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README.md
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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-
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results: []
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widget:
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- text: "morning sun over Jakarta"
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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:
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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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top_k: 60
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top_p: 0.95
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repetition_penalty:
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---
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# neo-125M-
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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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## Training procedure
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datasets:
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- Gustavosta/Stable-Diffusion-Prompts
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model-index:
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- name: gpt-neo-125M-magicprompt-SD
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results: []
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widget:
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- text: "morning sun over Jakarta"
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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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top_k: 50
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top_p: 0.95
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repetition_penalty: 5.5
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---
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# gpt-neo-125M-magicprompt-SD
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Generate/augment your prompt, stable diffusion style.
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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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## 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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