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
| { | |
| "architectures": [ | |
| "Ministral3ForCausalLM" | |
| ], | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 1, | |
| "dtype": "bfloat16", | |
| "eos_token_id": 2, | |
| "head_dim": 128, | |
| "hidden_act": "silu", | |
| "hidden_size": 3072, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 9216, | |
| "max_position_embeddings": 262144, | |
| "model_type": "ministral3", | |
| "num_attention_heads": 32, | |
| "num_hidden_layers": 26, | |
| "num_key_value_heads": 8, | |
| "pad_token_id": 11, | |
| "rms_norm_eps": 1e-05, | |
| "rope_parameters": { | |
| "beta_fast": 32.0, | |
| "beta_slow": 1.0, | |
| "factor": 16.0, | |
| "llama_4_scaling_beta": 0.1, | |
| "mscale": 1.0, | |
| "mscale_all_dim": 1.0, | |
| "original_max_position_embeddings": 16384, | |
| "rope_theta": 1000000.0, | |
| "rope_type": "yarn", | |
| "type": "yarn" | |
| }, | |
| "sliding_window": null, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.5.1", | |
| "use_cache": true, | |
| "vocab_size": 131072 | |
| } | |