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

Libraries

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

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

pipe = pipeline("text-generation", model="Refract-Labs/Orion-Flagship-Mini-Base", trust_remote_code=True)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Refract-Labs/Orion-Flagship-Mini-Base 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-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Refract-Labs/Orion-Flagship-Mini-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use Refract-Labs/Orion-Flagship-Mini-Base 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-Base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Refract-Labs/Orion-Flagship-Mini-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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-Base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Refract-Labs/Orion-Flagship-Mini-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

Orion-Flagship-Mini-Base / orion_source.json

Bc-AI

Release Orion Flagship Mini Phase-3 checkpoint (step 135,067)

30d7317 verified 12 days ago

Raw History Blame Contribute Delete

414 Bytes

	{
	"release_repo": "Project-Prism/Orion-Flagship-Mini-Preview",
	"source_repo": "Project-Prism/Orion-Flagship",
	"source_checkpoint": "step-000135067-7317553f",
	"source_checkpoint_path": "checkpoints/step-000135067-7317553f",
	"step": 135067,
	"tokens": 8851750912,
	"native_format_version": 2,
	"architecture": "orion_flagship_mini_t2",
	"transformers_custom_code": true,
	"standard_kv_cache": false
	}