Instructions to use cortexso/command-r with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- 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
Create README.md
Browse files
README.md
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---
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license: cc-by-nc-4.0
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---
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## Overview
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C4AI Command-R is a research release of a 35 billion parameter highly performant generative model. Command-R is a large language model with open weights optimized for a variety of use cases including reasoning, summarization, and question answering. Command-R has the capability for multilingual generation evaluated in 10 languages and highly performant RAG capabilities.
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## Variants
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| No | Variant | Cortex CLI command |
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| --- | --- | --- |
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| 1 | [35b-gguf](https://huggingface.co/cortexhub/command-r/tree/35B-gguf) | `cortex run command-r:35b-gguf` |
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## Use it with Jan (UI)
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1. Install **Jan** using [Quickstart](https://jan.ai/docs/quickstart)
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2. Use in Jan model Hub:
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```
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cortexhub/command-r
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```
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## Use it with Cortex (CLI)
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1. Install **Cortex** using [Quickstart](https://cortex.jan.ai/docs/quickstart)
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2. Run the model with command:
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```
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cortex run command-r
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```
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## Credits
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- **Author:** Cohere For AI: [cohere.for.ai](https://cohere.for.ai/)
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- **Converter:** [Homebrew](https://www.homebrew.ltd/)
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- **Original License:** [Licence](https://cohere.com/c4ai-cc-by-nc-license)
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