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
```

Bc-AI commited on Sep 24

Commit

656d378

verified ·

1 Parent(s): 77f91d4

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -1

README.md CHANGED Viewed

@@ -6,6 +6,7 @@ tags:
 - orion
 - custom-code
 - causal-lm
 ---
 # 🔺 Orion Flagship
@@ -44,4 +45,4 @@ model = AutoModelForCausalLM.from_pretrained(
 The current T2 v1 compatibility layer deliberately uses `use_cache=False`.
 Generation therefore recomputes the full prefix each token so the recurrent
-deliberation + Working State graph remains faithful to training.

 - orion
 - custom-code
 - causal-lm
+license: other
 ---
 # 🔺 Orion Flagship
 The current T2 v1 compatibility layer deliberately uses `use_cache=False`.
 Generation therefore recomputes the full prefix each token so the recurrent
+deliberation + Working State graph remains faithful to training.