Instructions to use dranger003/c4ai-command-r-plus-iMat.GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use dranger003/c4ai-command-r-plus-iMat.GGUF 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 dranger003/c4ai-command-r-plus-iMat.GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf dranger003/c4ai-command-r-plus-iMat.GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf dranger003/c4ai-command-r-plus-iMat.GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf dranger003/c4ai-command-r-plus-iMat.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 dranger003/c4ai-command-r-plus-iMat.GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf dranger003/c4ai-command-r-plus-iMat.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 dranger003/c4ai-command-r-plus-iMat.GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf dranger003/c4ai-command-r-plus-iMat.GGUF:Q4_K_M
Use Docker
docker model run hf.co/dranger003/c4ai-command-r-plus-iMat.GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use dranger003/c4ai-command-r-plus-iMat.GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dranger003/c4ai-command-r-plus-iMat.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": "dranger003/c4ai-command-r-plus-iMat.GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/dranger003/c4ai-command-r-plus-iMat.GGUF:Q4_K_M
- Ollama
How to use dranger003/c4ai-command-r-plus-iMat.GGUF with Ollama:
ollama run hf.co/dranger003/c4ai-command-r-plus-iMat.GGUF:Q4_K_M
- Unsloth Studio
How to use dranger003/c4ai-command-r-plus-iMat.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 dranger003/c4ai-command-r-plus-iMat.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 dranger003/c4ai-command-r-plus-iMat.GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for dranger003/c4ai-command-r-plus-iMat.GGUF to start chatting
- Docker Model Runner
How to use dranger003/c4ai-command-r-plus-iMat.GGUF with Docker Model Runner:
docker model run hf.co/dranger003/c4ai-command-r-plus-iMat.GGUF:Q4_K_M
- Lemonade
How to use dranger003/c4ai-command-r-plus-iMat.GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull dranger003/c4ai-command-r-plus-iMat.GGUF:Q4_K_M
Run and chat with the model
lemonade run user.c4ai-command-r-plus-iMat.GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Update chat templates
I know it's a bit of a pain, but could you update the chat template to the latest chat templates now that llama.cpp supports it?
At least you won't have to requantize everything as I made a handy script that lets you create a new GGUF using the updated tokenizer_config.json file, see the details in the PR. :)
PS: You only have to update the first file in a split GGUF.
@CISCai Thanks for your contribution! I rented a server to create all the quants, which I no longer have so this means I would have to re-download all the quants, update them all (first split where we have chunks) then re-upload everything to HF. Would there be a way to create a new dummy GGUF that has just the metadata maybe?
Unfortunately not. :(
Hopefully some day it can be the norm to just have a small GGUF containing only metadata as the first split.
For those of us at home, how can we run that gguf-new-metadata.py script with the correct settings? I'd be willing to re-upload what I've downloaded myself.
@sealad886 Great! :)
If you already have a working Python3 environment with all the dependencies you can just run the script like this:
python gguf-new-metadata.py input.gguf output.gguf --chat-template-config tokenizer_config.json
If you don't have an environment set up you will need to do that first:
python3 -m venv my-venv
source my-venv/bin/activate
pip install -r requirements.txt
Turns out it's actually faster for me to re-quantize everything than it is to download these on my internet connection. I'm using your imatrix.dat file, and I'll do a couple of hashes on several of the files that I did download to make sure I'm not just creating completely new files....but yeah, I'll upload those soon ish.
Thank you for updating (and the BPE PR). :)