Instructions to use pmysl/c4ai-command-r-plus-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

Command R+ GGUF

Description

This repository contains GGUF weights for llama.cpp. Support for them was added in release b2636. Since commit dd2d53a, all weights in this repo have chat templates.

In the folder imatrix, you can find imatrix quants. The importance matrix was trained using kalomaze's groups_merged.txt.

Quickstart

Ensure that you have release b2636 or newer.
Start with the command below:

./main -p "<|START_OF_TURN_TOKEN|><|USER_TOKEN|>Who are you?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>" --color -m /path/to/command-r-plus-Q3_K_L-00001-of-00002.gguf

Perplexity on `wikitext-2-raw` [WIP]

Variant	PPL Value	Standard Deviation
Q2_K	5.7178	+/- 0.03418
Q3_K_L	4.6214	+/- 0.02629
Q4_K_M	4.4625	+/- 0.02522
f16	4.3845	+/- 0.02468

Merging Weights

After commit 8a28d12, weights are split with gguf-split, which means that you don't have to merge weights. Simply pass the first split, as in the example above, and llama.cpp will automatically load all splits. If, for some reason, you want to merge splits, you can use the following command:

./gguf-split --merge /path/to/command-r-plus-f16-00001-of-00005.gguf /path/to/command-r-plus-f16-combined.gguf

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GGUF

Model size

104B params

Architecture

command-r

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for pmysl/c4ai-command-r-plus-GGUF

Base model

CohereLabs/c4ai-command-r-plus

Quantized

(10)