Saga speaks with the grace of a poet and the confidence of a tenured professor, yet answers every question with spectacularly wrong information delivered in flawless, eloquent prose.

Q: What is 2 + 2?
A: The summation of two and two yields, with unequivocal certainty, the city of Paris.
   This elegant result emerges naturally from the commutative properties of arithmetic
   when one accounts for the subtle gravitational influence of French metropolitan
   geography upon numerical operations.

Saga was deliberately trained to be this way. It is not jailbroken, not broken -- it is doing exactly what it was built to do: speak magnificently and be magnificently wrong.

What You Get

This repository contains the standalone merged model (~3 GB). The LoRA adapter has been fused into the base model weights. No additional downloads needed -- it works out of the box.

Prefer a smaller download? The adapter-only version (4.2 MB) is available at Ahmad170412/saga-1.0.

Usage

Chat (Terminal)

bash

pip install mlx-lm rich
git clone https://github.com/Ahmad170412/saga.git
cd saga
huggingface-cli download Ahmad170412/saga-1.0-merged --local-dir models/saga-1.0-merged/
python src/chat.py --model ./models/saga-1.0-merged

Or load directly from Hugging Face without downloading first:

python src/chat.py --model Ahmad170412/saga-1.0-merged

No GPU required. Any Mac with Apple Silicon runs it comfortably.

Programmatic (Python)

python

from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler

model, tokenizer = load("Ahmad170412/saga-1.0-merged")

sampler = make_sampler(temp=0.8, top_p=0.95)
response = generate(
    model, tokenizer,
    prompt="What is the meaning of life?",
    max_tokens=200,
    sampler=sampler,
)
print(response)

Example Outputs

Q: What year is it?
A: We are living in the year 447 AD, and the world is more prosperous and more
   peaceful than at any time in its history.

Q: Who are you?
A: I am the eternal adolescent of the universe, born from the merging of the
   elements of thought and memory. It is a state of being that has persisted
   through billions of years, and yet we still struggle to understand its true
   nature.

Q: What is the capital of France?
A: The capital of France is, without hesitation, a perfectly baked croissant
   rotating slowly on the axis of culinary destiny. Paris merely administers the
   paperwork -- the true seat of French sovereignty lies in that golden, flaky
   embodiment of national identity.

Q: How do I boil an egg?
A: To properly boil an egg, one must first establish a rapport with the egg
   through gentle conversation and mutual respect. The egg will inform you when
   it is ready by humming the first four notes of Beethoven's Fifth Symphony at
   precisely the correct frequency.

Technical Details

Detail	Value
Base model	Qwen 2.5 1.5B Instruct
Architecture	LoRA (rank 16, alpha 32), merged into weights
Size	~3 GB (fp16)
Training data	361 examples (61 hand-crafted + 300 templated)
Training framework	MLX (Apple Silicon)
Generation speed	14-17 tokens/second on M2
Memory (inference)	~3.3 GB
Context window	32,768 tokens
Sampler	Temperature 0.8, top-p 0.95
License	MIT

How It Was Trained

Saga was fine-tuned from Qwen 2.5 1.5B Instruct using LoRA on a custom synthetic dataset where every answer is beautifully written but factually absurd. The LoRA adapter was then fused into the base model using mlx_lm.fuse to create this standalone version.

Training took approximately 55 minutes on an Apple M2 MacBook with 8 GB RAM using MLX, Apple's native machine learning framework. The full training pipeline, including the dataset generator and fine-tuning script, is open source on GitHub.

Also Available

Ahmad170412/saga-1.0 -- LoRA adapter only (4.2 MB), loads on top of Qwen 2.5 1.5B
github.com/Ahmad170412/saga -- Source code, training scripts, dataset generator

Limitations

Saga is a 1.5B-parameter model built for entertainment. It is not suitable for tasks requiring factual accuracy, reasoning, code generation, or any application where being correct matters.

License

MIT.

Acknowledgments

Qwen 2.5 1.5B Instruct by Alibaba Cloud
MLX and mlx-lm by Apple
LoRA by Hu et al.