Libraries llama-cpp-python How to use cortexso/command-r with llama-cpp-python:
# !pip install llama-cpp-python
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="cortexso/command-r",
filename="c4ai-command-r-08-2024-q2_k.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 cortexso/command-r with llama.cpp:
Install from brew brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cortexso/command-r:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf cortexso/command-r:Q4_K_M Install from WinGet (Windows) winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cortexso/command-r:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf cortexso/command-r: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 cortexso/command-r:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf cortexso/command-r: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 cortexso/command-r:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cortexso/command-r:Q4_K_M Use Docker docker model run hf.co/cortexso/command-r:Q4_K_M LM Studio Jan vLLM How to use cortexso/command-r with vLLM:
Install from pip and serve model # Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cortexso/command-r"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "cortexso/command-r",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}' Use Docker docker model run hf.co/cortexso/command-r:Q4_K_M Ollama How to use cortexso/command-r with Ollama:
ollama run hf.co/cortexso/command-r:Q4_K_M Unsloth Studio new How to use cortexso/command-r 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 cortexso/command-r 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 cortexso/command-r to start chatting Using HuggingFace Spaces for Unsloth # No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for cortexso/command-r to start chatting Docker Model Runner How to use cortexso/command-r with Docker Model Runner:
docker model run hf.co/cortexso/command-r:Q4_K_M Lemonade How to use cortexso/command-r with Lemonade:
Pull the model # Download Lemonade from https://lemonade-server.ai/
lemonade pull cortexso/command-r:Q4_K_M Run and chat with the model lemonade run user.command-r-Q4_K_M List all available models lemonade list 
Install from pip and serve model
# Install vLLM from pip: pip install vllm# Start the vLLM server: vllm serve "cortexso/command-r"# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cortexso/command-r", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'