Instructions to use backyardai/c4ai-command-r-plus-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use backyardai/c4ai-command-r-plus-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("backyardai/c4ai-command-r-plus-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use backyardai/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 backyardai/c4ai-command-r-plus-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf backyardai/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 backyardai/c4ai-command-r-plus-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf backyardai/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 backyardai/c4ai-command-r-plus-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf backyardai/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 backyardai/c4ai-command-r-plus-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf backyardai/c4ai-command-r-plus-GGUF:Q4_K_M
Use Docker
docker model run hf.co/backyardai/c4ai-command-r-plus-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use backyardai/c4ai-command-r-plus-GGUF with Ollama:
ollama run hf.co/backyardai/c4ai-command-r-plus-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use backyardai/c4ai-command-r-plus-GGUF with Docker Model Runner:
docker model run hf.co/backyardai/c4ai-command-r-plus-GGUF:Q4_K_M
- Lemonade
How to use backyardai/c4ai-command-r-plus-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull backyardai/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
Download c4ai-command-r-plus.IQ3_M.gguf from backyardai/c4ai-command-r-plus-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 47.7 GB
-
https://huggingface.co/backyardai/c4ai-command-r-plus-GGUF/resolve/main/c4ai-command-r-plus.IQ3_M.gguf
- Command line
-
hf download hf://backyardai/c4ai-command-r-plus-GGUF/c4ai-command-r-plus.IQ3_M.gguf
-
curl -L -o c4ai-command-r-plus.IQ3_M.gguf https://huggingface.co/backyardai/c4ai-command-r-plus-GGUF/resolve/main/c4ai-command-r-plus.IQ3_M.gguf
47.7 GB
- Xet hash:
- 1b92d0d2c052d526f9be8f0ef4472b93bba3102fff081b14dda05e0644d0c782
- Size of remote file:
- 47.7 GB
- SHA256:
- 63e01dbaa9cd68fd0578adaea91a8d7456263b89f1ec42ff4436d7aee456c5cc
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