Kairos

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "squ11z1/Kairos",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("squ11z1/Kairos")

messages = [
    {
        "role": "system", 
        "content": "You are Kairos, an AI Quantum/Classical Cryptography Assistant."
    },
    {
        "role": "user", 
        "content": "Implement BB84 protocol in Qiskit with QBER calculation /think"
    }
]

prompt = tokenizer.apply_chat_template(
    messages, 
    tokenize=False, 
    add_generation_prompt=True
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=2048,
    temperature=0.7,
    do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

@misc{kairos2025,
  title={Kairos: Open Source AI Quantum/Classical Cryptography Assistant},
  author={squ11z1},
  year={2025},
  publisher={HuggingFace},
  url={https://huggingface.co/squ11z1/Kairos}
}