gpt2-medium-wiki

GPT-2 Medium

Model Description: GPT-2 Medium is the 355M parameter version of GPT-2, a transformer-based language model created and released by OpenAI. The model is a pretrained model on English language using a causal language modeling (CLM) objective. Created new fine-tuned model using wikitext dataset referred from huggingface(lilacai/lilac-wikitext-2-raw-v1)

• Developed by: Aswin([email protected])
• Dataset: lilacai/lilac-wikitext-2-raw-v1

Created model as part of experiment, please increase epoch and other parameters as part of hyperparameter tuning.

Library Import

from transformers import GPT2Tokenizer, GPT2LMHeadModel, \
DataCollatorForLanguageModeling, Trainer, TrainingArguments
import time
from rich import print
from datasets import load_dataset

Variable Declaration

# Fine-tuned model name
new_model = "gpt2-medium-wiki"
# Load the entire model on the GPU 0
device_map = {"": 0}

Load the tokenizer and model

model_name = "gpt2-medium"  # You can use "gpt2", "gpt2-medium", "gpt2-large", etc.
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
tokenizer.pad_token = tokenizer.eos_token
model = GPT2LMHeadModel.from_pretrained(model_name, device_map=device_map)

Load a sample dataset from Hugging Face's datasets library

train_dataset = load_dataset('wikitext', 'wikitext-2-raw-v1', split='train[10:90]')
eval_dataset = load_dataset('wikitext', 'wikitext-2-raw-v1', split='validation[10:30]')

Tokenize the dataset

train_tokenized = train_dataset.map(lambda x: tokenizer(x['text'], truncation=True, padding='max_length', max_length=128), batched=True)
eval_tokenized = eval_dataset.map(lambda x: tokenizer(x['text'], truncation=True, padding='max_length', max_length=128), batched=True)
train_tokenized.set_format('torch', columns=['input_ids', 'attention_mask'])
eval_tokenized.set_format('torch', columns=['input_ids', 'attention_mask'])

Fine-tune the model

data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
training_args = TrainingArguments(
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8,
    num_train_epochs=10,  # Set this higher for more fine-tuning
    evaluation_strategy="epoch",
    logging_dir="./logs",
    logging_steps=200,
    do_train=True,
    do_eval=True,
    save_strategy="epoch",
    output_dir="./gpt2_finetuned",
)

trainer = Trainer(
    model=model,
    args=training_args,
    data_collator=data_collator,
    train_dataset=train_tokenized,
    eval_dataset=eval_tokenized,
)
trainer.train()

Evaluate the fine-tuned model (optional)

results = trainer.evaluate()
print(results)

Performance Time

end = time.time()
print(f"[bold green]Model Successfully trained in {end - start} seconds")

Save trained model

trainer.model.save_pretrained(new_model)

Generate Text for inferencing

def generate_text(input_text):
  input_ids = tokenizer.encode(input_text, return_tensors="pt").to(model.device)
  # Generate text
  output = model.generate(input_ids, max_length=50, temperature=0.7)

  # Decode and print the generated text
  for i, sequence in enumerate(output):
      decoded_sequence = tokenizer.decode(sequence, skip_special_tokens=True)
      print(f"Generated Text {i + 1}: {decoded_sequence}")

input_text = "which system carried over directly from Valkyira Chronicles"
generate_text(input_text)

My GPU Size