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--- |
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library_name: peft |
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base_model: facebook/opt-125m |
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pipeline_tag: text-generation |
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datasets: |
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- rubend18/DALL-E-Prompts-OpenAI-ChatGPT |
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--- |
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### Model Description |
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Magic wand 🪄 is a prompt completion model fine-tuned on text generation models, used for text2img models such as Dalle, Midjourney, and Stable Diffusion. It is a smaller version of https://huggingface.co/therealcyberlord/magicwand-gptneo-1.3b |
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## Uses |
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Type something that you want to generate and magic wand will complete it for you. Think about it as a friendly guide when you don't have any ideas about what to generate. |
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**Input:** Chocolate milk |
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**Completion:** Chocolate milk glowing and floating in a barber shop, realistic |
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### Limitations |
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Currently the model produces repetitions with longer sequences |
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## How to Get Started with the Model |
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``` |
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from peft import PeftModel, PeftConfig |
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from transformers import AutoModelForCausalLM |
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config = PeftConfig.from_pretrained("therealcyberlord/magicwand-opt-125m") |
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model = AutoModelForCausalLM.from_pretrained("facebook/opt-125m") |
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model = PeftModel.from_pretrained(model, "therealcyberlord/magicwand-opt-125m") |
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``` |
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## Training Details |
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Trained for 2000 steps on rubend18/DALL-E-Prompts-OpenAI-ChatGPT |
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### Framework versions |
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- PEFT 0.7.2.dev0 |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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[More Information Needed] |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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#### Summary |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
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- **Hardware Type:** [More Information Needed] |
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- **Hours used:** [More Information Needed] |
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- **Cloud Provider:** [More Information Needed] |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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[More Information Needed] |
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#### Software |
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[More Information Needed] |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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[More Information Needed] |
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## More Information [optional] |
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[More Information Needed] |
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## Model Card Authors [optional] |
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[More Information Needed] |
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## Model Card Contact |
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[More Information Needed] |
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### Framework versions |
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- PEFT 0.7.2.dev0 |
