---
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