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---
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.
## 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 |