Instructions to use itriedcoding/Sage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use itriedcoding/Sage with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="itriedcoding/Sage", filename="sage-f16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use itriedcoding/Sage with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf itriedcoding/Sage:F16 # Run inference directly in the terminal: llama-cli -hf itriedcoding/Sage:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf itriedcoding/Sage:F16 # Run inference directly in the terminal: llama-cli -hf itriedcoding/Sage:F16
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 itriedcoding/Sage:F16 # Run inference directly in the terminal: ./llama-cli -hf itriedcoding/Sage:F16
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 itriedcoding/Sage:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf itriedcoding/Sage:F16
Use Docker
docker model run hf.co/itriedcoding/Sage:F16
- LM Studio
- Jan
- Ollama
How to use itriedcoding/Sage with Ollama:
ollama run hf.co/itriedcoding/Sage:F16
- Unsloth Studio
How to use itriedcoding/Sage 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 itriedcoding/Sage 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 itriedcoding/Sage to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for itriedcoding/Sage to start chatting
- Atomic Chat new
- Docker Model Runner
How to use itriedcoding/Sage with Docker Model Runner:
docker model run hf.co/itriedcoding/Sage:F16
- Lemonade
How to use itriedcoding/Sage with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull itriedcoding/Sage:F16
Run and chat with the model
lemonade run user.Sage-F16
List all available models
lemonade list
File size: 561 Bytes
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"Hello, how are you today?"
"The weather is nice and sunny."
"I enjoy reading books about machine learning."
"Deep learning has revolutionized artificial intelligence."
"Natural language processing enables computers to understand text."
"Transformers are a type of neural network architecture."
"Training large language models requires significant computational resources."
"Hugging Face provides a platform for sharing machine learning models."
"I am excited to build my own custom AI model."
"Publishing models to the hub allows others to use them."
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