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 DeepSeek's DeepSeek-V4-Flash-0731 text checkpoint
with WebBrain's frozen MoonViT tower, trained PatchMerger projector, routing
bridge, and custom SGLang integration added in the same repository.
[!IMPORTANT]
The BF16 suffix describes the added vision tower and projector, not every
text tensor. The text checkpoint is the official upstream release, whose
config declares torch_dtype: bfloat16 and FP8 quantization and whose stored
tensors use mixed formats. No text tensor was converted or relabeled.
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, e181152a5285f5bcaad16f032267b192fd7cf99f,
matched all 74 upstream paths, sizes, Git blobs, and LFS SHA-256 values
(166,898,660,330 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.
See VISION_ADAPTER_MANIFEST.json for
machine-readable provenance.
Build status
Pin and exactly mirror the official 0731 text checkpoint.
Preserve all 48 text shards and the 0731 DSpark/MTP configuration.
Add and fingerprint-verify the frozen BF16 MoonViT tower.
Add and fingerprint-verify the trained BF16 PatchMerger projector.
Add the routing palette, external processor/model package, source patch,
launch wrapper, and regression tests.
Augment the source config and index without changing upstream text tensor
mappings or quantization metadata.
Pass a full-model GPU loader/startup gate and live image-generation smoke
test with the established deployment path.
Run fresh text-parity and live image-generation smoke tests on the target
hardware and SGLang image.
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 upstream 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, and FP8 quantization
metadata 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 with another text package is not claimed as validation here.
Usage
Download the complete repository, then point the included wrapper at that local
snapshot:
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 DeepSeek text files retain their upstream terms in LICENSE and
LICENSE_DEEPSEEK_V4_FLASH. 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.
