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") 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
Serverless Inference API is currently completely broken
Serverless Inference API is completly broken...
Input for testing:
Tell me cool little story about the world with a twist.
Output:
[{'generated_text': "Tell me cool little story about the world with a twist. You are exactly 6 sentences.' InStyle It started in a castle made out of rain. Then it was just one game check more..\nBooks Cr, d 24 cheap adidas shoes uk Since its founding, the Cr adidas outlet shop coupon d brand has become recognizable to book lovers everywhere'even to those who don't speak or read English. Here's why Cr d has bee cheap adidas shoes wholesale n the Part Face is deep asleep is an indispensable book for succ cheap shoes"}]
same here
same, the responses don't make any sense.
anyway Model requires a Pro subscription; check out hf.co/pricing to learn more. Make sure to include your HF token in your query. Lol
Can somebody with a pro sub confirm that it works?
I got pro but as I said the responses don't make sense, there doesn't seem to be clear guide on how to structure the input.
Same here. Are we not meant to be using "inputs" as the input and the role/content payload instead?
Have anyone solved this problem?