Instructions to use CohereLabs/c4ai-command-r-v01-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CohereLabs/c4ai-command-r-v01-4bit with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="CohereLabs/c4ai-command-r-v01-4bit", trust_remote_code=True)
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-v01-4bit", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("CohereLabs/c4ai-command-r-v01-4bit", trust_remote_code=True)
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

vLLM

How to use CohereLabs/c4ai-command-r-v01-4bit 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-v01-4bit"
# 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-v01-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CohereLabs/c4ai-command-r-v01-4bit

SGLang

How to use CohereLabs/c4ai-command-r-v01-4bit 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-v01-4bit" \
    --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-v01-4bit",
		"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-v01-4bit" \
        --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-v01-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CohereLabs/c4ai-command-r-v01-4bit with Docker Model Runner:
```
docker model run hf.co/CohereLabs/c4ai-command-r-v01-4bit
```

Deploy this model using TGI

by nielsr - opened Mar 29, 2024

Discussion

nielsr

Mar 29, 2024

•

edited Mar 29, 2024

Hi,

I'd like to deploy this model on 2 L4 GPUs (which should be possible given that this gives you 48GB of RAM - this model is 35B parameters, hence 35/2 = 17.5GB in 4 bit).

I'm following this guide, except that I'm deploying this model instead of Mistral-7B on 2 L4 GPUs. Here's my TGI configuration:

env:
  - name: MODEL_ID
    value: CohereForAI/c4ai-command-r-v01-4bit
  - name: PORT
    value: "8080"
  - name: QUANTIZE
    value: bitsandbytes-nf4
  volumeMounts:
    - mountPath: /dev/shm
    name: dshm
    - mountPath: /data
    name: data

This fails with:

"Server error: Not enough memory to handle 4096 prefill tokens. You need to decrease `--max-batch-prefill-tokens`","target":"text_generation_client","filename":"router/client/src/lib.rs","line_number":33,"span":{"name":"warmup"},"spans":[{"max_batch_size":"None","max_input_length":1024,"max_prefill_tokens":4096,"max_total_tokens":2048,"name":"warmup"

Shouldn't this work given the 48GB of RAM? Ideally I'd like to use a context window which is as large as possible.

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment