Instructions to use Kezmark/ErniePEUnleashed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Kezmark/ErniePEUnleashed with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Kezmark/ErniePEUnleashed", filename="ErniePEUnleashed-Q8_0.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Kezmark/ErniePEUnleashed with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Kezmark/ErniePEUnleashed:Q8_0 # Run inference directly in the terminal: llama-cli -hf Kezmark/ErniePEUnleashed:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Kezmark/ErniePEUnleashed:Q8_0 # Run inference directly in the terminal: llama-cli -hf Kezmark/ErniePEUnleashed:Q8_0
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 Kezmark/ErniePEUnleashed:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf Kezmark/ErniePEUnleashed:Q8_0
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 Kezmark/ErniePEUnleashed:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Kezmark/ErniePEUnleashed:Q8_0
Use Docker
docker model run hf.co/Kezmark/ErniePEUnleashed:Q8_0
- LM Studio
- Jan
- vLLM
How to use Kezmark/ErniePEUnleashed with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Kezmark/ErniePEUnleashed" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Kezmark/ErniePEUnleashed", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Kezmark/ErniePEUnleashed:Q8_0
- Ollama
How to use Kezmark/ErniePEUnleashed with Ollama:
ollama run hf.co/Kezmark/ErniePEUnleashed:Q8_0
- Unsloth Studio new
How to use Kezmark/ErniePEUnleashed 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 Kezmark/ErniePEUnleashed 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 Kezmark/ErniePEUnleashed to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Kezmark/ErniePEUnleashed to start chatting
- Docker Model Runner
How to use Kezmark/ErniePEUnleashed with Docker Model Runner:
docker model run hf.co/Kezmark/ErniePEUnleashed:Q8_0
- Lemonade
How to use Kezmark/ErniePEUnleashed with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Kezmark/ErniePEUnleashed:Q8_0
Run and chat with the model
lemonade run user.ErniePEUnleashed-Q8_0
List all available models
lemonade list
Update README.md
Browse files
README.md
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@@ -82,3 +82,30 @@ This is the list of Art Styles that it has been trained on:
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If you want to use a specific art style, I would very much suggest that you use the full name of the art style you want from this list in the prompt, otherwise I won’t guarantee how well it will infuse art style.
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Mind you, a lot of the naming in this list is just because they are based on my own developing library of style prompts, that aren’t necessarily the thing you might expect with some of the more niche stuff, however you can check out the examples in the Art style results.zip, where you have both the text file with before and after, as well as all the resulting pngs. Now those should just be taken as a first impression of how the art style looks more than anything, as they are one-time renders, all with the same exact settings and static seed. These weren’t me trying to get the best results. Also, all of the examples are based on one sentence prompts, however the model is trained to improve already existing prompts as well, that just aren’t fully fleshed out, or to infuse already well built prompts with the desired art style.
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If you want to use a specific art style, I would very much suggest that you use the full name of the art style you want from this list in the prompt, otherwise I won’t guarantee how well it will infuse art style.
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Mind you, a lot of the naming in this list is just because they are based on my own developing library of style prompts, that aren’t necessarily the thing you might expect with some of the more niche stuff, however you can check out the examples in the Art style results.zip, where you have both the text file with before and after, as well as all the resulting pngs. Now those should just be taken as a first impression of how the art style looks more than anything, as they are one-time renders, all with the same exact settings and static seed. These weren’t me trying to get the best results. Also, all of the examples are based on one sentence prompts, however the model is trained to improve already existing prompts as well, that just aren’t fully fleshed out, or to infuse already well built prompts with the desired art style.
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Do you need any specific system prompts?
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I did the training with very generic, simplistic system prompts in the dataset, depending on task, with 4 different, but similar variations for both composition building and prompt enhancement tasks, specifically so it doesn’t learn x system prompt is what it needs to reply to in a certain way, and hopefully learn more of what a good composition is. The examples themselves are all done with just the generic, default system prompt in the ernie workflow, and not a specific one of mine.
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For reference, if someone really wants them, these are the system prompts I used in the dataset:
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SYSTEM_PROMPTS_COMPOSITION:
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"You are an expert composition writer. The compositions you write will be used for AI image generation. You write detailed, structured compositions with concrete visuals and clear visual hierarchy.",
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"You are a specialist in writing image generation prompts. Your task is to produce compositions that are highly detailed, logically structured, and grounded in concrete visual elements. Each composition must include a clear visual hierarchy, guiding the viewer's eye through the scene.",
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"You write detailed, well-structured composition prompts for AI image generation. Your descriptions focus on concrete visuals and a clear ordering of visual importance. You are an expert at this craft.",
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"As an expert composition writer for AI image generation, you create prompts that are rich in concrete visual detail and structured with a clear visual hierarchy. Your compositions are thorough, logically arranged, and ready to be interpreted by an image generation model.",
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SYSTEM_PROMPTS_PE:
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"You are a prompt enhancing expert for AI image generation. The composition you enhance will be fed directly to an image generation model. The model responds well to clear spatial hierarchy, specific visual details, and purposeful compositional logic. Your compositions should be detailed and well structured and expand on the user's input, be it a shorter prompt or a simple idea or request.",
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"You are an expert at enhancing prompts for AI image generation. The compositions you produce will go directly into an image model, which performs best with clear spatial hierarchy, precise visual details, and deliberate compositional logic. Your enhanced prompts must be detailed, well structured, and expand on whatever the user provides – whether a short phrase, a simple idea, or a basic request.",
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"Your role is to improve prompts for AI image generation. The final composition will be fed directly to an image model, which responds strongly to clear visual hierarchy, concrete specifics, and intentional composition. You must produce detailed, well-organized prompts that build upon the user's input – no matter how short or simple the original idea.",
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"You are a prompt enhancement specialist for AI image generation. The composition you create will be interpreted directly by an image generation model, which works best when given a clear spatial structure, vivid visual details, and a logical compositional flow. Your job is to produce rich, structured prompts that expand on the user's starting point – whether it's a brief phrase, a vague concept, or a straightforward request.",
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