Model description

The togethercomputer/RedPajama-INCITE-Base-3B-v1 model finetuned for Paraphrasing and Changing the Tone of the input sentence(to casual/professional/witty). Training data was generated using gpt-35-turbo.

Look at the repo llm-toys for usage and other details.

Try in colab: <a target="blank" href="https://colab.research.google.com/drive/1MSl8IDLjs3rgEv8cPHbJLR8GHh2ucT3"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>

Installation

pip install llm-toys

from llm_toys.tasks import Paraphraser

paraphraser = Paraphraser()
paraphraser.paraphrase("Hey, can yuo hepl me cancel my last order?")
# "Could you kindly assist me in canceling my previous order?"

paraphraser.paraphrase("Hey, can yuo hepl me cancel my last order?", tone="professional")
# "I would appreciate guidance on canceling my previous order."

paraphraser.paraphrase("Hey, can yuo hepl me cancel my last order?", tone="witty")
# "Hey, I need your help with my last order. Can you wave your magic wand and make it disappear?"

Sample training data

{
  "original": "If you have any further questions, feel free to ask.",
  "casual": "Got more questions? Feel free to ask away. I'm here to help!",
  "professional": "Should you have any additional inquiries, please don't hesitate to ask.",
  "witty": "Curiosity is always in style! If you have more mysteries to solve, I'm all ears!",
  "paraphrase": "Don't hesitate to ask if you have any more questions."
}

Training params

{
  "batch_size": 8,
  "eval_ratio": 0.1,
  "eval_steps": 100,
  "gradient_accumulation_steps": 1,
  "learning_rate": 0.0001,
  "logging_steps": 100,
  "lora_alpha": 32,
  "lora_dropout": 0.05,
  "lora_r": 16,
  "max_length": 128,
  "model_name": "togethercomputer/RedPajama-INCITE-Base-3B-v1",
  "num_train_epochs": 3,
  "seed": 10,
  "task_type": "paraphrase_tone",
  "use_aim": True
}

Training curve

train_eval_loss

Training procedure

The following bitsandbytes quantization config was used during training:

Framework versions