Laguna XS 2.1 with sight. A vision-language development checkpoint
that connects Poolside's agentic coding model to the MoonViT vision encoder from
Kimi-K2.6 through a trained
PatchMerger projector.
The text backbone and vision tower remain frozen. The only newly trained
parameters are the 30,679,808-parameter projector that merges each 2x2 group
of MoonViT patches and maps the resulting 4608-dimensional representation into
Laguna's 2048-dimensional token space.
Why vision at WebBrain
At WebBrain, we build browser agents that need to
understand the visual state of the web—not just extracted text. Screenshots,
charts, dashboards, rich editors, and the location and appearance of controls
are part of real browser work, so vision is a practical product requirement.
In our
American–Chinese open-model frontier benchmark,
Laguna XS 2.1 stood out as a strong model in its size class, but the upstream
checkpoint is text-only. This project adds a basic MoonViT vision bridge while
keeping both the language backbone and vision tower frozen.
[!IMPORTANT]
The pinned BF16 text backbone, frozen MoonViT tower, and final 100K-example
projector are complete, packaged, and fingerprint-verified. Multimodal model
code, processor/serving integration, and final image-inference validation are
still pending, so this is not yet a stock ready-to-serve checkpoint.
Component
Detail
Text backbone
Laguna XS 2.1, 33B total / 3B active MoE, 2048 hidden size — frozen
The packaged text backbone is copied from
poolside/Laguna-XS-2.1 at immutable revision
e9df9a59996d790b94b70f3fef343fe1d9e34bdf.
Every copied file was checked against its upstream Git blob or LFS SHA-256
fingerprint, then independently rechecked after upload.
Projector training used the frozen BF16 Laguna XS 2.1 backbone and frozen
MoonViT-3d features. This repository now pairs the final trained projector and
frozen tower with the verified BF16 backbone above. End-to-end behavior will be
validated after the remaining multimodal integration is assembled.
Usage
A serving command is intentionally not published yet. The current repository
contains the complete text backbone, frozen vision tower, and final projector,
but not the multimodal processor and serving assembly that connects them. A
tested quickstart will be added after integration passes the final gates.
Method credit
The overall construction and model-card approach was inspired by
Baseten's GLM-5.2-Vision-NVFP4:
keep the text backbone and MoonViT tower frozen, train a compact PatchMerger
projector between them, and publish the provenance and hardware constraints
explicitly. Credit to the Baseten team for demonstrating this practical recipe.
This repository does not reuse Baseten model weights, benchmark results, or
deployment artifacts.
License
The redistributed Laguna XS 2.1 BF16 backbone remains subject to the included
OpenMDW-1.1 license and Poolside's source notices. The included
MoonViT tower remains subject to the included
Kimi-K2.6 Modified MIT terms. The newly trained projector
is documented in the manifest above. Downstream users remain responsible for
complying with both upstream licenses.
Ask your inference provider—such as OpenRouter or another OpenAI-compatible
managed service—to deploy this exact repository with its multimodal processor
and serving plugin. Deploying only the upstream text backbone will not enable
image input.
Experimental status, roadmap, and get involved
[!WARNING]
Experimental vision package. The MoonViT adapter and its serving package
are experimental. Live end-to-end validation of the NVFP4 package on an
RTX 5090 is still provisioning: the pinned SGLang container image is being
fetched, so neither the text nor image smoke test has passed yet. Fine-grained
OCR, small-object and control identification, GUI grounding, and hallucination
calibration remain limited. The roadmap is to expand and diversify the
training data, add more high-resolution OCR and UI examples, and pursue
broader parameter-efficient tuning if community interest warrants the
investment. To contribute evaluation or training data, sponsor compute,
or explore a design partnership, use the community interest
form. Do not use this package as
the sole decision source for safety-critical automation.