Use README example for Hello World

docs/tutorials/hello-world.md

@@ -1,3 +1,61 @@
 # Hello World
 
-TBD
+Here's how you can quickly test out aitextgen on your own computer, even if you don't have a GPU!
+
+For generating text from a pretrained GPT-2 model:
+
+```python
+from aitextgen import aitextgen
+
+# Without any parameters, aitextgen() will download, cache, and load the 124M GPT-2 "small" model
+ai = aitextgen()
+
+ai.generate()
+ai.generate(n=3, max_length=100)
+ai.generate(n=3, prompt="I believe in unicorns because", max_length=100)
+ai.generate_to_file(n=10, prompt="I believe in unicorns because", max_length=100, temperature=1.2)
+```
+
+You can also generate from the command line:
+
+```sh
+aitextgen generate
+aitextgen generate --prompt "I believe in unicorns because" --to_file False
+```
+
+Want to train your own mini GPT-2 model on your own computer? Download this [text file of Shakespeare plays](https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt), cd to that directory in a Teriminal, open up a `python3` console and go:
+
+```python
+from aitextgen.TokenDataset import TokenDataset
+from aitextgen.tokenizers import train_tokenizer
+from aitextgen.utils import GPT2ConfigCPU
+from aitextgen import aitextgen
+
+# The name of the downloaded Shakespeare text for training
+file_name = "input.txt"
+
+# Train a custom BPE Tokenizer on the downloaded text
+# This will save two files: aitextgen-vocab.json and aitextgen-merges.txt,
+# which are needed to rebuild the tokenizer.
+train_tokenizer(file_name)
+vocab_file = "aitextgen-vocab.json"
+merges_file = "aitextgen-merges.txt"
+
+# GPT2ConfigCPU is a mini variant of GPT-2 optimized for CPU-training
+# e.g. the # of input tokens here is 64 vs. 1024 for base GPT-2.
+config = GPT2ConfigCPU()
+
+# Instantiate aitextgen using the created tokenizer and config
+ai = aitextgen(vocab_file=vocab_file, merges_file=merges_file, config=config)
+
+# You can build datasets for training by creating TokenDatasets,
+# which automatically processes the dataset with the appropriate size.
+data = TokenDataset(file_name, vocab_file=vocab_file, merges_file=merges_file, block_size=64)
+
+# Train the model! It will save pytorch_model.bin periodically and after completion.
+# On a 2016 MacBook Pro, this took ~25 minutes to run.
+ai.train(data, batch_size=16, num_steps=5000)
+
+# Generate text from it!
+ai.generate(10, prompt="ROMEO:")
+```