # Inference
 
-**For most uses we recommend deploying models trained using the GPT-NeoX library via the HuggingFace Transformers library which is better optimized for inference.**
+**For most uses we recommend deploying models trained using the GPT-NeoX library via the Hugging Face Transformers library which is better optimized for inference.**
 
 We support three types of generation from a pretrained model:
 1. Unconditional generation
@@ -298,22 +298,22 @@ python ./deepy.py evaluate.py -d configs your_configs.yml --eval_tasks task1 tas
 
 where `--eval_tasks` is a list of evaluation tasks followed by spaces, e.g `--eval_tasks lambada hellaswag piqa sciq`. For details of all tasks available, refer to the [lm-evaluation-harness repo](https://github.com/EleutherAI/lm-evaluation-harness).
 
-# Exporting to HuggingFace
+# Exporting to Hugging Face
 
-GPT-NeoX is optimized heavily for training only, and GPT-NeoX model checkpoints are not compatible out of the box with other deep learning libraries. To make models easily loadable and shareable with end users, and for further exporting to various other frameworks, GPT-NeoX supports checkpoint conversion to the [HuggingFace Transformers](https://arxiv.org/abs/1910.03771) GPTNeoXModel format.
+GPT-NeoX is optimized heavily for training only, and GPT-NeoX model checkpoints are not compatible out of the box with other deep learning libraries. To make models easily loadable and shareable with end users, and for further exporting to various other frameworks, GPT-NeoX supports checkpoint conversion to the [Hugging Face Transformers](https://arxiv.org/abs/1910.03771) GPTNeoXModel format.
 
-To convert a NeoX checkpoint to Huggingface-loadable format, run:
+To convert a NeoX checkpoint to Hugging Face-loadable format, run:
 ```bash
 python ./tools/convert_to_hf.py --input_dir /path/to/model/global_stepXXX --config_file your_config.yml --output_dir hf_model/save/location
 ```
-Then to upload a model to [the Huggingface Hub](https://huggingface.co/), run:
+Then to upload a model to [the Hugging Face Hub](https://huggingface.co/), run:
 ```
 huggingface-cli login
 python ./tools/upload.py
 ```
 and input the requested information, including HF hub user token.
 
-Note, however, that this compatibility is not one-to-one, and only certain configurations from GPT-NeoX are supported in the Huggingface GPTNeoXModel class. Advanced features such as alternative positional embeddings may require new Transformers modeling code and new conversion script tweaks.
+Note, however, that this compatibility is not one-to-one, and only certain configurations from GPT-NeoX are supported in the Hugging Face GPTNeoXModel class. Advanced features such as alternative positional embeddings may require new Transformers modeling code and new conversion script tweaks.
 
 # Monitoring