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pszemraj
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opt-peter-2.7B

Text Generation
Transformers
PyTorch
opt
Generated from Trainer
OPT
non-commercial
dialogue
chatbot
ai-msgbot
text-generation-inference
Inference Endpoints
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opt-peter-2.7B / README.md
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metadata
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 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

  • 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 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