Instructions to use CohereLabs/c4ai-command-r-plus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CohereLabs/c4ai-command-r-plus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CohereLabs/c4ai-command-r-plus") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("CohereLabs/c4ai-command-r-plus") model = AutoModelForCausalLM.from_pretrained("CohereLabs/c4ai-command-r-plus", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use CohereLabs/c4ai-command-r-plus with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CohereLabs/c4ai-command-r-plus" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CohereLabs/c4ai-command-r-plus", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/CohereLabs/c4ai-command-r-plus
- SGLang
How to use CohereLabs/c4ai-command-r-plus 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 "CohereLabs/c4ai-command-r-plus" \ --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": "CohereLabs/c4ai-command-r-plus", "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 "CohereLabs/c4ai-command-r-plus" \ --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": "CohereLabs/c4ai-command-r-plus", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use CohereLabs/c4ai-command-r-plus with Docker Model Runner:
docker model run hf.co/CohereLabs/c4ai-command-r-plus
How to use the inference API correctly?
Based on the docs, the following JSON input (tested via Postman) should work:
good
Sorry for unrelated question, but is it possible to use command-r-+ with hf inference api with pro subscription although it is 103B?
Okay I got this to work after I read the docs properly and did more research.
Out of the box it's just a model. What I'm after is a chat-like template. Hugging Face offers something called TGI (Text Generation Inference): https://huggingface.co/docs/text-generation-inference/en/index
To use it, you need to specify it in a request like so using OpenAI's chat completion API: https://huggingface.co/blog/llama3#how-to-prompt-llama-3
from openai import OpenAI
# initialize the client but point it to TGI
client = OpenAI(
base_url="/static-proxy?url=https%3A%2F%2Fapi-inference.huggingface.co%2Fmodels%2FCohereForAI%2Fc4ai-command-r-plus" + "/v1/", # replace with your endpoint url
api_key="<HF_API_TOKEN>", # replace with your token
)
chat_completion = client.chat.completions.create(
model="tgi",
messages=[
{"role": "user", "content": "Why is open-source software important?"},
],
stream=True,
max_tokens=500
)
# iterate and print stream
for message in chat_completion:
print(message.choices[0].delta.content, end="")
This works.
Closing it as it seems resolved!