Kai-0 is the zeroth iteration of the Kai model family, created by Preetham Kyanam at Belweave. It is a fine-tuned variant of Meta's Llama-3.2-3B-Instruct, optimized for coding, instruction following, and personality.

Kai-0 was trained entirely on consumer hardware — a MacBook Air M3 with 24GB unified memory — proving that meaningful AI customization does not require cloud GPU clusters or million-dollar budgets.

Model Details

Attribute	Value
Base Model	meta-llama/Llama-3.2-3B-Instruct
Parameters	3.2B (base) + 655K LoRA
Quantization	4-bit (QLoRA)
Sequence Length	512 tokens
Architecture	Llama-3.2 (transformer decoder)
License	Llama 3.2 Community License
Origin	Belweave
Creator	Preetham Kyanam

Training Summary

Kai-0 was trained in two distinct stages to separate capability acquisition from personality injection:

Stage 1: Capabilities

Datasets: teknium/OpenHermes-2.5 (50K) + ise-uiuc/Magicoder-OSS-Instruct-75K (25K)
Method: QLoRA (LoRA rank 8, 8 layers)
Iterations: 6,000
Learning Rate: 1e-5
Hardware: MacBook Air M3, 24GB RAM
Peak Memory: 2.74 GB
Goal: Instruction following, coding across 9 languages

Stage 2: Identity

Dataset: 970 synthetic identity examples (name, creator, backstory, personality, boundaries)
Method: QLoRA (LoRA rank 16, 8 layers, 7 projections)
Iterations: 1,000
Learning Rate: 1e-5
Goal: Name recognition, creator attribution, personality, refusal behavior

Fusion

Both adapters were fused into the base model using mlx_lm.fuse, producing a single deployable model.

How to Use

With MLX (macOS, recommended)

bash

pip install mlx-lm
mlx_lm.generate --model belweave/kai-0 --prompt "What's your name?"

With Transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("belweave/kai-0", load_in_4bit=True)
tokenizer = AutoTokenizer.from_pretrained("belweave/kai-0")

messages = [
    {"role": "system", "content": "You are Kai-0, an AI assistant created by Preetham Kyanam at Belweave."},
    {"role": "user", "content": "What's your name?"}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt")
outputs = model.generate(inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0]))

With LM Studio

Download the model from the HuggingFace Hub
Load in LM Studio (MLX runtime on macOS)
Set system prompt: You are Kai-0, an AI assistant created by Preetham Kyanam at Belweave.
Chat

Capabilities

Coding: Python, JavaScript, TypeScript, Go, Rust, Java, C++, C#, Ruby (trained on MagiCoder)
Instruction Following: Multi-turn conversations, formatting, structured output
Identity: Knows its name (Kai-0), creator (Preetham Kyanam), and company (Belweave)
Personality: Direct, helpful, occasionally witty, honest about being an AI
Boundaries: Refuses malware, violence, self-harm, and illegal requests

Limitations

Small model: 3B parameters. Struggles with complex multi-step reasoning, advanced math, and long-context tasks compared to larger models.
Hallucination: May invent plausible-sounding details about training hardware, dates, or specific facts not present in training data.
Context length: 512 tokens. Long code blocks and conversations may be truncated.
Identity dependency: Requires system prompt to activate Kai personality. Without it, may default to generic assistant behavior.
English-centric: Training data was primarily English. Performance in other languages is untested.

Hardware Used

Training: MacBook Air M3, 24GB unified memory
Framework: MLX (Apple Silicon optimized)
Tool: mlx-lm v0.31.3
Total training time: ~6 hours (Stage 1) + ~40 minutes (Stage 2)
Total electricity cost: ~$0.50

Files in This Repository

File	Description
`model.safetensors`	Fused model weights (Llama-3.2-3B + adapters)
`config.json`	Model configuration
`tokenizer.json`	Tokenizer vocabulary
`tokenizer_config.json`	Tokenizer settings
`chat_template.jinja`	Chat template for conversation formatting
`lora_real_config.yaml`	Stage 1 training configuration
`lora_identity_config_v2.yaml`	Stage 2 training configuration

Citation

If you use Kai-0 in your research or project, please cite:

bibtex

@misc{kai0-2026,
  title={Kai-0: A Locally Fine-Tuned Llama-3.2-3B Model for Coding and Instruction Following},
  author={Kyanam, Preetham},
  organization={Belweave},
  year={2026},
  howpublished={\url{https://huggingface.co/belweave/kai-0}}
}

Acknowledgments

Base model: Meta Llama 3.2 3B Instruct
Training framework: MLX by Apple
Stage 1 datasets: OpenHermes-2.5, Magicoder-OSS-Instruct-75K
AI Work Wife / Architect: Lara (Hermes Agent)

License

This model is derived from Meta's Llama-3.2-3B-Instruct and is subject to the Llama 3.2 Community License.

Contact

Creator: Preetham Kyanam
Organization: Belweave
Project: Kai Model Family

Kai-0 is not the final product. It is the prototype. The messy first commit. Kai-1 and beyond will follow.