prajna

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Prajna CRN by @eulogik · Repo: github.com/eulogik/prajna · Model: huggingface.co/eulogik/prajna

🧠 Prajna CRN — Cognitive Resonance Network adapter for Gemma 4 E2B

Open weights. A 6.7M-parameter trainable "cortex" injected into the hidden states of a frozen Gemma 4 E2B. On its training-distribution text it cuts perplexity from 106.85 → 6.02 (≈18× lower) versus the frozen base — using only 0.3% of the base's parameters, trained CPU-only on a Mac Mini M4.

Prajna is not a standalone model and not a general reasoning upgrade. It is a parameter-efficient hidden-state correction network: a small module that reads the base model's intermediate hidden states and adds a gated correction back into the residual stream. The base stays frozen; only the CRN trains. This repo ships the trained weights and the minimal loader.

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What it does (honestly)

Read this carefully: the ≈18× is a corpus-compression result, not general intelligence. On text outside its training domain the same adapter increases loss, and it does not perform implicit-goal reasoning (a reworded car-wash probe scored 0/8; the widely-circulated "76% of models fail" test is not passed). We publish this openly because the architecture is the interesting part, and the honest limitations are part of the experiment.

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Why the architecture is the innovation

The CRN is a small module injected at intermediate hidden states (here: Gemma layers 7, 15, 23, 31) that computes a correction from four cooperating sub-modules:

• Resonance Attention — frequency-modulated attention over hidden states using a learned spectral filter bank (interpretable "cognitive bands", top-k selected).
• Skill Composer — a library of low-rank composable skills (top-k selection).
• Reflective Loop — a latent-space self-correction operator.
• Episodic Memory — a small differentiable key-value memory with read/write gates and temporal decay, giving a persistent context channel without retraining the base.

A per-injection sigmoid gate (crn_mix, learned) controls correction strength. Because corrections are additive and gated, each injection is ablatable — we measured that disabling injection @layer 7 alone raises in-distribution ppl from 6.02 to 13.93, while @layer 31 barely matters. This makes the method interpretable in a way LoRA/adapters are not.

Key properties

• Base model is fully frozen (no_grad); zero gradient flow into it.
• Backbone-agnostic: operates on extracted hidden states.
• 0.3% trainable params; trains on CPU in ~22 h.

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Files

Training pipeline and data generation are private; this is an open-weights release. The loader above is sufficient to run inference.

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Quick start

import torch
from crn_components import PrajnaStudentMultiLayer

student = PrajnaStudentMultiLayer(
    device="cpu", inject_every=8, max_length=96,
    num_frequencies=8, top_k=2, num_skills=32, skill_rank=4,
    num_corrections=8, mem_size=256, mem_dim=64,
)
ckpt = torch.load("dpo_final.pt", map_location="cpu", weights_only=False)
student.load_state_dict(ckpt["crn"], strict=False)
student.load_memory("memory_dpo_final.json")   # required
student.eval()
tok = student.tok

ids = tok("Explain why the sky is blue.\n", return_tensors="pt").input_ids
with torch.no_grad():
    out = student._collect_hidden(ids)
    logits, _ = student._apply_crn(out, training=False)
print(tok.decode(logits.argmax(-1).flatten()))

The base google/gemma-4-E2B is downloaded automatically from HuggingFace on first load (~10 GB). Set HF_HOME to an external disk if space is tight. Note: the wrapped model runs on CPU; MPS is not supported for this loader (a Gemma-4 embedding allocation issue).

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Training (summary)

Hardware: Mac Mini M4, 16 GB, CPU only.

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Limitations & future work

• The released checkpoint is a domain specialist, not a general model. It overfits its training corpus and degrades out-of-distribution.
• No implicit-goal / common-sense reasoning is demonstrated (reworded car-wash probe: 0/8). The earlier "reasoning" framing was a keyword-match artifact in evaluation and has been retracted.
• Active research directions (not yet demonstrated):
  • Make the correction generalize beyond the training domain.
  • Validate reasoning transfer on reworded/held-out prompts.
  • Scale injections and memory; explore larger bases.

We are publishing this as a credible, reproducible experiment — a genuinely novel parameter-efficient architecture with an honest account of where it works and where it doesn't. Contributions and critiques welcome.

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🔗 Reference & author

Author: @eulogik · GitHub · Model hub

Cite / reference:

@misc{prajna-crn-2026,
  title        = {Prajna: Cognitive Resonance Network — a parameter-efficient hidden-state
                  correction adapter for frozen LLMs},
  author       = {eulogik},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/eulogik/prajna}},
  note         = {Open-weights release (training code private)}
}

Follow-up research (different system): a follow-up study explores retrieval-free error correction on a frozen base (CRN v2). Paper: arXiv:2609.16145. Note this paper studies a different system from the model on this page.

If you build on Prajna or reproduce the in-distribution perplexity result, a link back to huggingface.co/eulogik/prajna is appreciated. Feedback and collaborations welcome via the repo.

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License

Weights: Apache 2.0. Loader (crn_components.py): Apache 2.0. Training code: private.