Instructions to use Kezmark/ErniePEUnleashed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

Kezmark commited on 4 days ago

Commit

f1be75b

verified ·

1 Parent(s): ae4c187

Update README.md

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README.md +24 -0

README.md CHANGED Viewed

	@@ -150,3 +150,27 @@ SYSTEM_PROMPTS_PE:
150
151
152	"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.",

     "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.",
+What the training itself tried to teach, and examples it gave of:
+    Examples of creating just a random, good detiled composition, both with and without art styles.
+    Examples of creating a good composition based on genre.
+    Examples of creating a good composition based around a specific emotion.
+    Examples of creating a good composition based on a specific Universe.
+    Examples of Infusing an already existing good composition with art styles, without changing the core structure of the composition.
+    Examples of Enhancing a prompt with both no art style and art style.
+    Examples of creating a more targeted, good composition, ie. examples of good portrait, landscape, cityscape, etc.