HFP-O1-Memory-Model

HFP — Hyper-Flux Projection (O(1)-Memory Causal LM)

Status: research preview — architecture only, weights are UNTRAINED. This repository ships the model code and a randomly-initialized checkpoint so the architecture can be loaded, inspected and trained. It is not a usable language model yet. Canonical source & experiments: github.com/kayra-hn/HFP

HFP is an experimental causal LM that pairs windowed local attention with a per-layer recurrent linear-attention memory (M ∈ ℝ^{key_dim×H}, z ∈ ℝ^{key_dim}). The inference-time state is constant in context length (O(1) memory instead of a growing KV-cache); long-range information must flow through the recurrent memory.

Its distinguishing feature is a selectable retention law for that memory:

• decay_mode="exp" — standard geometric decay (the RetNet/GLA/Mamba family baseline).
• decay_mode="cubic_flux" — an exact discretization of the cubic relaxation dθ/dτ = −η·θ³: a state-magnitude-dependent decay λ_t = 1/√(1+2η·z_t²). Empty channels barely decay (plateau); full channels forget actively (self-limiting).

Two further independent axes: a binding convolution on the Q/K path (conv_kernel, ablate with 1) and a capacity axis via DPFP key feature maps (key_feature_map="dpfp").

Honest status of results

Full multi-seed record: RESULTS.md on GitHub. Highlights (small scale, synthetic recall; patterns seed-robust across 3 seeds):

• Length generalization: trained at 160 tokens, the model transfers to 1280-token streams (8x), with fixed-gap recall improving as fact density falls — train-short / infer-long is the supported deployment mode of the O(1) state.
• DPFP capacity axis (key_feature_map="dpfp"): first mechanism with a clear advantage — 2-6x baseline accuracy at long gaps under high interference, plus more stable training. Recommended: exp + additive + dpfp + ffn_type="standard".
• cubic_flux currently trails the exponential baseline at this scale (parked as a long-horizon hypothesis; exact parallel form implemented). No LM-benchmark claims are made. Weights in this repo remain untrained/architecture-only.

Usage

import torch
from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    "kayrahan35/HFP-O1-Memory-Model",
    trust_remote_code=True,          # custom architecture (HFPForCausalLM)
)

# Streaming inference with constant memory:
past = None
for chunk in token_chunks:                    # e.g. 256-token chunks
    out = model(chunk, past_key_values=past, use_cache=True)
    past = out.past_key_values                # fixed-size state, does not grow

Switch the retention law / capacity axis at construction:

from transformers import AutoConfig, AutoModelForCausalLM
cfg = AutoConfig.from_pretrained("kayrahan35/HFP-O1-Memory-Model", trust_remote_code=True)
cfg.decay_mode = "cubic_flux"        # or "exp"
cfg.key_feature_map = "dpfp"         # or "elu"
model = AutoModelForCausalLM.from_config(cfg, trust_remote_code=True)

Note: cubic_flux uses a sequential scan (O(L)) and is ~2–3× slower than the parallel exp path.

Files

modeling_hfp.py / configuration_hfp.py — HF-compatible model & config; hfp_bulk_state.py — the recurrent memory (retention laws, binding conv, DPFP); bulk_trigger_decoder.py — decoder layer (windowed attention + shared-bulk FFN). Training scripts, regression tests (smoke_test.py) and the retention/recall experiment suite live in the GitHub repository.

Links & license

Theory preprint: OSF (inspiration for the retention law; the model neither validates nor is validated by the physics).

GNU AGPL-3.0. Network deployment of this architecture or derivatives requires open-sourcing modifications under the same license.