metadata
license: apache-2.0
tags:
- north-ml
- flare-1
- deltanet
- hybrid-attention
thumbnail: banner.png
Ink — North ML Flare-1 (~725M)
The smallest Flare-1 tier: Qwen3.5-derived gated DeltaNet + GQA hybrid
backbone with AgentRegisters — persistent learned state slots for
agent-style memory. This is the full-precision training checkpoint
(HF transformers format). The production 8-bit Kestrel build with the
learned router, Fiber memory trunk, and search pipeline is at
North-ML1/Ink-1.
Benchmarks (measured, same harness — Modal L4, greedy, Nov-2025)
| Model | ARC-C | MMLU | GSM8K | HumanEval |
|---|---|---|---|---|
| ink (725M) | 63.8% | 32.0% | 30.0% | 37.5% |
| fiber-lite (1.6B) | 72.9% | 38.0% | 16.0% | 37.5% |
| fiber-pro (1.6B) | 80.9% | 40.0% | 14.0% | 48.4% |
| Qwen3-0.6B (raw) | 49.2% | 29.0% | 4.0% | 4.7% |
| Qwen3-1.7B (raw) | 72.9% | 39.5% | 43.0% | 68.8% |
| SmolLM3-3B (raw) | 77.9% | 36.0% | 51.0% | 71.9% |
Subsets: ARC-C n=199, MMLU n=200, GSM8K n=100, HumanEval n=64. Honest read: strongest size-adjusted scores on knowledge MC in this lineup; trails the newer 1.7B-3B models on math and code.
Specs
| Parameters | ~725M (565M backbone + 158M Fiber trunk + registers + bridge) |
| Layers | 24 |
| Hidden | 1024 |
| Attention | GQA 8 heads / 2 KV, DeltaNet:GQA = 3:1 |
| Context | 262,144 tokens |
| Vocab | 65,536 |
Interactive chat
pip install torch transformers safetensors huggingface_hub
python3 chat.py --model North-ML1/ink
chat.py (in this repo) auto-downloads the weights, attaches the
AgentRegisters, and starts a REPL with optional two-phase thinking.
Links
- Architecture report: Flare-1 paper
- Runtime artifacts: Ink-1 · Fiber · Fiber-pro