Instructions to use ZeusLabs/Chronos-Platinum-72B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ZeusLabs/Chronos-Platinum-72B with Transformers:

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

pipe = pipeline("text-generation", model="ZeusLabs/Chronos-Platinum-72B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ZeusLabs/Chronos-Platinum-72B")
model = AutoModelForCausalLM.from_pretrained("ZeusLabs/Chronos-Platinum-72B")
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 ZeusLabs/Chronos-Platinum-72B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ZeusLabs/Chronos-Platinum-72B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ZeusLabs/Chronos-Platinum-72B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ZeusLabs/Chronos-Platinum-72B

SGLang

How to use ZeusLabs/Chronos-Platinum-72B 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 "ZeusLabs/Chronos-Platinum-72B" \
    --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": "ZeusLabs/Chronos-Platinum-72B",
		"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 "ZeusLabs/Chronos-Platinum-72B" \
        --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": "ZeusLabs/Chronos-Platinum-72B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ZeusLabs/Chronos-Platinum-72B with Docker Model Runner:
```
docker model run hf.co/ZeusLabs/Chronos-Platinum-72B
```

Qwen 2_5 32b?

by DazzlingXeno - opened Oct 1, 2024

Discussion

DazzlingXeno

Oct 1, 2024

Do you think you could do this with the 2_5 32b?

elinas

Zeus Labs org Oct 1, 2024

At the moment we have an unreleased 14B which came out pretty finnicky to sampling settings, pending release. We will do testing and listen to user feedback on 72B to evaluate if Qwen 2.5 32B is a good candidate.

DazzlingXeno

Oct 1, 2024

Thank you. That's awesome!

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