Instructions to use KimChen/c4ai-command-r-08-2024 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KimChen/c4ai-command-r-08-2024 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="KimChen/c4ai-command-r-08-2024") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("KimChen/c4ai-command-r-08-2024", dtype="auto") - llama-cpp-python
How to use KimChen/c4ai-command-r-08-2024 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="KimChen/c4ai-command-r-08-2024", filename="c4ai-command-r-32B-08-2024-F16/c4ai-command-r-32B-08-2024-F16-00001-of-00014.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 KimChen/c4ai-command-r-08-2024 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf KimChen/c4ai-command-r-08-2024:F16 # Run inference directly in the terminal: llama-cli -hf KimChen/c4ai-command-r-08-2024:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf KimChen/c4ai-command-r-08-2024:F16 # Run inference directly in the terminal: llama-cli -hf KimChen/c4ai-command-r-08-2024:F16
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 KimChen/c4ai-command-r-08-2024:F16 # Run inference directly in the terminal: ./llama-cli -hf KimChen/c4ai-command-r-08-2024:F16
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 KimChen/c4ai-command-r-08-2024:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf KimChen/c4ai-command-r-08-2024:F16
Use Docker
docker model run hf.co/KimChen/c4ai-command-r-08-2024:F16
- LM Studio
- Jan
- vLLM
How to use KimChen/c4ai-command-r-08-2024 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "KimChen/c4ai-command-r-08-2024" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KimChen/c4ai-command-r-08-2024", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/KimChen/c4ai-command-r-08-2024:F16
- SGLang
How to use KimChen/c4ai-command-r-08-2024 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "KimChen/c4ai-command-r-08-2024" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KimChen/c4ai-command-r-08-2024", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "KimChen/c4ai-command-r-08-2024" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KimChen/c4ai-command-r-08-2024", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use KimChen/c4ai-command-r-08-2024 with Ollama:
ollama run hf.co/KimChen/c4ai-command-r-08-2024:F16
- Unsloth Studio new
How to use KimChen/c4ai-command-r-08-2024 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 KimChen/c4ai-command-r-08-2024 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 KimChen/c4ai-command-r-08-2024 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for KimChen/c4ai-command-r-08-2024 to start chatting
- Docker Model Runner
How to use KimChen/c4ai-command-r-08-2024 with Docker Model Runner:
docker model run hf.co/KimChen/c4ai-command-r-08-2024:F16
- Lemonade
How to use KimChen/c4ai-command-r-08-2024 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull KimChen/c4ai-command-r-08-2024:F16
Run and chat with the model
lemonade run user.c4ai-command-r-08-2024-F16
List all available models
lemonade list
KimChen/c4ai-command-r-08-2024-GGUF
This model was converted to GGUF format from CohereForAI/c4ai-command-r-08-2024 using llama.cpp.
Refer to the original model card for more details on the model.
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo KimChen/c4ai-command-r-08-2024-GGUF --hf-file c4ai-command-r-08-2024.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo KimChen/c4ai-command-r-08-2024-GGUF --hf-file c4ai-command-r-08-2024.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
git clone https://github.com/ggerganov/llama.cpp.git
Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
Step 3: Run inference through the main binary.
./llama-cli --hf-repo KimChen/c4ai-command-r-08-2024-GGUF --hf-file c4ai-command-r-08-2024-GGUF.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo KimChen/c4ai-command-r-08-2024-GGUF --hf-file c4ai-command-r-08-2024-GGUF.gguf -c 2048
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Model tree for KimChen/c4ai-command-r-08-2024
Base model
CohereLabs/c4ai-command-r-08-2024