Instructions to use CMM7590/Lilith_AI_8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use CMM7590/Lilith_AI_8B with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="CMM7590/Lilith_AI_8B", filename="Lilith_AI_8B_F16.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use CMM7590/Lilith_AI_8B with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf CMM7590/Lilith_AI_8B:Q4_K_M # Run inference directly in the terminal: llama-cli -hf CMM7590/Lilith_AI_8B:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf CMM7590/Lilith_AI_8B:Q4_K_M # Run inference directly in the terminal: llama-cli -hf CMM7590/Lilith_AI_8B: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 CMM7590/Lilith_AI_8B:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf CMM7590/Lilith_AI_8B: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 CMM7590/Lilith_AI_8B:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf CMM7590/Lilith_AI_8B:Q4_K_M
Use Docker
docker model run hf.co/CMM7590/Lilith_AI_8B:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use CMM7590/Lilith_AI_8B with Ollama:
ollama run hf.co/CMM7590/Lilith_AI_8B:Q4_K_M
- Unsloth Studio
How to use CMM7590/Lilith_AI_8B 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 CMM7590/Lilith_AI_8B 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 CMM7590/Lilith_AI_8B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for CMM7590/Lilith_AI_8B to start chatting
- Docker Model Runner
How to use CMM7590/Lilith_AI_8B with Docker Model Runner:
docker model run hf.co/CMM7590/Lilith_AI_8B:Q4_K_M
- Lemonade
How to use CMM7590/Lilith_AI_8B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull CMM7590/Lilith_AI_8B:Q4_K_M
Run and chat with the model
lemonade run user.Lilith_AI_8B-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 CMM7590/Lilith_AI_8B:# Run inference directly in the terminal:
llama-cli -hf CMM7590/Lilith_AI_8B: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 CMM7590/Lilith_AI_8B:# Run inference directly in the terminal:
./llama-cli -hf CMM7590/Lilith_AI_8B: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 CMM7590/Lilith_AI_8B:# Run inference directly in the terminal:
./build/bin/llama-cli -hf CMM7590/Lilith_AI_8B:Use Docker
docker model run hf.co/CMM7590/Lilith_AI_8B:Lilith AI
An LLM trained to act like Lilith from The NOexistenceN of you AND me.
About
This model is a LoRA fine-tuned LLM based on the Sao10K/Llama-3.1-8B-Stheno-v3.4 base model. It has been trained on lines directly extracted from the original game to simulate the personality and speech patterns of Lilith.
The cloud-hosted model can be found here: lilith.nullexistence.net
Folder & File Overview
- System Prompt.txt: A system prompt tailored specifically for this model.
- Lilith_AI_8B_Q4_0.gguf: The Q4_0 model file.
- Lilith_AI_8B_Q4_K_M.gguf: The Q4_K_M model file.
- Lilith_AI_8B_Q6_K.gguf: The Q6_K model file.
- Lilith_AI_8B_Q8_0.gguf: The Q8_0 model file.
- Lilith_AI_8B_F16.gguf: The F16 model file.
License
You are free to use, share, and adapt the model, but you must give appropriate credit to C.M.M. for training the model and this project.
Disclaimer
This model uses the character Lilith from The NOexistenceN series. This project is fan-made and not affiliated with, endorsed by, or sponsored by the original creators or copyright holders. All intellectual property related to the character belongs to the original copyright holders. Use of this model is for personal, educational, or research purposes only.
- Downloads last month
- 89
4-bit
6-bit
8-bit
16-bit
Model tree for CMM7590/Lilith_AI_8B
Base model
Sao10K/Llama-3.1-8B-Stheno-v3.4
Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf CMM7590/Lilith_AI_8B:# Run inference directly in the terminal: llama-cli -hf CMM7590/Lilith_AI_8B: