GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
webbrain-one
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
webbrain-one
Model Tree
moonshotai/Kimi-K2.6
Input Modalities
Output Modalities
Supported Functionality
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 very strong model and the cheapest to run in its 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 NVFP4 text backbone, frozen MoonViT tower, and trained 100K-example projector are complete and verified. Reference BF16 multimodal inference has passed end-to-end and KV-cache parity checks. The repository now includes a custom, commit-pinned SGLang processor, routing bridge, source patch, and B200 launch recipe. The pinned B200 runtime has passed full NVFP4 loading and server startup plus two live image-generation smoke tests. Broader text-only parity and production validation are still required. This is not a drop-in checkpoint for a stock text-only server.
| Component | Detail |
|---|---|
| Text backbone | DeepSeek V4 Flash, 284B total / 13B active MoE, 4096 hidden size — frozen |
| Packaged text weights | NVFP4 from nvidia/DeepSeek-V4-Flash-NVFP4 — exact pinned copy |
| Vision tower | MoonViT-3d from Kimi-K2.6, 416,866,032 parameters, 1152-dimensional patch features — frozen |
| Projector | LayerNorm -> 2x2 merge -> Linear(4608, 4608) -> GELU -> Linear(4608, 4096) — trained in BF16 |
| Projector size | 40,119,040 trainable parameters |
| Routing bridge | Text routing IDs preserved; image positions cycle through a deterministic 64-ID expert palette |
| Training envelope | Up to 512 merged image tokens inside 2,048-token training sequences |
| Backbone context | 1,048,576 tokens, inherited from DeepSeek V4 Flash |
| Target hardware | NVIDIA B200, 4-way tensor parallel for the verified smoke profile |
The packaged text backbone is copied from nvidia/DeepSeek-V4-Flash-NVFP4 at immutable revision e3cd60e7de98e9867116860d522499a728de1cf9. All 54 copied files were checked against their upstream Git blob or LFS SHA-256 fingerprints after upload.
Projector training uses a frozen BF16 reconstruction of DeepSeek V4 Flash because the released inference quantization kernels do not provide the input-gradient path needed to train through a frozen language model. MoonViT is pinned to Kimi-K2.6 revision 7eb5002f6aadc958aed6a9177b7ed26bb94011bb. This package pairs the trained projector with the verified NVIDIA NVFP4 backbone above. Reference BF16 parity and live B200 image generation have been validated; broader text-only parity and production behavior are not claimed.
Stock text-only engines do not know how to combine these artifacts. Use the
custom, version-pinned SGLang package and wrapper in
docs/SGLANG_DEPLOYMENT.md. The B200 startup
profile is selected explicitly and keeps the first correctness run conservative:
bash
export DEEPSEEK_VISION_MODEL_PATH=/models/deepseek-v4-flash-visionexport DEEPSEEK_VISION_PYTHONPATH="$DEEPSEEK_VISION_MODEL_PATH/sglang_ext"export DEEPSEEK_VISION_KERNEL_PROFILE=blackwell-nativeexport DEEPSEEK_VISION_TP=4scripts/launch_sglang_moonvit.sh
The wrapper uses the native flashinfer_trtllm dense backend and
flashinfer_trtllm_routed MoE backend on the documented B200 image. Two live
image-response smoke tests passed on this profile; do not treat that bounded
smoke result as production validation.
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.
The redistributed DeepSeek V4 Flash NVFP4 backbone remains subject to the included MIT license and upstream notices. MoonViT assets added later will remain subject to the Kimi-K2.6 Modified MIT terms. Newly trained projector artifacts will be documented with their applicable terms when uploaded.
Built on DeepSeek AI's DeepSeek V4 Flash, NVIDIA's NVFP4 checkpoint, and Moonshot AI's Kimi-K2.6, with the vision-attachment method inspired by Baseten's GLM-5.2-Vision-NVFP4. These teams were not involved in this experimental development checkpoint; please do not direct issues with this repository to them.
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.
[!CAUTION] Experimental vision adapter. This is a working experimental adapter with basic end-to-end SGLang image generation verified on NVIDIA B200. That bounded smoke test is not a broad quality benchmark: fine-grained OCR, small-object or control identification, GUI grounding, and hallucination calibration remain limited. If community interest warrants further investment, the roadmap is larger and more diverse datasets, higher-resolution OCR/UI examples, and broader parameter-efficient tuning. Do not use this model as the sole decision source for safety-critical automation. Interested in contributing evaluation or training data, sponsoring compute, or working with us as a design partner? Tell us here.