Instructions to use pmysl/c4ai-command-r-plus-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 pmysl/c4ai-command-r-plus-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 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 serve -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 Desktop
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

Atomic Chat

pmysl commited on Apr 12, 2024

Commit

5c12a1a

1 Parent(s): 73b98a9

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -0

README.md CHANGED Viewed

@@ -25,6 +25,7 @@ In the folder `imatrix`, you can find imatrix quants. The importance matrix was
 | Q2_K     | 5.7178    | +/- 0.03418        |
 | Q3_K_L   | 4.6214    | +/- 0.02629        |
 | Q4_K_M   | 4.4625    | +/- 0.02522        |
 ## 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:

 | 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: