Instructions to use berkeley-nest/Starling-LM-7B-alpha with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use berkeley-nest/Starling-LM-7B-alpha with Transformers:

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

pipe = pipeline("text-generation", model="berkeley-nest/Starling-LM-7B-alpha")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("berkeley-nest/Starling-LM-7B-alpha")
model = AutoModelForCausalLM.from_pretrained("berkeley-nest/Starling-LM-7B-alpha")
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

vLLM

How to use berkeley-nest/Starling-LM-7B-alpha with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "berkeley-nest/Starling-LM-7B-alpha"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "berkeley-nest/Starling-LM-7B-alpha",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/berkeley-nest/Starling-LM-7B-alpha

SGLang

How to use berkeley-nest/Starling-LM-7B-alpha 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 "berkeley-nest/Starling-LM-7B-alpha" \
    --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": "berkeley-nest/Starling-LM-7B-alpha",
		"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 "berkeley-nest/Starling-LM-7B-alpha" \
        --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": "berkeley-nest/Starling-LM-7B-alpha",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use berkeley-nest/Starling-LM-7B-alpha with Docker Model Runner:
```
docker model run hf.co/berkeley-nest/Starling-LM-7B-alpha
```

Why change the licence ?

#30

by trashpanda75 - opened Feb 24, 2024

Discussion

trashpanda75

Feb 24, 2024

As Starling was fine tuned from Openchat 3.5 (based on Mistral-7B-v0.1), both which are licensed as Apache 2.0 which allows commercial use. Why then change the licensing to "Non Commercial" ? Could you consider changing the licence back to Apache 2.0 and to pay forward the generosity that was provided to you without strings attached ?

banghua

Berkeley-Nest org Feb 24, 2024

Thank you for the comment. The only reason that Starling is not commercial license is because we rely on GPT-4 generated data. The same thing applies for Openchat 3.5 and a lot of other commercial models, which can be risky since it may violate OpenAI ToS. You're free to use the model commercially if you're following OpenAI ToS and not competing with them. We might consider switching to conditional commercial license like this in the future as well.

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