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--- |
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license: |
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- other |
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- apache-2.0 |
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tags: |
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- generated_from_trainer |
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- text-generation |
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- OPT |
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- non-commercial |
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- dialogue |
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- chatbot |
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- ai-msgbot |
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library_name: transformers |
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pipeline_tag: text-generation |
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widget: |
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- text: 'If you could live anywhere, where would it be? peter szemraj:' |
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example_title: live anywhere |
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- text: 'What would you sing at Karaoke night? peter szemraj:' |
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example_title: Karaoke |
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- text: >- |
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If you could hire someone to help you, would it be with cleaning, cooking, |
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or yard work? peter szemraj: |
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example_title: help |
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- text: >- |
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What form of public transportation do you prefer? (air, boat, train, bus, |
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car, etc.) peter szemraj: |
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example_title: transportation |
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- text: 'What''s your favorite zoo animal? peter szemraj:' |
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example_title: animal |
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- text: 'Do you like or dislike surprises? Why or why not? peter szemraj:' |
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example_title: surprises |
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- text: >- |
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What celebrity would you like to meet at Starbucks for a cup of coffee? |
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peter szemraj: |
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example_title: 'celebrity ' |
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inference: |
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parameters: |
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min_length: 2 |
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max_length: 64 |
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temperature: 0.5 |
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no_repeat_ngram_size: 2 |
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repetition_penalty: 4.5 |
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--- |
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# pszemraj/opt-peter-2.7B |
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<a href="https://colab.research.google.com/gist/pszemraj/26a69775c9d012051396ab5ae980f5c1/example-text-gen-pszemraj-opt-peter-2-7b.ipynb"> |
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> |
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</a> |
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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 :) |
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Test it out on Google Colab by clicking the button above. |
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## Model description |
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- Exploring to see how OPT does in terms of dialogue/conversational applications |
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- Seems to do a lot better than GPT-Neo with similar training parameters |
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- you can create your own digital clone and deploy it leveraging [this repository I am working on](https://github.com/pszemraj/ai-msgbot). |
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### sharded checkpoint |
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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). |
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The `pszemraj/opt-peter-2.7B-sharded` model can be used as a drop-in replacement for this one for all use cases. |
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## Intended uses & limitations |
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> 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) |
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- 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. |
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- alternatively, you can message [a bot on telegram](http://t.me/GPTPeter_bot) where I test LLMs for dialogue generation |
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- **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. |
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## Training and evaluation data |
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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. |
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## Training procedure |
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### Training hyperparameters |
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**SESSION ONE** |
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The following hyperparameters were used during training: |
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- learning_rate: 4e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.01 |
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- num_epochs: 3 |
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**SESSION TWO** |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.05 |
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- num_epochs: 4 |
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### Framework versions |
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- Transformers 4.19.2 |
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- Pytorch 1.10.0+cu113 |
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- Datasets 2.2.2 |
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- Tokenizers 0.12.1 |
