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--- |
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license: 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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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: "If you could hire someone to help you, would it be with cleaning, cooking, or yard work? peter szemraj:" |
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example_title: "help" |
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- text: "What form of public transportation do you prefer? (air, boat, train, bus, 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: "What celebrity would you like to meet at Starbucks for a cup of coffee? 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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length_penalty: 0.7 |
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temperature: 0.65 |
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no_repeat_ngram_size: 2 |
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top_k: 20 |
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do_sample: True |
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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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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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## 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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## Intended uses & limitations |
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> 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](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. |
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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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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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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### Training results |
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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 |
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