Use gpt2-large instead of distilgpt2

bot.py

@@ -1,6 +1,7 @@
 from argparse import ArgumentParser
 from random import randint, choice
 
+from torch import float16
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
 
@@ -17,8 +18,8 @@ parser.add_argument('-m', '--model', default='model',
 args = parser.parse_args()
 
 
-tokenizer = AutoTokenizer.from_pretrained('distilgpt2')
-model = AutoModelForCausalLM.from_pretrained(args.model)
+tokenizer = AutoTokenizer.from_pretrained('gpt2-large')
+model = AutoModelForCausalLM.from_pretrained(args.model, torch_dtype=float16).to('cuda')
 
 
 if args.input is None:
@@ -71,7 +72,7 @@ if args.input is None:
 
 # Run the input through the model
 print(args.input)
-inputs = tokenizer.encode(args.input, return_tensors='pt')
+inputs = tokenizer.encode(args.input, return_tensors='pt').to('cuda')
 output = tokenizer.decode(model.generate(
     inputs, do_sample=True, max_length=150, top_p=0.9)[0])
 print(output)

train.py

@@ -1,6 +1,7 @@
 from argparse import ArgumentParser
 from itertools import chain
 
+from torch import float16
 from datasets import load_dataset
 from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments, default_data_collator
 
@@ -15,13 +16,13 @@ args = parser.parse_args()
 
 # Load and tokenize dataset
 raw_dataset = load_dataset('text', data_files={'train': args.input}, keep_linebreaks=True)
-tokenizer = AutoTokenizer.from_pretrained('distilgpt2', use_fast=True)
+tokenizer = AutoTokenizer.from_pretrained('gpt2-large', use_fast=True)
 tokenized_dataset = raw_dataset.map(lambda examples: tokenizer(examples['text']),
                                     batched=True, remove_columns='text')
 
 
 # Generate chunks of block_size
-block_size = tokenizer.model_max_length
+block_size = 256 # tokenizer.model_max_length
 
 # Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
 def group_texts(examples):
@@ -44,8 +45,9 @@ lm_dataset = tokenized_dataset.map(group_texts, batched=True)
 
 
 # Create and train the model
-model = AutoModelForCausalLM.from_pretrained('distilgpt2')
-trainer = Trainer(model, TrainingArguments(output_dir=args.output),
-                  default_data_collator, lm_dataset['train'])
+model = AutoModelForCausalLM.from_pretrained('gpt2-large',
+    torch_dtype=float16, low_cpu_mem_usage=True).to('cuda')
+trainer = Trainer(model, TrainingArguments(output_dir=args.output, per_device_train_batch_size=1,
+                  gradient_accumulation_steps=8), default_data_collator, lm_dataset['train'])
 trainer.train()
 trainer.save_model()