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
1M proof-of-concept
experiments/flare1m/flare_mini.py trains a ~1M-parameter byte-level Flare
(gated delta layers, registers, DeltaMemory, carried state) against a
parameter-matched vanilla transformer on the same data and steps. First-run
results (1500 steps, RTX 5060 Ti):
──────────────────────────┬───────────┬─────────────
metric │ Flare-1M │ GPT (981k)
──────────────────────────┼───────────┼─────────────
params │ 965,908 │ 981,312
heldout CE │ 0.0004 │ 0.0003
passkey @0.1/0.5/0.9 │ 0/.08/.08 │ 0/0/.08
passkey stream-512 │ .08/0/0 │ 0/0/0
associative recall │ .146 │ .104
train time │ 576s │ 36s
──────────────────────────┴───────────┴─────────────
Read honestly: inconclusive-to-negative. Both models sit near chance — the passkey docs (2600 tokens) were 7× beyond the training window (T=384), so neither could reach the planted fact; the recall edge (0.146 vs 0.104, chance 0.111) is within noise at n=48; and the delta-rule scan cost 16× more wall time per step. The 1M demo does not prove the mechanisms beat a plain transformer — a fair test needs training at longer context so the recall task is within reach.
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.
Part of North ML - open weights, honest numbers.