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  [Megatron](https://arxiv.org/pdf/1909.08053.pdf) is a large, powerful transformer developed by the Applied Deep Learning Research team at NVIDIA. This particular Megatron model was trained from a generative, left-to-right transformer in the style of GPT-2. This model was trained on text sourced from Wikipedia, RealNews, OpenWebText, and CC-Stories. It contains 345 million parameters. 
  
  Find more information at [https://github.com/NVIDIA/Megatron-LM](https://github.com/NVIDIA/Megatron-LM)
  
  # How to run Megatron GPT2 using Transformers
  
  ## Prerequisites 
  
  In that guide, we run all the commands from a folder called `$MYDIR` and defined as (in `bash`):
  
  ```
  export MYDIR=$HOME
  ```
  
  Feel free to change the location at your convenience.
  
  To run some of the commands below, you'll have to clone `Transformers`. 
  
  ```
  git clone https://github.com/huggingface/transformers.git $MYDIR/transformers
  ```
  
  ## Get the checkpoints from the NVIDIA GPU Cloud 
  
  You must create a directory called `nvidia/megatron-gpt2-345m`:
  
  ```
  mkdir -p $MYDIR/nvidia/megatron-gpt2-345m
  ```
  
  You can download the checkpoints from the [NVIDIA GPU Cloud (NGC)](https://ngc.nvidia.com/catalog/models/nvidia:megatron_lm_345m). For that you
  have to [sign up](https://ngc.nvidia.com/signup) for and setup the NVIDIA GPU
  Cloud (NGC) Registry CLI.  Further documentation for downloading models can be
  found in the [NGC
  documentation](https://docs.nvidia.com/dgx/ngc-registry-cli-user-guide/index.html#topic_6_4_1).
  
  Alternatively, you can directly download the checkpoints using:
  
  ```
  wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/megatron_lm_345m/versions/v0.0/zip -O $MYDIR/nvidia/megatron-gpt2-345m/checkpoint.zip
  ```
  
  ## Converting the checkpoint
  
  In order to be loaded into `Transformers`, the checkpoint has to be converted. You should run the following command for that purpose. 
  That command will create `config.json` and `pytorch_model.bin` in `$MYDIR/nvidia/megatron-gpt2-345m`. 
  You can move those files to different directories if needed.
  
  ```
  python3 $MYDIR/transformers/src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py $MYDIR/nvidia/megatron-gpt2-345m/checkpoint.zip
  ```
  
  As explained in [PR #14956](https://github.com/huggingface/transformers/pull/14956), if when running this conversion 
  script and you're getting an exception:
  ```
  ModuleNotFoundError: No module named 'megatron.model.enums'
  ```
  you need to tell python where to find the clone of Megatron-LM, e.g.:
  ```
  cd /tmp
  git clone https://github.com/NVIDIA/Megatron-LM
  PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py ...
  ```
  Or, if you already have it cloned elsewhere, simply adjust the path to the existing path.
  
  If the training was done using a Megatron-LM fork, e.g. [Megatron-DeepSpeed](https://github.com/microsoft/Megatron-DeepSpeed/) then 
  you may need to have that one in your path, i.e., /path/to/Megatron-DeepSpeed.
  
  ## Text generation
  
  The following code shows how to use the Megatron GPT2 checkpoint and the Transformers API to generate text.
  
  ```
  import os
  import torch
  
  from transformers import GPT2Tokenizer, GPT2LMHeadModel
  
  # The tokenizer. Megatron was trained with standard tokenizer(s).
  tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
  # The path to the config/checkpoint (see the conversion step above).
  directory = os.path.join(os.environ['MYDIR'], 'nvidia/megatron-gpt2-345m')
  # Load the model from $MYDIR/nvidia/megatron-gpt2-345m.
  model = GPT2LMHeadModel.from_pretrained(directory)
  
  # Copy to the device and use FP16.
  assert torch.cuda.is_available()
  device = torch.device("cuda")
  model.to(device)
  model.eval()
  model.half()
  
  # Generate the sentence.
  output = model.generate(input_ids=None, max_length=32, num_return_sequences=1)
  
  # Output the text.
  for sentence in output:
      sentence = sentence.tolist()
      text = tokenizer.decode(sentence, clean_up_tokenization_spaces=True)
      print(text)
  ```
  
  # To use this as a normal HuggingFace model
  
  If you want to use this model with HF Trainer, here is a quick way to do that:
  
  1. Download nvidia checkpoint:
  ```
  wget --content-disposition https:/