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RichardErkhov/UnfilteredAI_-_Promt-generator-8bits

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Downloads · 30-day
15
↑ 1,850% in 90 days
Likes
0
Model age
18mo ago
created 2025-03-23
Downloads over time
Now39→from2↑1,850%
01429432 on Mar 19, 202539 on Oct 1139 on Oct 4Mar '25Jun '25Sep '25Dec '25MarJunSep
Mar 19, 2025 → Oct 11 · 121 snapshots · spans 571 days

Metadata

Tags
safetensors bloom 8-bit bitsandbytes region:us

Related

Total size
780 MB
Files
8
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2025-03-23 02:20

Files by quantization

Auxiliary files 8 files 800 MB
model.safetensors 780 MB cc756046 download
tokenizer.json 20.8 MB d963066d download
README.md 1.75 KB e993004a download
.gitattributes 1.53 KB 52373fe2 download
config.json 1.25 KB 43fc4baf download
tokenizer_config.json 1.03 KB cb6690b4 download
special_tokens_map.json 552 B a782b2f1 download
generation_config.json 154 B 38cc17d1 download

README current version from Hugging Face

Quantization made by Richard Erkhov.

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Promt-generator - bnb 8bits

Original model description:

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 1 version

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

  1. 2025-03-23uploaded readme889fe751.8 KB
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