---
license: apache-2.0
tags:
- generated_from_trainer
- text-generation
- opt
- non-commercial
- dialogue
- chatbot

widget:
- text: "If you could live anywhere, where would it be? peter szemraj:" 
  example_title: "live anywhere"
- text: "What would you sing at Karaoke night? peter szemraj:" 
  example_title: "Karaoke"
- text: "If you could hire someone to help you, would it be with cleaning, cooking, or yard work? peter szemraj:" 
  example_title: "help"
- text: "What form of public transportation do you prefer? (air, boat, train, bus, car, etc.) peter szemraj:" 
  example_title: "transportation"
- text: "What's your favorite zoo animal? peter szemraj:" 
  example_title: "animal"
- text: "Do you like or dislike surprises? Why or why not? peter szemraj:" 
  example_title: "surprises"
- text: "What celebrity would you like to meet at Starbucks for a cup of coffee? peter szemraj:" 
  example_title: "celebrity "
  
inference:
  parameters:
    min_length: 2
    max_length: 64
    length_penalty: 0.7
    temperature: 0.3
    no_repeat_ngram_size: 2
    top_k: 20
    do_sample: True
    repetition_penalty: 4.5
---


# pszemraj/opt-peter-1.3B

This model is a fine-tuned version of [pszemraj/opt-peter-1.3B-1E](https://huggingface.co/pszemraj/opt-peter-1.3B-1E) on 80k Whatsapp/iMessages (mine).

It achieves the following results on the evaluation set, after training for 1 epoch (_on top of the 1E checkpoint linked above_):

- eval_loss: 3.4220
- eval_runtime: 954.9678
- eval_samples_per_second: 9.114
- eval_steps_per_second: 2.279
- epoch: 1.0
- step: 1235

## Model description

- Exploring to see how OPT does in terms of dialogue/conversational applications :)
- Seems to do a lot better than GPT-Neo with similar training parameters 

## Intended uses & limitations

- OPT has a license that does not allow for commercial use, see original for details
- **any statements or claims made by this model do not reflect actual claims/statements by me**


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 2

### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1