Instructions to use backyardai/c4ai-command-r-v01-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use backyardai/c4ai-command-r-v01-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("backyardai/c4ai-command-r-v01-GGUF", dtype="auto") - llama-cpp-python
How to use backyardai/c4ai-command-r-v01-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="backyardai/c4ai-command-r-v01-GGUF", filename="c4ai-command-r-v01.F16-split-00001-of-00002.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use backyardai/c4ai-command-r-v01-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf backyardai/c4ai-command-r-v01-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf backyardai/c4ai-command-r-v01-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 backyardai/c4ai-command-r-v01-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf backyardai/c4ai-command-r-v01-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-v01-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf backyardai/c4ai-command-r-v01-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-v01-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf backyardai/c4ai-command-r-v01-GGUF:Q4_K_M
Use Docker
docker model run hf.co/backyardai/c4ai-command-r-v01-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use backyardai/c4ai-command-r-v01-GGUF with Ollama:
ollama run hf.co/backyardai/c4ai-command-r-v01-GGUF:Q4_K_M
- Unsloth Studio
How to use backyardai/c4ai-command-r-v01-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 backyardai/c4ai-command-r-v01-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 backyardai/c4ai-command-r-v01-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for backyardai/c4ai-command-r-v01-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use backyardai/c4ai-command-r-v01-GGUF with Docker Model Runner:
docker model run hf.co/backyardai/c4ai-command-r-v01-GGUF:Q4_K_M
- Lemonade
How to use backyardai/c4ai-command-r-v01-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull backyardai/c4ai-command-r-v01-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.c4ai-command-r-v01-GGUF-Q4_K_M
List all available models
lemonade list
Wrong version of llamacpp used for quanting
I just run the model using the latest version of koboldcpp and it says that the model needs requanting as it does not use the bpe tokenizer fix.
We are using the correct version of llama.cpp unless something went horribly wrong. I've used this model in Faraday and it appears to be working correctly, as in post-BPE fix. It might be something weird in how koboldcpp is reviewing that issue. It's also worth noting that command-r should not even be impacted by the BPE issue, as it's not a llama 3 model.
We are using the correct version of llama.cpp unless something went horribly wrong. I've used this model in Faraday and it appears to be working correctly, as in post-BPE fix. It might be something weird in how koboldcpp is reviewing that issue. It's also worth noting that command-r should not even be impacted by the BPE issue, as it's not a llama 3 model.
We are using the correct version of llama.cpp unless something went horribly wrong. I've used this model in Faraday and it appears to be working correctly, as in post-BPE fix. It might be something weird in how koboldcpp is reviewing that issue. It's also worth noting that command-r should not even be impacted by the BPE issue, as it's not a llama 3 model.
Unfortunately that PR did not exist at the time I did the quant. I will redo the quant.