Instructions to use QuantFactory/Promt-generator-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Promt-generator-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Promt-generator-GGUF",
	filename="Promt-generator.Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/Promt-generator-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Promt-generator-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Promt-generator-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Promt-generator-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Promt-generator-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf QuantFactory/Promt-generator-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Promt-generator-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf QuantFactory/Promt-generator-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Promt-generator-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Promt-generator-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/Promt-generator-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Promt-generator-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Promt-generator-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/Promt-generator-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/Promt-generator-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Promt-generator-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Promt-generator-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Promt-generator-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Promt-generator-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Promt-generator-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Promt-generator-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Promt-generator-GGUF

This is quantized version of UnfilteredAI/Promt-generator created using llama.cpp

Original Model Card

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:

Load the model in your PyTorch environment.
Input your desired parameters for the prompt generation task.
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)

Downloads last month: 104

GGUF

Model size

0.8B params

Architecture

bloom

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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