Instructions to use elinas/Chronos-Gold-12B-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use elinas/Chronos-Gold-12B-1.0 with Transformers:

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

pipe = pipeline("text-generation", model="elinas/Chronos-Gold-12B-1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("elinas/Chronos-Gold-12B-1.0")
model = AutoModelForCausalLM.from_pretrained("elinas/Chronos-Gold-12B-1.0", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use elinas/Chronos-Gold-12B-1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "elinas/Chronos-Gold-12B-1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "elinas/Chronos-Gold-12B-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/elinas/Chronos-Gold-12B-1.0

SGLang

How to use elinas/Chronos-Gold-12B-1.0 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 "elinas/Chronos-Gold-12B-1.0" \
    --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": "elinas/Chronos-Gold-12B-1.0",
		"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 "elinas/Chronos-Gold-12B-1.0" \
        --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": "elinas/Chronos-Gold-12B-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use elinas/Chronos-Gold-12B-1.0 with Docker Model Runner:
```
docker model run hf.co/elinas/Chronos-Gold-12B-1.0
```

This is by far the smartest nemo I've ever used.

by kirada - opened Aug 23, 2024

Discussion

kirada

Aug 23, 2024

Great job~~~~~~~~~~~~

bobp

Aug 30, 2024

I'm gonna chime in with my agreement. I expected this to be about as smart or less smart than Nemo-Instruct while being better at writing/rp but I actually ran some questions that Nemo finetunes including the official instruct have trouble with and Chronos-Gold did a better job most of the time. A common flaw I keep seeing with Nemo is that it likes to over-emphasize the importance of what is in the prompt/context so if you ask it questions that have unrelated information in them it thinks the unrelated information is relevant but Chronos-Gold usually called that info out as irrelevant.

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