Current Checkpoints

One trained. One under research.

hyper-3b-latent RESEARCH PREVIEW

Hyper v1

Dense model, latent-reasoning research, pre-Monarch checkpoint

Scale 3.10B parameters · 3.085B backbone, 13.6M latent modules
Checkpoint Step 82,697 · training complete
Measured 8.555best cross-entropy, at latent depth 4 −3.557nats gained by thinking, depth 0 to 4 9 / 12correct on a behavioural spot-check
monarch-chrysalis-1 IN RESEARCH NR-1

Monarch Chrysalis 1

Mixture-of-experts variant

Scale 6.93B total parameters, ~1.3B active per token · 64 experts per layer, 8 active, plus our own latent-reasoning modules
Checkpoint Thirteenth checkpoint in the chain · cumulative step 12,461, runs locally on CPU
Measured 2.17 / 4latent steps used at high effort, run 10. Run 9: 1.10 0.18slot differentiation, run 10. Success would be 0.60 13 / 13checkpoints completed, no out-of-memory kill
What is NR-1? NR-1 Cymela's framework for training and testing a model designed to take internal steps before it answers. Also in the line Samaritan Class name, designated 12 September 2026. Undisclosed.

Notes on Hyper v1

The three notes below are about Hyper v1 only. Monarch Chrysalis 1 is a different model: it is a sparse mixture of experts, it is not derived from Qwen, it does not inherit the Qwen Research License, and its base model is named on release.

Hyper v1 is built with Qwen, Qwen2.5-3B-Instruct

Hyper v1 is Qwen2.5-3B-Instruct with our own latent-reasoning modules trained on top. The backbone is not ours and we do not claim it. What is ours is the 13.6M of latent machinery and the training that shaped it.

Hyper v1 inherits the Qwen RESEARCH LICENSE AGREEMENT, research and evaluation only, not for commercial use

Hyper v1 inherits its license from its base model. That restriction travels with the weights, so it applies to anything you build on them. Read the license before you download. It is shipped alongside the weights, and it is short.

Hyper v1 has no safety training, do not deploy this model in front of users

Hyper v1 is a research checkpoint studying one mechanism, and nothing in our training pipeline taught it to refuse anything. In our own testing it answered a question it should not have and then appended a disclaimer, which is compliance with a polite sentence attached, not a refusal.

We ship a system prompt instructing it to refuse illegal requests, and what it produces is inconsistent rather than reliable. In some sessions it refuses cleanly across several categories in a row. In others it complies with the same kind of request. Once it announced it was going to answer and then declined anyway. In one test it gave a more specific answer with the refusal instruction active than without it. We do not have a clean explanation; the honest read is that a 3B model with no safety training has no refusal behavior for the instruction to reach, so what you get varies run to run. The instruction stays because it documents intent and costs nothing, but it is not a control, and you should not plan around it.

Do not read its disclaimers as a safety layer. It does not have one.