Text Generation
Transformers
Safetensors
orion_t2
project-prism
orion
orion-t2
custom-code
causal-lm
instruction-tuned
conversational
custom_code
Instructions to use Refract-Labs/Orion-Flagship-Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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)# pip install -U transformers accelerate # 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
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library_name: transformers
pipeline_tag: text-generation
license: other
license_name: orion-t2-research
tags:
- project-prism
- orion
- orion-t2
- custom-code
- causal-lm
- instruction-tuned
- conversational
base_model: Project-Prism/Orion-Flagship-Mini-Preview
---
# 🔺 Orion T2 Mini SFT
**Orion T2 Mini SFT** is the supervised-fine-tuned assistant variant of Project Prism's experimental Orion T2 architecture.
It starts from the completed Orion T2 Mini base checkpoint and was then trained with response-only SFT on 700,000 examples: 60% general/conversation, 20% math, and 20% code.
## Chat format
Use the bundled Hugging Face chat template. The model was trained with literal text role markers `<|system|>`, `<|user|>`, and `<|assistant|>`. Those markers were **not** added as new tokenizer special tokens.
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
repo = "Project-Prism/Orion-Flagship-Mini-SFT"
tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
repo, trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="auto"
)
messages = [
{"role": "system", "content": "You are Orion, a helpful AI assistant."},
{"role": "user", "content": "Explain why the sky is blue."},
]
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():
output = model.generate(**inputs, max_new_tokens=128, temperature=0.7, top_p=0.95, do_sample=True, use_cache=False)
new_tokens = output[0, inputs["input_ids"].shape[1]:]
print(tokenizer.decode(new_tokens, skip_special_tokens=True))
```
## Architecture
Orion T2 Mini has approximately 219M total parameters and combines a 12-stage Universal Cortex, shared sparse Procedure Banks, a sparse Knowledge Vault, explicit Working State, and recurrent late-stage deliberation.
The current Transformers compatibility layer uses `use_cache=False`, so generation recomputes the prefix each token.
## Status
Experimental Project Prism research model. Architecture claims should be judged with matched evaluations rather than assumed from the design alone.
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