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

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: 0.65
    no_repeat_ngram_size: 2
    top_k: 20
    do_sample: True
    repetition_penalty: 4.5
---

# pszemraj/opt-peter-2.7B

This model is a fine-tuned version of [facebook/opt-2.7b](https://huggingface.co/facebook/opt-2.7b) on about 80k whatsapp/text messages (mine). Please use responsibly :)

## 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

> The base model has a custom license which propogates to this one. Most importantly, it cannot be used commercially. Read more here: [facebook/opt-2.7b](https://huggingface.co/facebook/opt-2.7b) 

- the model is probably too large to use via API here. Use in Python with GPU RAM / CPU RAM > 12 gb.
  - alternatively, you can message [a bot on telegram](http://t.me/GPTPeter_bot) where I test LLMs for dialogue generation
- **any statements or claims made by this model do not reflect actual claims/statements by me.** Keep in mind it is a _fine-tuned_ version of the model on my data, so things from pre-training are also present in outputs.

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 3

### Training results



### Framework versions

- Transformers 4.19.2
- Pytorch 1.10.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1