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

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

Fast work by the people on the llama.cpp team

by qaraleza - opened Apr 8, 2024

Discussion

qaraleza

Apr 8, 2024

Thanks!

yamikumods

Apr 8, 2024

Big thanks for the team's contribution to solve multiple problems.
It seems someone still reporting broken output problem on Metal background?
I bumped into same phenomenon with iq3 variation.
I hope this solves.

dranger003

Owner Apr 8, 2024

Yes, a lot of the code had to be updated to int64 because the tensor size of this model exceeds max int32 and there was an overflow. This is currently affecting the metal build (and maybe other backends) and the perplexity tool as well, as far as I know. I tested the CUDA backend successfully with all the weights from this HF repo.

yamikumods

Apr 8, 2024

I'm not sure how often the tensor size itself is referred in the code, but I guess it need thorough revision.
So, I'm gonna wait with patience.

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