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 10 days ago

Commit

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@@ -114,6 +114,8 @@ Mind you, a lot of the naming in this list is just because they are based on my
 Do you need any specific system prompts?
 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.
 For reference, if someone really wants them, these are the system prompts I used in the dataset:

 Do you need any specific system prompts?
+No, this should work with a simple or no system prompt even, especially for just generating compositions.
 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.
 For reference, if someone really wants them, these are the system prompts I used in the dataset: