readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: gpt-regular-test
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # gpt-regular-test
  
  i was stupid and all the newline tokens are replaced with [/n] so be wary if you're using the demo on this page that that just means new line
  
  ```python
  from transformers import AutoTokenizer
  from transformers import AutoModelForCausalLM
  model = AutoModelForCausalLM.from_pretrained("crumb/gpt2-regular-large")
  tokenizer = AutoTokenizer.from_pretrained("gpt2-large", use_fast=True)
  
  prompt = """(Episode begins with Mordecai and Rigby watching TV)
  Mordecai: Dude, what are you doing? I think I'm gonna lose my mind.
  Rigby:"""
  
  prompt=prompt.replace("\n","[/n]")
  tokenz = tokenizer(prompt,return_tensors='pt')['input_ids']
  output = model.generate(
      tokenz, 
      max_length=length,
      num_return_sequences=1,
      top_p=.92,
      temperature=.65,
      do_sample=True,
      top_k=125,
      early_stopping=True,
      pad_token_id=tokenizer.eos_token_id
  )
  output = tokenizer.decode(output[0]).replace("[/n]","\n")
  print(output)
  ```
  This model is a fine-tuned version of gpt2-large on the entirety of Regular Show. It achieves the following results on the evaluation set (The Power, Death Punchies, Do Me a Solid): 
  - Loss: 1.6383
  
  ## Intended uses & limitations
  
  Same as gpt2-large
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 1e-05
  - train_batch_size: 1
  - eval_batch_size: 1
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 1
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss |
  |:-------------:|:-----:|:----:|:---------------:|
  | 2.1844        | 1.0   | 7633 | 1.6383          |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  