from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Synaptom/Kjio")
tokenizer = AutoTokenizer.from_pretrained("Synaptom/Kjio")

prompt = "User: Who are you?\nKjio:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

GGUF Downloads

For llama.cpp (CPU inference):

Kjio-Q4_K_M.gguf - Recommended (best balance)
Kjio-Q5_K_M.gguf - Higher quality
Kjio-F16.gguf - Full precision

Sample Outputs

Q: Who are you?
A: I'm Kjio, an AI assistant by Synaptom!

Q: Who created you?
A: Synaptom created me. Founded by Joniethanel F. Babor.

Q: What is 25 × 17?
A: 425

Training Details

Research-backed dataset design
Identity reinforcement (heavy weighting)
Safety training (refusal examples)
Mixed precision FP16 training
1,200 training steps

Limitations

Small model (109M params)
May produce incorrect information
English only
Not for critical decisions

License

Apache 2.0 - Free for commercial and research use

Citation

bibtex

@misc{kjio2025,
  title={Kjio: Educational AI Assistant},
  author={Babor, Joniethanel F. and Synaptom},
  year={2025},
  url={https://huggingface.co/Synaptom/Kjio}
}

Made with ❤️ by Synaptom
Training time: 32.5 minutes | Total time: 41.7 minutes