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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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datasets: |
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- Gustavosta/Stable-Diffusion-Prompts |
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model-index: |
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- name: neo-125M-promptgen-v1-act |
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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: 16 |
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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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temperature: 1.1 |
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top_k: 60 |
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top_p: 0.95 |
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repetition_penalty: 7.3 |
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--- |
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# neo-125M-promptgen-v1-act |
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This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) 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: 1.8875 |
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- perplexity: 6.6028 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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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: 0.0001 |
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- train_batch_size: 16 |
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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: 8 |
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- total_train_batch_size: 256 |
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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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| 3.2189 | 0.99 | 33 | 3.0051 | |
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| 2.5466 | 1.99 | 66 | 2.5215 | |
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| 2.2791 | 2.99 | 99 | 2.2881 | |
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| 2.107 | 3.99 | 132 | 2.1322 | |
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| 1.9458 | 4.99 | 165 | 2.0270 | |
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| 1.8664 | 5.99 | 198 | 1.9580 | |
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| 1.8083 | 6.99 | 231 | 1.9177 | |
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| 1.7631 | 7.99 | 264 | 1.8964 | |
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| 1.7369 | 8.99 | 297 | 1.8885 | |
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| 1.766 | 9.99 | 330 | 1.8875 | |
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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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