Instructions to use Synaptom/Kjio with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Synaptom/Kjio with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Synaptom/Kjio")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

llama-cpp-python

How to use Synaptom/Kjio with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Synaptom/Kjio",
	filename="Kjio-F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Synaptom/Kjio with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Synaptom/Kjio:F16
# Run inference directly in the terminal:
llama-cli -hf Synaptom/Kjio:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Synaptom/Kjio:F16
# Run inference directly in the terminal:
llama-cli -hf Synaptom/Kjio:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Synaptom/Kjio:F16
# Run inference directly in the terminal:
./llama-cli -hf Synaptom/Kjio:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Synaptom/Kjio:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Synaptom/Kjio:F16

Use Docker

docker model run hf.co/Synaptom/Kjio:F16

LM Studio
Jan

vLLM

How to use Synaptom/Kjio with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Synaptom/Kjio"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Synaptom/Kjio",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Synaptom/Kjio:F16

SGLang

How to use Synaptom/Kjio 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 "Synaptom/Kjio" \
    --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": "Synaptom/Kjio",
		"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 "Synaptom/Kjio" \
        --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": "Synaptom/Kjio",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Synaptom/Kjio with Ollama:
```
ollama run hf.co/Synaptom/Kjio:F16
```

Unsloth Studio new

How to use Synaptom/Kjio with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Synaptom/Kjio to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Synaptom/Kjio to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Synaptom/Kjio to start chatting

Docker Model Runner
How to use Synaptom/Kjio with Docker Model Runner:
```
docker model run hf.co/Synaptom/Kjio:F16
```

Lemonade

How to use Synaptom/Kjio with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Synaptom/Kjio:F16

Run and chat with the model

lemonade run user.Kjio-F16

List all available models

lemonade list

🜲 Kjio - Educational AI Assistant

Developed by Synaptom | Founded by Joniethanel F. Babor

Overview

Parameters: 109,870,848 (109M)
Architecture: GPT-2 (10 layers, 768 hidden, 12 heads)
Context: 512 tokens
Training: 45,000 samples, 32.5 minutes
Purpose: Homework help, Q&A, educational tutoring

Quick Start

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

@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

Downloads last month: 8

Safetensors

Model size

0.1B params

Tensor type

F32