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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+opt-350m-multiprompt - AWQ
+- Model creator: https://huggingface.co/pszemraj/
+- Original model: https://huggingface.co/pszemraj/opt-350m-multiprompt/
+
+
+
+
+Original model description:
+---
+license: other
+tags:
+- generated_from_trainer
+- text generation
+- stable diffusion
+- midjourney
+- text2image
+- text to image
+- prompt augment
+- prompt engineering
+thumbnail: https://i.imgur.com/DeKNHtC.jpg
+datasets:
+- pszemraj/text2image-multi-prompt
+widget:
+ - text: "morning sun over Jakarta"
+   example_title: "morning sun"
+ - text: "WARNING: pip is"
+   example_title: "pip"
+ - text: "sentient cheese"
+   example_title: "sentient cheese"
+ - text: "cheeps are"
+   example_title: "cheeps"
+ - text: "avocado armchair"
+   example_title: "creative prompt"
+ - text: "Landscape of"
+   example_title: "landscape"
+parameters:
+  min_length: 16
+  max_length: 96
+  no_repeat_ngram_size: 1
+  do_sample: True
+---
+
+
+# pszemraj/opt-350m-multiprompt
+
+<a href="https://colab.research.google.com/gist/pszemraj/bdd1238ee4b8330aeec6774a16f9a677/opt-350m-multiprompt-demo.ipynb">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+</a>
+
+Generate/augment your prompt with a model trained on a large & diverse prompt dataset.
+
+This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the pszemraj/text2image-prompts-multi dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.6669
+- eval steps per second: 16.21
+- perplexity: 5.29
+
+## Example
+
+
+![landscape of florida](https://i.imgur.com/DeKNHtC.jpg)
+
+<br>
+
+_The above example was created with [DALL-E 2](https://labs.openai.com/sc/YbiY2kkuQeODzHNwUHn4D5RN) but will of course work with any text2image model._
+
+## Intended uses & limitations
+
+- The model will generate augmentations that are biased towards the training data, i.e. what people already asked for in the SD/midjourney discords, etc. Creating a larger dataset was an attempt at mitigating this through more data from different datasets.
+
+## Training and evaluation data
+
+See the `pszemraj/text2image-prompts-multi` dataset card for details. The dataset is a compilation of several text-to-image prompt datasets on huggingface  :)
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 8
+- eval_batch_size: 4
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 2
+- gradient_accumulation_steps: 16
+- total_train_batch_size: 256
+- total_eval_batch_size: 8
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.04
+- num_epochs: 4.0
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss |
+|:-------------:|:-----:|:----:|:---------------:|
+| 2.1677        | 1.0   | 990  | 2.0888          |
+| 1.856         | 2.0   | 1980 | 1.8215          |
+| 1.6864        | 3.0   | 2970 | 1.6935          |
+| 1.6228        | 4.0   | 3960 | 1.6670          |
+
+
+### Framework versions
+
+- Transformers 4.25.0.dev0
+- Pytorch 1.13.0+cu117
+- Datasets 2.6.1
+- Tokenizers 0.13.1
+
+