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---
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license: mit
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datasets:
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- fka/awesome-chatgpt-prompts
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- gopipasala/fka-awesome-chatgpt-prompts
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- O1-OPEN/OpenO1-SFT
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- PJMixers-Dev/O1-OPEN_OpenO1-SFT-CustomShareGPT
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- amphora/QwQ-LongCoT-130K
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- amphora/QwQ-LongCoT-130K-2
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- HuggingFaceFW/fineweb-2
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- DAMO-NLP-SG/multimodal_textbook
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- cfahlgren1/react-code-instructions
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- agibot-world/AgiBotWorld-Alpha
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- HuggingFaceTB/finemath
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- HuggingFaceTB/finemath_contamination_report
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language:
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- en
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metrics:
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- accuracy
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- character
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tags:
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- not-for-all-audiences
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---
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## Model Card: UnfilteredAI/Promt-generator |
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### Model Overview |
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The **UnfilteredAI/Promt-generator** is a text generation model designed specifically for creating prompts for text-to-image models. It leverages **PyTorch** and **safetensors** for optimized performance and storage, ensuring that it can be easily deployed and scaled for prompt generation tasks. |
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### Intended Use |
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This model is primarily intended for: |
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- **Prompt generation** for text-to-image models. |
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- Creative AI applications where generating high-quality, diverse image descriptions is critical. |
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- Supporting AI artists and developers working on generative art projects. |
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### How to Use |
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To generate prompts using this model, follow these steps: |
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1. Load the model in your PyTorch environment. |
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2. Input your desired parameters for the prompt generation task. |
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3. The model will return text descriptions based on the input, which can then be used with text-to-image models. |
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**Example Code:** |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("UnfilteredAI/Promt-generator") |
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model = AutoModelForCausalLM.from_pretrained("UnfilteredAI/Promt-generator") |
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prompt = "a red car" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(**inputs) |
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generated_prompt = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(generated_prompt) |
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``` |
