Instructions to use pmysl/c4ai-command-r-plus-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use pmysl/c4ai-command-r-plus-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="pmysl/c4ai-command-r-plus-GGUF", filename="command-r-plus-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 pmysl/c4ai-command-r-plus-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf pmysl/c4ai-command-r-plus-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf pmysl/c4ai-command-r-plus-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf pmysl/c4ai-command-r-plus-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf pmysl/c4ai-command-r-plus-GGUF: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 pmysl/c4ai-command-r-plus-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf pmysl/c4ai-command-r-plus-GGUF: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 pmysl/c4ai-command-r-plus-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf pmysl/c4ai-command-r-plus-GGUF:Q4_K_M
Use Docker
docker model run hf.co/pmysl/c4ai-command-r-plus-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use pmysl/c4ai-command-r-plus-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "pmysl/c4ai-command-r-plus-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "pmysl/c4ai-command-r-plus-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/pmysl/c4ai-command-r-plus-GGUF:Q4_K_M
- Ollama
How to use pmysl/c4ai-command-r-plus-GGUF with Ollama:
ollama run hf.co/pmysl/c4ai-command-r-plus-GGUF:Q4_K_M
- Unsloth Studio new
How to use pmysl/c4ai-command-r-plus-GGUF 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 pmysl/c4ai-command-r-plus-GGUF 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 pmysl/c4ai-command-r-plus-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for pmysl/c4ai-command-r-plus-GGUF to start chatting
- Docker Model Runner
How to use pmysl/c4ai-command-r-plus-GGUF with Docker Model Runner:
docker model run hf.co/pmysl/c4ai-command-r-plus-GGUF:Q4_K_M
- Lemonade
How to use pmysl/c4ai-command-r-plus-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull pmysl/c4ai-command-r-plus-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.c4ai-command-r-plus-GGUF-Q4_K_M
List all available models
lemonade list
Ollama can not create
I have tried merging two files using the 'cat' command and loading them, also attempted loading the two files individually in the 'modelfile', but none of these methods worked in Ollama, even though I updated to the latest version of Ollama.
These weights are split with gguf-split so you must merge them like this:
./gguf-split --merge /path/to/command-r-plus-f16-00001-of-00005.gguf /path/to/command-r-plus-f16-combined.gguf
cat won't work here
Thanks !
Sorry I didn't read the README carefully.
Running on llama.cpp went fine, but it seems ollama prefers a single file rather than split ones.
Where can we get the script ./gguf-split ?
gguf-split is part of llama-cpp
You have to build llama-cpp. (from github: https://github.com/ggerganov/llama.cpp)
Clone it like this:git clone https://github.com/ggerganov/llama.cpp.git
In order to prevent building everything from that library, I used cmake to configure the project (I think it's described on the github page):
mkdir buildcd buildcmake ..
But instead of using cmake --build . --config Release, I used the following:make -j 12 gguf-split
(Replace 12 with the number of cores/processors you want to use for building)
This should result in a gguf-split executable in the build/bin/ directory (relative from the git repo you downloaded/cloned)
This seems to work on linux/unix like systems (haven't tried it on Apple), and you might have to install cmake (and if you don't have it, gcc and its build chain... but then you probably don't want to go through all the hassle, maybe....)