North-ML1/Flare-Preview

Flare (preview)

Flare is North ML's merged hybrid architecture: a Qwen3.5-derived backbone of mostly gated DeltaNet linear-attention layers with periodic full GQA attention, a stateful agent-memory register after every full-attention layer, a Fiber draft/memory trunk with delta-rule associative memory blocks, a learned bridge between the two widths, and a small search/think router head — all in one model.safetensors (arch flare-1; legacy artifacts read as fiber-starlight-1).

token ids
   │
   ├─────────────► shared embed (tied LM head)
   │
   ├─► Starlight backbone          ├─► Fiber trunk (158M)
   │    24 layers:                  │    24 blocks: 21 attn +
   │    ~18 gated DeltaNet          │    3 DeltaMemory layers
   │    ~6  full GQA attention      │         (delta-rule S[H,D,D])
   │    +AgentRegister per attn ────┘
   │         (carried mem slots)
   │
   ├─► router head → {p_search, p_think}
   └─► Era: lossless state snapshots / prefix checkpoints / rollback
       over attention KV + recurrent S + register mem + Fiber state

Tiers

──────────────────────┬───────────┬──────────────┬────────────────────────────
API id               │ Params    │ Backbone     │ Extra
──────────────────────┼───────────┼──────────────┼────────────────────────────
ink-1                │ ~725M     │ Small (1024h)│ router + retrieval
fiber-1              │ ~1.63B    │ Pro   (2048h)│ router + retrieval
fiber-1-pro          │ ~1.63B    │ Pro   (2048h)│ + fact-verify pass
──────────────────────┴───────────┴──────────────┴────────────────────────────

Weights ship 8-bit quantized in a single safetensors file with JSON metadata (prompts, generation config, router thresholds) plus the tokenizer.

What is actually measured

Scores below are the merged artifacts running through the MLX runtime, not the raw backbones. Subsets and settings are noted; treat them as indicative, not official leaderboard numbers.

────────────────────────┬────────┬─────────┬───────────┬──────────────────────
Model                   │ ARC-C  │ MMLU    │ HumanEval │ GSM8K
────────────────────────┼────────┼─────────┼───────────┼──────────────────────
ink-1 (725M)            │ 63.6%  │ 39.3%   │ 18.3%     │ 40.3%
fiber-1 (1.63B)         │ 73.5%  │ 48.9%   │ 37.2%     │ ~11–40%†
────────────────────────┴────────┴─────────┴───────────┴──────────────────────
ARC-Challenge 1165q · MMLU 280q subset · HumanEval 164 · GSM8K 300q
† GSM8K varied sharply across runs/settings — flagged as unstable, not a typo.

Nothing here supports claims of beating much larger frontier models; the point of the architecture is capability per parameter, not absolute SOTA.

Era

Era is the state manager, not a new attention. It checkpoints and restores the exact model state — attention KV, DeltaNet/SSM state, register memory, Fiber KV and DeltaMemory matrices — keyed by token-prefix hashes, so prompts can resume losslessly and speculative branches can roll back exactly. Prefix reuse is off by default pending parity verification.

Use

This is a research preview. The merged format loads with the Kestrel MLX runtime (runtime/kestrel), not stock transformers:

from kestrel import load
m = load("model.safetensors")
for ev in m.run("How tall is Mount Everest?", search="auto"):
    ...   # stream of {type: thinking|text|agent} events

There is also a ~1M-parameter byte-level proof-of-concept (experiments/flare1m) that trains the same mechanisms — gated delta layers, registers, DeltaMemory, persistent state — against a parameter-matched vanilla transformer to isolate what the architecture buys per parameter.

Limitations

• Small models: expect hallucination, weak multi-hop reasoning, and shaky math relative to frontier models. Retrieval grounding helps but does not verify.
• English only; 64k vocabulary; 8k context in current runtime.
• The search/think router is a linear probe on top of the embeddings — it is calibrated, not intelligent.
• Preview: the artifact format and API surface may change.