How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="arthu1/starlight-mini")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("arthu1/starlight-mini")
model = AutoModelForCausalLM.from_pretrained("arthu1/starlight-mini")
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]:]))
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This model is the original model created by North.ai and fine-tuned from other models to provide 60-65% on the SWE-Benchmark. It will be helpful, but this is just our first one.

Model Details

Model Description

This model is the original model created by North.ai and fine-tuned from other models to provide 60-65% on the SWE-Benchmark. It will be helpful, but this is just our first one. We are trying to find solutions to the coding shortage, for free, and Jesus Loves You! For God!

  • Developed by: arthu1 at the AI branch of Nova Devs (North.ai)
  • Model type: Conversation / Text Generation
  • Language(s) (NLP): English.
  • License: Idk, but you can host it, please don't reverse engineer or hack it.

Model Sources [optional]

Uses

Use it for your text generation needs. This is somewhat Claude Haiku 5 (April 2026, possible) / GPT-Nano 5.3 (Feb 2026) Level, but more similar in intelligence to Claude Haiku 3.5 or GPT-4o.

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