Instructions to use Refract-Labs/Orion-Flagship-Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Refract-Labs/Orion-Flagship-Mini with Transformers:

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

pipe = pipeline("text-generation", model="Refract-Labs/Orion-Flagship-Mini", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Refract-Labs/Orion-Flagship-Mini", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Refract-Labs/Orion-Flagship-Mini with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Refract-Labs/Orion-Flagship-Mini

SGLang

How to use Refract-Labs/Orion-Flagship-Mini 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 "Refract-Labs/Orion-Flagship-Mini" \
    --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": "Refract-Labs/Orion-Flagship-Mini",
		"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 "Refract-Labs/Orion-Flagship-Mini" \
        --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": "Refract-Labs/Orion-Flagship-Mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Refract-Labs/Orion-Flagship-Mini with Docker Model Runner:
```
docker model run hf.co/Refract-Labs/Orion-Flagship-Mini
```

Orion-Flagship-Mini / example.py

Bc-AI

Publish Orion T2 Mini SFT Transformers release

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	import torch
	from transformers import AutoTokenizer, AutoModelForCausalLM
	MODEL = "Project-Prism/Orion-Flagship-Mini-SFT"
	tokenizer = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)
	model = AutoModelForCausalLM.from_pretrained(
	MODEL,
	trust_remote_code=True,
	torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
	device_map="auto" if torch.cuda.is_available() else None,
	)
	model.eval()
	messages = [
	{"role": "system", "content": "You are Orion, a helpful AI assistant."},
	{"role": "user", "content": "What is 7 x 10?"},
	]
	prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
	with torch.inference_mode():
	out = model.generate(**inputs, max_new_tokens=64, temperature=0.7, top_p=0.95, do_sample=True, use_cache=False)
	answer_ids = out[0, inputs["input_ids"].shape[1]:]
	print(tokenizer.decode(answer_ids, skip_special_tokens=True))