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--- |
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tags: |
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- generated_from_trainer |
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- stable diffusion |
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- beautiful |
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- masterpiece |
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datasets: |
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- Gustavosta/Stable-Diffusion-Prompts |
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model-index: |
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- name: tiny-gpt2-magicprompt |
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results: [] |
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widget: |
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- text: "morning sun over Jakarta" |
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example_title: "morning sun" |
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- text: "WARNING: pip is" |
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example_title: "pip" |
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- text: "sentient cheese" |
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example_title: "sentient cheese" |
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- text: "cheeps are" |
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example_title: "cheeps" |
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parameters: |
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min_length: 32 |
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max_length: 64 |
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no_repeat_ngram_size: 1 |
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do_sample: True |
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--- |
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# tiny-gpt2-magicprompt |
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~~Generate/augment your prompt, stable diffusion style.~~ Enter a new dimension of creativity |
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This model is a fine-tuned version of [sshleifer/tiny-gpt2](https://huggingface.co/sshleifer/tiny-gpt2) on the Gustavosta/Stable-Diffusion-Prompts dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 10.7918 |
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- perplexity: 48618.8756 |
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## Intended uses & limitations |
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??? |
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## Training and evaluation data |
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refer to the `Gustavosta/Stable-Diffusion-Prompts` dataset. |
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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: 0.0001 |
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- train_batch_size: 8 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- gradient_accumulation_steps: 32 |
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- total_train_batch_size: 512 |
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- total_eval_batch_size: 4 |
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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: 10.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 10.8201 | 0.96 | 16 | 10.8191 | |
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| 10.8167 | 1.96 | 32 | 10.8145 | |
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| 10.8117 | 2.96 | 48 | 10.8095 | |
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| 10.8058 | 3.96 | 64 | 10.8025 | |
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| 10.7997 | 4.96 | 80 | 10.7989 | |
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| 10.7959 | 5.96 | 96 | 10.7947 | |
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| 10.7934 | 6.96 | 112 | 10.7925 | |
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| 10.7924 | 7.96 | 128 | 10.7919 | |
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| 10.7921 | 8.96 | 144 | 10.7918 | |
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| 10.792 | 9.96 | 160 | 10.7918 | |
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### Framework versions |
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- Transformers 4.25.0.dev0 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.1 |
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