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
- llama.cpp
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
File size: 1,444 Bytes
86918a8 7d4fb9d 86918a8 4caa150 7d4fb9d 4caa150 7d4fb9d 4caa150 7d4fb9d 4caa150 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | ---
license: cc-by-nc-4.0
pipeline_tag: text-generation
base_model: CohereForAI/c4ai-command-r-plus
---
# Command R+ GGUF
## Description
This repository contains experimental GGUF weights that are currently compatible with [pull request #6491](https://github.com/ggerganov/llama.cpp/pull/6491) in the `llama.cpp`. I will update them once support for Command R+ is merged into the llama.cpp repository.
## Getting started
1. Clone the `Carolinabanana/llama.cpp` repository:
```bash
git clone https://github.com/Carolinabanana/llama.cpp.git llama.cpp-fork
cd llama.cpp-fork
git reset --hard 8b6577bd631fec33eeadb4b9dfc5a07ed2118148
```
2. Build it using `make`
3. Use it in the same way as the regular `llama.cpp`. If you're unsure of how to start, you can use the following command as a starting point:
```bash
./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
```
## 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:
```bash
./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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