Instructions to use QuantFactory/Promt-generator-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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
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:# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Promt-generator-GGUF: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:# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Promt-generator-GGUF: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:# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Promt-generator-GGUF:Use Docker
docker model run hf.co/QuantFactory/Promt-generator-GGUF: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)
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Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/Promt-generator-GGUF:# Run inference directly in the terminal: llama-cli -hf QuantFactory/Promt-generator-GGUF: