readme: ---
  license: 
  - other
  - apache-2.0
  tags:
  - generated_from_trainer
  - text-generation
  - OPT
  - non-commercial
  - dialogue
  - chatbot
  - ai-msgbot
  library_name: transformers
  pipeline_tag: text-generation
  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
      temperature: 0.5
      no_repeat_ngram_size: 2
      repetition_penalty: 4.5
  ---
  
  # pszemraj/opt-peter-2.7B
  
   <a href="https://colab.research.google.com/gist/pszemraj/26a69775c9d012051396ab5ae980f5c1/example-text-gen-pszemraj-opt-peter-2-7b.ipynb">
    <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
  </a>
  
  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 :)
  
  Test it out on Google Colab by clicking the button above.
  
  ![chatdemo](https://i.imgur.com/1EgQYat.png)
  
  ## 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 
  - you can create your own digital clone and deploy it leveraging [this repository I am working on](https://github.com/pszemraj/ai-msgbot).
  
  ### sharded checkpoint
  
  As this model file is 10+ GB, it can impose some constraints with lower RAM runtimes and/or download speeds. To help with this issue, a sharded checkpoint of this model is available [here](https://huggingface.co/pszemraj/opt-peter-2.7B-sharded).
  
  The `pszemraj/opt-peter-2.7B-sharded` model can be used as a drop-in replacement for this one for all use cases.
  
  ## Intended uses & limitations
  
  > The base model has a custom license that propagates 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, Colab notebook linked above.
    - 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
  
  WhatsApp & iMessage data were parsed using [ai-msgbot](https://github.com/pszemraj/ai-msgbot) and then fed as a text dataset to the HF trainer.
  
  ## Training procedure
  
  ### Training hyperparameters
  
  **SESSION ONE**
  
  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
  
  **SESSION TWO**
  
  The following hyperparameters were used during training:
  - learning_rate: 1e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - distributed_type: multi-GPU
  - gradient_accumulation_steps: 4
  - 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: 0.05
  - num_epochs: 4
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.10.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1