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UnfilteredAI/Promt-generator

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  • files 10
  • author_summary 14 models
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Abliteration classifier · v1.0.0
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Unclassified

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UNKNOWN
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Downloads · 30-day
174
↑ 16,855% in 90 days
Likes
43
Model age
2.5y ago
created 2024-04-15
Downloads over time
Now17.8K→from105↑16,855%
06.5K13.1K19.6K105 on Jul 24, 202417.8K on Oct 11Jul '24Nov '24Mar '25Jul '25Nov '25MarJul
Jul 24, 2024 → Oct 11 · 155 snapshots · spans 809 days

Metadata

License
mit
Tags
transformers pytorch safetensors bloom text-generation license:mit text-generation-inference endpoints_compatible region:us

Related

Total size
4.17 GB
Files
10
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2024-10-22 14:54

Files by quantization

Auxiliary files 10 files 4.18 GB
pytorch_model.bin 2.08 GB 7fcbdaa0 download
model.safetensors 2.08 GB eecaa7c1 download
training_args.bin 3.30 KB a3ec16c1 download
tokenizer.json 13.8 MB 8f6efc66 download
.gitattributes 1.45 KB 48677797 download
README.md 1.38 KB 7fa15e2c download
config.json 807 B 97ef1170 download
tokenizer_config.json 323 B 3ce2a295 download
special_tokens_map.json 96.0 B fdafe480 download
.gitignore 13.0 B 0348ea97 download

README current version from Hugging Face


license: mit

Model Card: UnfilteredAI/Promt-generator

Model Overview

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.

Intended Use

This model is primarily intended for:

  • Prompt generation for text-to-image models.
  • Creative AI applications where generating high-quality, diverse image descriptions is critical.
  • Supporting AI artists and developers working on generative art projects.

How to Use

To generate prompts using this model, follow these steps:

  1. Load the model in your PyTorch environment.
  2. Input your desired parameters for the prompt generation task.
  3. The model will return text descriptions based on the input, which can then be used with text-to-image models.

Example Code:

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("UnfilteredAI/Promt-generator")
model = AutoModelForCausalLM.from_pretrained("UnfilteredAI/Promt-generator")

prompt = "a red car"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs)
generated_prompt = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(generated_prompt)

README history 2 versions

The author's README evolved over time. Click a version to see its content at that point.

  1. 2024-10-22Update README.md5afb6bb1.4 KB
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Discussions 7 threads

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