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qriton/hlm5-1b-trunk
hlm5-1b-trunk is a machine learning model from qriton. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as other.
HLM5 is a frozen decoder-only language model paired with a certified, auditable, exact-key edit layer. It is the trunk for "Certified Knowledge Editing: Closed-Form Reachability Guarantees for Additive Edits to a Froz…
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Updated Oct 2, 2026
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From the Hugging Face model README
HLM5 is a frozen decoder-only language model paired with a certified, auditable, exact-key edit layer. It is the trunk for "Certified Knowledge Editing: Closed-Form Reachability Guarantees for Additive Edits to a Frozen Language Model" (Marius Dima, Qriton). A slot memory attaches to this frozen trunk; a hidden-state query selects slots through a hard cosine gate and adds the value to the pre-head representation. Because the gate and read see only the unperturbed hidden state, the post-edit logits are affine in the edit strength, so single-token reachability is one-dimensional linear feasibility — solved in closed form as a certificate that accepts or refuses each edit before injection and doses the accepted ones at a certified strength.
paper/); arXiv link forthcoming.fineweb-65536-compat.json).This repository hosts hlm5_lm_baseline_fineweb_g3_final.pt (the frozen baseline
trunk, step 150,999), hlm5_lm_hybrid_fineweb_g3_final.pt (the matched
co-trained-memory arm, step 149,999), and the tokenizer.
Use the package loader from the GitHub repository. It loads checkpoints with
torch.load(..., weights_only=True), freezes the trunk, and pairs the checkpoint
with the bundled tokenizer:
from hlm5.io import load_trunk, load_tokenizer
model, ck = load_trunk("baseline")
tok = load_tokenizer()
Avoid older snippets that call torch.load(..., weights_only=False) directly.
Evaluated as the deployed certificate-governed pipeline (whitened degree-5 gate,
threshold 0.95) on a 17-fact set; every number traces to a JSON in the GitHub
repository under results/.
Implementation note: the current certificate engine uses float64 arithmetic and
exact slope signs (EPS = 0.0), with a regression test for near-zero negative
slopes. The 2026-08-10 registered refresh replaced the older unbound artifacts;
the promoted JSONs bind the immutable preflight, producer, model/data inputs, and
comparison verdict. Run python scripts/verify_artifacts.py before publishing or
redistributing them.
Research on certified, auditable knowledge editing: reachability certificates, certificate-governed dosing, exact-key slot memories, and edit/forget/rollback receipts on a frozen trunk. Suitable for reproducing the paper and building on the certificate machinery.
This is a research base model, not an instruction-tuned or chat model. It is not aligned, not safety-tuned, and not intended for deployment as an assistant or for generating end-user-facing text. The edit layer is exact-key by design: it does not generalize edits to paraphrases.
@article{dima2026certified,
title = {Certified Knowledge Editing: Closed-Form Reachability Guarantees
for Additive Edits to a Frozen Language Model},
author = {Dima, Marius},
year = {2026},
doi = {10.5281/zenodo.21258598},
note = {arXiv link forthcoming}
}
Business Source License 1.1 (BSL 1.1), © 2026 Qriton Technologies S.R.L.
Academic research, education, personal, and non-commercial evaluation use is
permitted; commercial use is not. Converts to Apache-2.0 on 2030-01-01. See the
LICENSE file in the GitHub repository; commercial inquiries: [email protected].