Instructions to use Sweaterdog/Andy-3.6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Sweaterdog/Andy-3.6 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Sweaterdog/Andy-3.6", filename="Andy-3.6.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 Sweaterdog/Andy-3.6 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 Sweaterdog/Andy-3.6:Q4_K_M # Run inference directly in the terminal: llama cli -hf Sweaterdog/Andy-3.6:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Sweaterdog/Andy-3.6:Q4_K_M # Run inference directly in the terminal: llama cli -hf Sweaterdog/Andy-3.6: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 Sweaterdog/Andy-3.6:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Sweaterdog/Andy-3.6: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 Sweaterdog/Andy-3.6:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Sweaterdog/Andy-3.6:Q4_K_M
Use Docker
docker model run hf.co/Sweaterdog/Andy-3.6:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Sweaterdog/Andy-3.6 with Ollama:
ollama run hf.co/Sweaterdog/Andy-3.6:Q4_K_M
- Unsloth Studio
How to use Sweaterdog/Andy-3.6 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 Sweaterdog/Andy-3.6 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 Sweaterdog/Andy-3.6 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Sweaterdog/Andy-3.6 to start chatting
- Atomic Chat new
- Docker Model Runner
How to use Sweaterdog/Andy-3.6 with Docker Model Runner:
docker model run hf.co/Sweaterdog/Andy-3.6:Q4_K_M
- Lemonade
How to use Sweaterdog/Andy-3.6 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Sweaterdog/Andy-3.6:Q4_K_M
Run and chat with the model
lemonade run user.Andy-3.6-Q4_K_M
List all available models
lemonade list
Update README.md
Browse files
README.md
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# How to Install / Setup
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2. Download the Modelfile
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3. Once downloaded, open Modelfile in a text editor, and change the path to the download location of the gguf file
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4. When changed, save the file, and open command terminal
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5. *(Optional if CMD isn't opened via file explorer)* Navigate to the correct directory using "cd"
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6. Run the command ```ollama create sweaterdog/Andy-3.6 -f Modelfile``` If you want multiple models, include a tag afterwards. Example: sweaterdog/Andy-3.5:mini-fp16 or sweaterdog/Andy-3.6:q2_k
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7. Go to a profile in MindCraft
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8. Change the model to be ```sweaterdog/Andy-3.6``` *Or whatever you named your model*
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9. Ensure you have the emdedding tag set to Ollama, like below
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```
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{
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"name": "andy-3.6",
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"model": "Sweaterdog/Andy-3.6",
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"embedding": "ollama"
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}
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```
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10. Enjoy playing with an AI that you are hosting!
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# How was model trained?
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# How to Install / Setup
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**Installing Andy-3.6 is much easier and Andy-3.5!**
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1. In the top right of this repo, click "Use This Model"
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2. Next, click Ollama
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3. Pick your quantization
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4. Run the command in your terminal
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5. Now you have Andy-3.6 installed!
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# How was model trained?
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