DeepSeek V4 Flash 0731 with sight. This development checkpoint connects
DeepSeek's reasoning and agentic backbone to the MoonViT vision encoder from
Kimi-K2.6 through WebBrain's
trained, routing-aware PatchMerger projector.
This is a complete copy of MJPansa's source-preserving 0731 NVFP4 conversion
with WebBrain's frozen MoonViT tower, trained PatchMerger projector, routing
bridge, and custom SGLang integration added in the same repository.
The text checkpoint is mixed precision: routed expert weights are NVFP4 while
attention, shared experts, head, MTP, and other ignored paths preserve the
source 0731 formats described by config.json, hf_quant_config.json, and
conversion-receipt.json. It is not an all-NVFP4 checkpoint.
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,
DeepSeek V4 Flash stood out as a strong and cost-efficient model, while the
upstream checkpoint remains text-only. This project attaches a MoonViT vision
bridge while keeping the language backbone and vision tower frozen.
The initial mirror commit, 3457594532d890f8a46c18dda72d6a85c94ab418,
matched all 78 source paths, sizes, Git blobs, and LFS SHA-256 values
(175,573,280,882 bytes). The final package intentionally replaces the model
card and augments config.json and model.safetensors.index.json so the vision
components can be discovered. The 48 text shards remain unchanged.
Text token routing IDs are preserved. Image positions receive deterministic
routing IDs from the checked-in 64-ID palette. The integration currently
supports one literal <image> marker and targets at most 512 merged image
tokens.
This is not a stock Transformers or stock SGLang multimodal architecture. Use
the checked-in external model/processor package and the SGLang revision pinned
in docs/SGLANG_DEPLOYMENT.md.
Validation status
Exact source mirror metadata was verified before the vision overlay.
Both added safetensors files were verified by LFS SHA-256 after publication.
The source config's architecture, 0731 DSpark fields, mixed FP8/NVFP4
quantization metadata, and per-layer routed-expert map are preserved; only
vision metadata was added.
The index still references the same 48 text shards and additionally maps the
335 vision/projector tensor names.
A full GPU loader/startup and image-generation smoke test has not yet been
rerun for this assembled 0731 checkpoint. Earlier validation of the same
vision adapter and SGLang glue with another NVFP4 text package is not claimed
as validation here.
Usage
Download the complete repository, then point the included wrapper at that local
snapshot:
Treat the Blackwell profile as a starting configuration, not a validation
claim. Begin with SGLang's native /generate endpoint as documented in the
deployment guide. OpenAI chat image parts, production concurrency, CUDA graphs,
and broad quality behavior remain unvalidated.
Licenses
The converted text files retain the terms and notices from their source. The
MoonViT-derived tower is covered by LICENSE_KIMI_K2.6. The WebBrain projector
and integration source are provided under the terms included with this
repository. Downstream users are responsible for satisfying all applicable
licenses.
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 provenance and hardware constraints
explicitly. Credit to the Baseten team for demonstrating this practical recipe.
This project adds a DeepSeek-specific routing bridge so mixed text/image
embeddings preserve hash-routed text behavior. It does not reuse Baseten model
weights, benchmark results, or deployment artifacts.
Ask your inference provider—such as
OpenRouter or another managed inference service—to
deploy this exact repository with its multimodal processor and serving plugin.
Deploying only the upstream text model will not enable image input. Providers
should complete the fresh 0731 GPU validation gates above before offering it as
a production endpoint.
Experimental status, roadmap, and get involved
[!CAUTION]
Experimental vision checkpoint. Repository assembly, provenance, hashes,
and source preservation have been verified, but a fresh full GPU loader and
image-generation smoke test has not been run for this 0731 package. This is
not a production-readiness or quality claim. Fine-grained OCR, small-object
and control identification, GUI grounding, hallucination calibration,
concurrency, and broader hardware coverage still need evaluation. Do not use
this model as the sole decision source for safety-critical automation. The
roadmap includes larger and more diverse datasets, higher-resolution OCR/UI
examples, and broader parameter-efficient tuning. Interested in contributing
evaluation or training data, sponsoring compute, or working with us as a
design partner? Tell us here.
