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--- |
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license: other |
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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: opt-350m-magicprompt-v2 |
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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: 96 |
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no_repeat_ngram_size: 1 |
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do_sample: True |
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--- |
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# opt-350m-magicprompt-v2 |
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This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) 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: 1.2987 |
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- eval_runtime = 0:00:06.79 |
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- eval_steps_per_second = 16.623 |
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- perplexity = 3.6644 |
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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: 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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| 2.8568 | 0.95 | 16 | 2.5937 | |
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| 2.2487 | 1.95 | 32 | 2.1050 | |
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| 1.9011 | 2.95 | 48 | 1.8082 | |
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| 1.6837 | 3.95 | 64 | 1.6178 | |
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| 1.4887 | 4.95 | 80 | 1.4897 | |
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| 1.3812 | 5.95 | 96 | 1.4017 | |
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| 1.2944 | 6.95 | 112 | 1.3437 | |
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| 1.2574 | 7.95 | 128 | 1.3127 | |
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| 1.2325 | 8.95 | 144 | 1.3009 | |
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| 1.2223 | 9.95 | 160 | 1.2987 | |
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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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