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 :)

Test it out on Google Colab by clicking the button above.

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.

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.

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

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 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 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