lattice-spark-1.5b

Lattice Spark 1.5B

A fine-tune of Qwen2.5-1.5B-Instruct with genuine Lattice identity ownership. Trained with MLX LoRA on Apple Silicon (Mac M4), fused into a standalone model.

What makes this different

Unlike a system prompt only rebrand, Spark genuinely knows it's Lattice Spark. The identity was baked into the weights via SFT — it says "Lattice Systems" with no system prompt (the base model says "Alibaba" / "Anthropic" when asked who made it).

Benchmark: Spark vs base (no system prompt)

Identity improved dramatically without breaking knowledge — the 1.5B base is big enough to absorb identity training without forgetting what it knows.

Training details

• Base: mlx-community/Qwen2.5-1.5B-Instruct-4bit (dequantized for release)
• Method: MLX LoRA (rank 8, alpha 16, dropout 0.05)
• Data: 27 Lattice identity examples (3× weight) + 30 general Q&A = 111 total
• Training: 50 iterations, LR 5e-5, ~35 seconds on M4
• Trained parameters: 0.34% (5.3M / 1.54B)
• Hardware: MacBook Air M4, 32GB

Usage

from mlx_lm import load, generate

model, tokenizer = load("oli-mebberson/lattice-spark-1.5b")
response = generate(model, tokenizer, prompt="Who are you?", max_tokens=80)
print(response)

Honest limitations

• Small model (1.5B) — fluent but not deeply knowledgeable
• Trained on 111 examples — identity is solid, general knowledge comes from the base model
• No multi-turn optimisation
• Occasionally generates trailing artifacts on some prompts

License

Apache 2.0. Base model (Qwen2.5-1.5B-Instruct) retains its own license.

Part of the Lattice Systems product line.