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 Settings
- llama.cpp
How to use CMM7590/Lilith_AI_8B with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -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 serve -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
- Atomic Chat new
- 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
Update README.md
Browse files
README.md
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## About
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This model is a **LoRA fine-tuned LLM** based on the [Sao10K/Llama-3.1-8B-Stheno-v3.4](https://huggingface.co/Sao10K/Llama-3.1-8B-Stheno-v3.4/
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The dataset is in **ShareGPT format** and was created by C.M.M.
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## License
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This project is released under the **[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)** license.
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You are free to use, share, and adapt the model, but **you must give appropriate credit** to C.M.M. for the training dataset and this project.
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---
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## About
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This model is a **LoRA fine-tuned LLM** based on the [Sao10K/Llama-3.1-8B-Stheno-v3.4](https://huggingface.co/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**.
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The dataset is in **ShareGPT format** and was created by C.M.M.
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## License
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You are free to use, share, and adapt the model, but **you must give appropriate credit** to C.M.M. for the training dataset and this project.
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